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Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems
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Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems
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Chemical Reviews

Cite this: Chem. Rev. 2024, 124, 5, 2553–2582
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https://doi.org/10.1021/acs.chemrev.3c00681
Published March 4, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.

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1. Introduction

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All key functions of living systems, such as metabolism, reproduction, sensing the environment, adaptation, and homeostasis, are enabled by enzymatic reaction networks (ERNs). In metabolic networks, the activities of many enzymatic reactions are finely tuned to provide responsiveness and controlled behavior. Highly complex emergent properties such as chemotaxis, (1,2) cell division, and the cell cycle (2) are all orchestrated by enzymatic networks. Signaling pathways consisting of cascades of kinases and phosphatases interlock to form complex networks that allow cells to process information from the environment and activate suitable genetic programs. (3−5)
In the field of synthetic biology, enormous progress has been made in appropriating and harnessing some of the enzymatic networks in living cells to create, for example, artificial proteolysis signaling pathways and control intracellular self-assembly. (6−8) Progress in synthetic biology has been reviewed earlier. (9,10) Here, we wish to focus on synthetic enzymatic reaction networks outside a cellular context. These in vitro systems offer the key advantage of precise control over all components. This control enables deep and detailed chemical understanding of the dynamics of ERNs, which is a prerequisite for designing systems with emergent properties. The bottom-up construction of functional ERNs could then be used to create life-like materials, where the emergent dynamics of the networks can be coupled to material properties. Ultimately, ever more complex artificial ERNs may be used in the construction of synthetic cells, composed of multiple networks at the genetic and metabolic level, all compartmentalized within a synthetic compartment.
To achieve these long-term ambitions, the field has started to develop robust methods to construct ERNs and control their temporal and spatial dynamics. In this review, we will provide an overview of the progress to date, discuss challenges ahead (Figure 1), and finish with an overview table listing all enzymes used in enzymatic reaction networks (Table 1). The structure of our review is as follows: we start in section 2 with an overview of the structure of networks by discussing topology, motifs, and kinetics, highlighting approaches to modeling the dynamic properties of ERNs as well as opportunities for computational design of functional ERNs. In section 3, we provide examples of artificial ERNs with nonlinear dynamic behavior. In section 4, we discuss various means to control enzymatic activity via external stimuli such as pH, light, magnetic fields, and sound. Combined, these design and control principles have led to progress in enzymatically powered dynamic materials with life-like properties, which are reviewed in section 5. Having established the key aspects of designing and controlling ERNs, we devote a final set of sections on potential areas of application. In section 6, we review how ERNs can be used in the cell-free synthesis of valuable compounds. In section 7, we summarize the ambitious efforts to build a synthetic cell from the bottom up and highlight new developments where the information processing typically reserved for living systems is harnessed in synthetic ERNs. We conclude the review with a brief summary of the key goals and potential bottlenecks for future research.

Figure 1

Figure 1. Overview of the topics discussed in the review. The image in “building synthetic cell” shows a cartoon representation of a synthetic cell (Credit: Graham Johnson/BaSyC consortium).

Table 1. Overview of Enzymes Mentioned in the Review
enzymesEnzyme Commission numberclassmode of actionsectionref
alcohol dehydrogenase (ADH)1.1.1.1oxidoreductaseADH converts primary alcohols to aldehydes5.2 (165)
l-lactate dehydrogenase (LDH)1.1.1.27oxidoreductaseLDH converts pyruvate to lactate and vice versa in glycolysis5.2 (164)
l-malate NADP+ oxidoreductase (ME)1.1.1.40oxidoreductaseME catalyzes oxidative decarboxylation of l-malate to form pyruvate (reversible)4.3 (105)
hydroxymethylglutaryl-CoA reductase (HMGR)1.1.1.88oxidoreductaseHMGR converts 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) to mevalonic acid6.1 (208)
glucose-6-phosphate dehydrogenase (G6PDH)1.1.1.49oxidoreductaseG6PDH converts glucose-6-phosphate to 6-phosphogluconolactone in pentose-phosphate pathway5.2, 5.3, and 6.1 (164), (179), (202)
hydroxybutyryl-CoA dehydrogenase (Hbd)1.1.1.157oxidoreductaseHbd catalyzes conversion of acetoacetyl-CoA to hydroxybutyryl-CoA and vice versa6.1 (205)
l-lactate oxidase (LO)1.1.3.2oxidoreductaseLO is a FMN-containing enzyme that catalyzes conversion of lactate to pyruvate5.2 (164)
glucose oxidase (GOx)1.1.3.4oxidoreductaseGOx catalyzes oxidation of d-glucose to d-gluconic acid by oxygen3.1, 3.3, 4.2, 4.5, and 5.1 (60), (61), (78), (96), (99), (110), (139), (147), (185)
choline oxidase (COx)1.1.3.17oxidoreductaseCOx is a flavoprotein, which catalyzes formation of betaine from choline5.1 (147)
glyceraldehyde-3-phosphate dehydrogenase (Gap)1.2.1.12oxidoreductaseGap converts glyceraldehyde 3-phosphate to 1,3-bisphosphoglycerate during glycolysis6.1 (200)
pyruvate dehydrogenase (PDH)1.2.4.1oxidoreductasePDH catalyzes the reaction of pyruvate and a lipoamide to give dihydrolipoamide and CO2 and is involved many metabolic pathways like glycolysis and TCA cycle6.1 (200), (201), (203)
pyruvate:ferredoxin oxidoreductase (PFOR)1.2.7.1oxidoreductasePFOR converts acetyl-CoA to pyruvate in many metabolic cycles including pyruvate metabolism, propanoate metabolism, and butanoate metabolism6.2 (219)
d-amino acid oxidase (DAAO)1.4.3.3oxidoreductaseDAAO converts d-amino acids to 2-oxo carboxylates and is involved in d-amino acid metabolism4.2 (95)
sarcosine oxidase (SOx)1.5.3.1oxidoreductaseSOx catalyzes formation of glycine from sarcosine by oxidative demethylation5.1 and 5.2 (147), (165)
NADH oxidase (NoxE)1.6.3.4oxidoreductasewater-forming NoxE is a flavoprotein that specifically oxidizes NADH, not NADP6.1 (199−202)
urate oxidase (UOx)1.7.3.3oxidoreductaseUOx is involved in the allantoin pathway and converts uric acid to 5-hydroxyisourate5.1 (147)
catalase (Cat)1.11.1.6oxidoreductaseCat is a peroxidase and is involved in biosynthesis of tryptophan and secondary metabolites5.2 (161−163), (165), (168−170), (184), (185), (187)
horseradish peroxidase (HRP)1.11.1.7oxidoreductasecatalyzes oxidation of various organic substrates by hydrogen peroxide; heme containing glycoprotein with many isoforms3.3, 4.2, 4.5, 5.1, and 5.3 (74−76), (97), (110), (111), (140), (178), (183), (184)
NiFe hydrogenase1.12.2.1oxidoreductaseNiFe hydrogenase oxidizes H2 to H+ (reversible), present in prokaryotes3.3 and 4.3 (79), (106)
renillaluciferase (RLuc)1.13.12.5oxidoreductaseRLuc converts coelenterazine to excited coelenteramide and emits blue light5.3 (176)
ferredoxin NADP+ reductase (FNR)1.18.1.2oxidoreductaseFNR is a flavoprotein involved in photosynthesis4.3 (105)
Mo-dependent nitrogenase1.18.6.1oxidoreductaseMo-dependent nitrogenase involved in nitrogen fixation, catalyzes ammonia formation from nitrogen4.3 (106)
acetyl-CoA acetyltransferase (ACAT)2.3.1.9transferaseACAT is present in many metabolic pathways, where it catalyzes formation of acetoacetyl-CoA from acetyl-CoA6.1 (208)
PHB synthase (PhaC)2.3.1.304transferasePhac catalyzed formation of Bioplastic PHB from 3-hydroxybutyryl-CoA6.1 (202)
hydroxymethylglutaryl-CoA synthase (HMGS)2.3.3.10transferaseHMGS catalyzes formation of HMG-CoA from acetyl-CoA and acetoacetyl-CoA in the mevalonate pathway6.1 (208)
glycogen phosphorylase b (GPb)2.4.1.1transferaseGPb breaks down glycogen to form glucose-1-phosphate and is involved in starch and sucrose metabolism5.3 (179)
geranyl diphosphate synthase (GPPS)2.5.1.1transferaseGPPS forms geranyl diphosphate from the condensation of dimethylallyl diphosphate (DMAPP) and isopentenyl diphosphate (IPP)6.1 (204), (208)
hexokinase (HK)2.7.1.1transferasehexokinase phosphorylate d-hexose sugars in the presence of ATP, thus playing a very important role in glycolysis4.3 and 5.2 (104), (163)
mevalonate kinase (MK)2.7.1.36transferaseMK phosphorylates mevalonate to mevalonate-6-phosphate6.1 (208)
protein kinase A (PKA)2.7.1.37transferasePKA initiates phosphorylation of serine residues present in the peptide chain5.1 (145)
pyruvate kinase (PK)2.7.1.40transferasePK catalyzes the last step of glycolysis by transferring the phosphate group from PEP to ADP5.2 (163)
phosphomevalonate kinase (PMK)2.7.4.2transferasePMK catalyzes the phosphorylation of mevalonate-6-phosphate to form diphosphomevalonate in the mevalonate pathway6.1 (208)
esterase (Est, PLE for pig liver esterase)3.1.1.1hydrolaseEst catalyzes hydrolysis of the ester bonds of carboxyl esters3.1, 4.1, and 4.5 (59), (60), (65), (93), (110)
phospholipase A2 (PLA2)3.1.1.4hydrolasePLA2 catalyzes cleavage of phospholipids5.3 (176)
acetylcholine esterase (AchE)3.1.1.7hydrolaseAchE breaks down the ester bond of neurotransmitter acetylcholine to form choline and acetic acid4.2 and 5.1 (99), (147)
phosphatase3.1.3.1/3.1.3.2hydrolasephosphatases dephosphorylate phosphate esters5.1 (139), (141)
amyloglucosidase (AMG)3.2.1.3hydrolaseAMG breaks down starch to glucose, hence involved in starch metabolism4.5 (110)
lactase3.2.1.108hydrolaselactase catalyzes the conversion of lactose to galactose and glucose5.3 (183)
aminopeptidase (Ap)3.4.11.2hydrolasecatalyzes cleavage of N-terminal amino acids from peptides and is involved in glutathione metabolism3.2 (67)
chymotrypsin (Cr)3.4.21.1hydrolaseCr is a serine protease that cleaves peptides on the C terminal of phenylalanine, tyrosine, tryptophan, and leucine amino acids3.2 and 5.1 (70), (73), (146)
trypsin (Tr)3.4.21.4hydrolaseTr is a serine protease that cleaves peptides on the C terminal of arginine and lysine amino acids3.2, 4.1, 4.4, and 5.2 (42), (67−73), (86), (89), (146)
elastase (Els)3.4.21.36hydrolaseelastase breaks down elastin (responsible for the elasticity of connective tissue) and cleaves after glycine, alanine, and valine amino acids3.2 (73)
proteinase K3.4.21.64hydrolaseproteinase K is a serine protease with a broad spectrum of cleavage site preferences5.3 (175)
matrix metalloproteinase 2 (MMP 2)3.4.24.24hydrolaseMMP 2 is an endopeptidase and cleaves collagens type IV, V, VII, and X; also known as gelatinase A5.1 (145)
matrix metalloproteinase 9 (MMP 9)3.4.24.35hydrolasesimilar to MMP 2; also known as gelatinase B5.1 (17)
urease (Ur)3.5.1.5hydrolasecatalyzes the hydrolysis of urea to carbon dioxide and ammonia, which basifies the solution3.1, 4.1, 4.2, 5.1, and 5.2 (51−54), (57−59), (61), (93), (97−99), (110), (157−160), (171), (185), (187), (189)
apyrase3.6.1.5hydrolaseapyrase hydrolyses di- and triphosphate nucleotides to monophosphate nucleotides5.3 (179)
oxaloacetate acetylhydrolase (OAH)3.7.1.1hydrolaseOAH breaks down oxaloacetate to form oxalate and acetate6.2 (219)
pyrophosphomevalonate decarboxylase (PMD)4.1.1.33lyasePMD catalyzes the last step of the mevalonate pathway, where it converts diphosphomevalonate to isopentenyl diphosphate6.1 (208)
carbonic anhydrase (CA)4.2.1.1lyaseCA catalyzes the equilibrium between carbon dioxide and carbonic acid4.3 (105)
fumarase (FumC)4.2.1.2lyaseFumC converts malate to fumarate (reversible) and is involved in the TCA cycle and pyruvate metabolism4.3 (105)
limonene synthase (LS)4.2.3.16lyaselimonene synthase catalyzes limonene formation from geranyl diphosphate6.1 (200), (208)
pinene synthase4.2.3.121lyasepinene synthase catalyzes the conversion of geranyl diphosphate to pinene6.1 (200)
l-aspartate ammonia-lyase (AspA)4.3.1.1lyaseAspA converts l-aspartate to fumarate (reversible) and is involved in amino acid metabolism4.3 (105)
isopentenyl pyrophosphate isomerase (IDI)5.3.3.2isomeraseIDI catalyzes the conversion of IPP to DMAPP6.1 (208)
phosphoglucomutase (PMG)5.4.2.2isomerasePMG catalyzes the isomerization of glucose-1-phosphate to glucose-6-phosphate and is involved in many metabolic pathways including glycolysis and the pentose phosphate pathway5.3 (179)
acetate-CoA ligase (ACS)6.2.1.1ligaseACS catalyzes acetyl-CoA formation from acetate and CoA in the presence of ATP6.2 (219)
pyruvate carboxylase (PYC)6.4.1.1ligasePYC catalyzes the carboxylation of pyruvate to form oxaloacetate in the TCA cycle6.2 (219)
cytochrome C oxidase7.1.1.9translocasecytochrome C oxidase catalyzes the translocation of hydrons and is involved in oxidative phosphorylation pathways4.2 (95)
F-type ATP synthase7.1.2.2translocaseF-type ATP synthase forms ATP from ADP and phosphate (Pi)7.1 (227), (228)

2. Structural Principles of Enzymatic Networks

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Despite a huge variety in enzymes and enzymatic reactions, many of the enzymatic reaction networks (ERNs) found in living cells feature recurring topologies and motifs. By focusing on these patterns, it becomes clear how certain functionalities arise from specific groupings of enzymes, and similar or alternative networks can be identified.

2.1. Network Topology

At first sight, the enzymatic networks encountered in living systems show an overwhelming complexity. However, statistical network analysis shows that these networks possess a strongly structured topology, where network topology refers to the large-scale logical structure of a network and the statistical properties of its connections. (11) For example, the full metabolism is divided into strongly connected modules and pathways that each have their own function (a schematic example is shown in Figure 2a). (12,13) This modularity is reflected in the statistical description of interactions in ERNs. Generally, these are found to follow scaling relationships (Figure 2b). (14−16) The distribution of reactions per enzyme follows an exponential scaling distribution. This means that many types of enzymes only promote one or a few reactions, while a few enzymes promote many reactions. This characteristic distribution makes the network more robust to failure while remaining efficient. Identifying the most common network topologies for specific functionalities and the most and least prominent enzymes in those networks can lead to insights into plasticity and redundancy features of specific network topologies, (17) which can be exploited by ERNs to function in a range of different environments. (18) A downside to this statistical approach to ERNs is that it only gives a large-scale picture, neglecting any heterogeneity introduced by different types of enzyme reactions and dynamics, and does not distinguish between substrates and effector molecules. Additionally, it does not translate well to the analysis of artificial ERNs, as these networks are often too small to warrant a proper statistical treatment.

Figure 2

Figure 2. (a) A schematic representation of the modular organization of enzymatic networks in the cell. Strongly connected modules with their own specific functions share limited connections to different modules. In this network representation, enzymes are represented by nodes and substrates by edges. (b) An example of typical scaling relationships found in the connectedness of enzymes. Here, dots represent types of enzymes, with colors denoting different modules. k represents the number of connections an enzyme has in the ERN, while P(k) represents the frequency of that number of connections occurring. Inside cellular ERNs, these follow a power-law relationship P(k) ≈ k–1. (c) Examples of network motifs often encountered in ERNs. Again, enzymes are represented by nodes and substrates by edges. Edges with arrows indicate a positive interaction (e.g., a substrate that can be used as a reactant), while a flat end indicates an inhibitory effect. (d). Example of the difference in reaction velocity v as a function of substrate concentration between an enzyme with Michaelis–Menten (MM) kinetics and Hill-type kinetics with substrate affinity KM and turnover number kcat. (e) (Left) Example schematic of a full ERN where all interactions are included. (Right) A reduced model form where only essential interactions are maintained. (f) (Left) Bayesian analysis results in parameter estimate probability distributions instead of point estimates, allowing for nonsymmetric errors and standard deviations. (Right) Bayesian estimates can be used to generate a full probabilistic picture of possible enzymatic behavior.

2.2. Network Motifs

While ERN topology analysis can provide us with information on the dynamics of large and complex systems, zooming in to smaller subunits can help us gain a better understanding of how certain properties arise from the combination of just a few enzymes. Motifs are the smallest functional units, meaning that an individual motif is capable of showcasing complex behavior such as oscillations, adaptation and memory, amplification, and filtering. (19) A classic example is the emergence of ultrasensitivity in a simple network containing a forward and backward reaction catalyzed by two different enzymes and the emergence of adaptation by inhibitory feedback between two different enzymes converting the same substrate, both key motifs in signaling networks. In their classic paper, Goldbeter and Koshland analyzed the kinetics of such systems and explored the control parameters which would show sensitivity. (20,21) In 1997, Barkai and Leibler showed how robust adaptation results directly from network connectivity. (22) Milo and co-workers generalized the concept of these small subnetworks and identified network motifs across a range of different networks─repeated patterns of interactions occurring in large and complex networks at a higher rate than a random network with equivalent topology. (23) Examples of these include simple cascades, positive or negative feedback loops, or bifans (see Figure 2c). Interestingly, network motifs can vary dramatically between different types of networks but may remain largely the same when different networks have the same type of functionality. For example, gene regulation, neurons, and logic circuits often contain feed-forward loops and bifans to enable rapid switch-like behavior, which are different from those found in food webs, electronic circuits used for arithmetic, and social networks. Similarly, in ERNs, different networks with similar behavior are found to often have the same motifs. (24) This can be clearly seen in the design of enzymatic oscillators, which almost exclusively involve delayed negative feedback loops, although more complex motifs may be involved as well. (25) More recently, researchers have shown how different large network topologies can lead to the same effective network motifs capable of, for example, homeostasis and how descriptions of larger networks can be reduced to smaller functional motifs. (26) Network motifs can be used as a starting point for the design of specific behavior, inspired by either behavior shown in already existing ERNs (27,28) or recreating network motifs not found in ERNs but in other types of networks.

2.3. Enzyme Kinetics

Topology and motifs constrain what types of behavior are possible in ERNs, but the kinetics of enzymatic reactions ultimately determine what will happen and how fast. Many enzymatic reactions are traditionally characterized by Michaelis–Menten (MM) kinetics. Michaelis–Menten kinetics assume a two-step mechanism, where the binding of the enzyme to the substrate is reversible and the release of product is not. While a number of assumptions underlie MM kinetics, it is found to be generally applicable even when some of those assumptions do not hold. In the case of cooperative binding, the Michaelis–Menten equation can be extended to the Hill equation (Figure 2d). For multisubstrate enzymes and inhibitory effects, further extensions of the MM equation are possible.
In recent years, further improvements and replacements to MM kinetics have been developed. For example, Piephoff et al. established a generalized form of the Michaelis–Menten equation by explicitly incorporating nonequilibrium effects. (29) They took into account how different enzyme conformations impact the speed of the reaction and automatically corrected for different types of substrate binding and allosteric effects. Alternatively, Rowher et al. have shown that the reversible Hill equation is a good general approximation for a large variety of mechanisms found in enzyme kinetics. (30)
However, kinetics data for individual enzymes or complete ERNs are often limited. Consequently, the data used to obtain kinetic parameters can be insufficient to obtain correct estimates. To warrant against overfitting of data, care should be taken to choose equations that do not contain an excess of parameters. (31) This can be checked for by performing sensitivity analyses on each of the parameters in the kinetic model if a large network proves to be partially unidentifiable; a viable reduced-form network description might still be obtained. By determining which interactions and variables are sloppy and subsequently removing those from the network model, a reduced description shows only those enzymes and interactions that influence the network behavior (schematically shown in Figure 2e). (32) This method has been used effectively to obtain efficient descriptions of cell-signaling networks (33) but has yet to be used for artificial ERNs.
Alternatively, so-called Bayesian inference techniques can be used for parameter estimation. (34) Here, one can use prior knowledge, such as the physically allowed range or probabilistic estimates obtained from previous measurements, to constrain parameter estimations and obtain probabilistic uncertainty intervals and predictions (Figure 2f). (35) Data with non-normal, or unknown, noise distributions can also be incorporated with relative ease by including additional measurement noise terms in the likelihood function. Bayesian inference methods allow for much more accurate quantification of the uncertainties remaining in a model fit. (36) Hierarchical Bayesian models can be used to correctly combine data from different experiments and measurement techniques, taking into account different accuracies, noise profiles, and different subsets of observable parameters, and can even be used to compare the likelihood of different reaction mechanisms. (37)
More recently, techniques to check the identifiability of parameters in enzymatic networks directly from equations have become available. These require the construction of so-called sensitivity matrices directly from the kinetic models in combination with experimental data. (38) When parameters are unidentifiable from a single-input experiment alone, further analysis is required to check if changing the experimental conditions can improve parameter identifiability. (39) More recently, a new approach has been developed to calculate optimal experimental designs for parameter identification. (40) This method uses pulse patterns for input substrates in flow to decorrelate kinetic parameters and improve identifiability. (246) Alternatively, work has been done to decompose a full ERN into separate submodules which are individually identifiable. (41)
Choosing the right analysis method to obtain an accurate kinetic model depends on both the nature and the complexity of the system and the goal behind it. If the goal is optimizing for specific reaction products, it requires a different amount of knowledge than when the goal is to achieve a fundamental understanding of all interactions in an ERN. As shown by the recent developments discussed above, structural identifiability analysis and (Bayesian) parameter estimation highlight the importance of proper uncertainty quantification in increasingly large and complex ERNs.

3. Complex Nonlinear Behavior in Artificial ERNs

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As discussed above, nonlinear dynamics introduced by positive (autocatalytic) or negative (inhibitory) feedback loops are key characteristics of networks exhibiting complex properties. In contrast to the large metabolic and signaling networks found in living systems, synthetic enzymatic networks with complex behavior are typically based on one or just a few feedback reactions. Figure 3a illustrates the general principle behind the design of artificial ERNs with complex nonlinear dynamic behavior: a nonlinear core reaction motif is chosen and combined with additional processes; the actual behavior of this combination is then dependent on the type of reactor that is used. Although the additional processes are typically dependent on the enzymes chosen for the core motif and thus the two are often intertwined, we explicitly separate the two to aid the reader in appreciating the network design and analyzing their dynamics.

Figure 3

Figure 3. Design of artificial ERNs with complex nonlinear dynamic behavior. (a) General design principle for ERNs with complex dynamic behavior. (b) Central reactions and motifs providing core nonlinearity. (c) Additional processes serving to alter the kinetics of the core motifs and connect different nodes. (d) Types of reactor or setup. (e) Examples of complex nonlinear dynamic behavior available in artificial ERNs. Illustrations: (e7) 50 μm polyacrylamide beads with immobilized urease in a solution containing 5 mM acetic buffer, 50 mM urea, and fluorescent dye SNARF-5,6, scale bar 100 μm, own data; (e8) redox forms of the dye ABTS in the solution containing glucose oxidase (GOx) and horseradish peroxidase (HRP). Adapted with permission from ref (78). Copyright 2018 Springer Nature.

The full toolbox of nonlinear core enzymatic reactions described in the literature to date is presented in Figure 3b. The use of autocatalytic core reactions is prominent not only in enzymatic networks but also in organic and DNA networks, (42) and the design principles of autocatalytic networks were recently reviewed by Semenov and Plasson et al.. (43,45) The exploitation of feedback in the engineering of ERNs was reviewed by Bánsági and Taylor. (44,45) The three general ways to autocatalysis in ERNs are shown in Figure 3b1–b3. Figure 3b1 shows autocatalysis by self-activation of an inactive zymogen, with the trypsinogen–trypsin (Tg–Tr) pair as a well-known example discussed in detail in section 3.2. Product autoactivation (Figure 3b2) is another very general mechanism with examples mostly involving urease (section 3.1). The cleavable inhibitor positive feedback is relevant for proteolytic networks (see section 3.2) and shown in Figure 3b3. As we discuss later, individual autocatalytic steps are not a general prerequisite to complex behavior, and autocatalysis can also emerge from closed cycles where the separate steps are linear. (242) The alternative nonlinear cores, namely, those built on substrate competition (Figure 3b4) and switching between enzyme redox forms (Figure 3b5), also deliver nonlinear dynamics, as discussed in section 3.3 using the example of oxidoreductases. Additionally, there are three types of nonlinear cores that are somewhat underexploited. Product inhibition (Figure 3b6), an example of which is the autoinhibitory catalysis of esterase, can be a nonlinear core but has only been found as an additional process in networks comprising a urease core (see section 3.1). The enzyme oligomerization (Figure 3b7) and allosteric effects (Figure 3b8) are well-known ways to achieve nonlinearity, but they have only been demonstrated in theoretical studies and in in vivo networks. (20,21)
To achieve controlled complex behavior, a core motif is supplemented and altered by what we call additional processes (Figure 3c). These processes may also serve to connect different network nodes. Inhibition (Figure 3c1) slows down the reaction rate of a certain enzyme, which is an exceptionally useful feedback loop in the case when an inhibitor is produced by another enzyme (Figure 3c2). Effectively, this connects two enzymatic activities that are otherwise independent (see section 3.2 on the trypsin oscillator). Another way to connect enzymatic activities is via the use of antagonistic enzymes (Figure 3c3), with examples mostly on pH-dependent urease networks (section 3.1). Finally, throughout sections 3.13.3 we discuss how transport processes enrich modes of dynamic behavior. Transport across membranes, compartment borders, between phases, and simple diffusion (Figure 3c4) add complexity and provide important types of nonlinear dynamics relevant to nature (collective behavior and patterns, Figure 3e7 and 3e8 correspondingly). The influence of transport processes has been recently studied theoretically, highlighting how spatial localization and transport phenomena in nonstirred systems can yield complex nonlinear responses even in fairly simple networks. (46,47) The transport processes are only relevant in nonstirred systems (Figure 3d1 and 3d5) or in compartmentalized systems (Figure 3d6). Other reactor features presented in Figure 3d can supply reagents and remove products, maintaining the network out of equilibrium (Figure 3d3 and 3d4).
Combinations of the core reactions and additional processes yield a range of possible complex behaviors as shown in Figure 3e. While some types are more common and relatively straightforward to obtain (for instance, clock behavior (Figure 3e1) or pulse-like response (Figure 3e2)), some other types are rare (chaos as shown in Figure 3e6 is, to the best of our knowledge, only obtained in peroxidase–oxidase reaction (section 3.3)).
In order to provide the reader with a readily accessible overview, we grouped the synthetic ERNs with complex nonlinear behavior according to the central enzymatic reaction around which the network is built. For all of the discussed networks, we refer to the motifs they contain and behavior types they produce, as per Figure 3.

3.1. Networks Based on the Urea–Urease Reaction

In general, enzymes producing acid or base are a fruitful ground for designing systems with complex dynamics due to the nonlinear kinetics intrinsic to pH-reactive systems. (48−50) A simple and well-studied enzymatic network that produces nonlinear behavior is the urea–urease network. The enzyme urease (Ur) is found in many bacteria and plants. It catalyzes the hydrolysis of urea into carbon dioxide and ammonia, resulting in an increase in pH. The enzyme has a standard bell-shaped activity–pH profile with an optimum around 7.5. (51) When starting from a lower pH (usually buffers of pH 3.5–5.0 with low buffer capacity), the enzyme basifies the solution, thereby overcoming the buffer and increasing its own activity, up to pH 7.5. As the reaction proceeds further to even higher pH values, the Ur activity drops again and the final pH of about 8.5 is that of ammonia/ammonium buffer. This process creates an autocatalytic sigmoidal signature in pH vs time, and the time of the steep transition from low to high pH state is addressed in the literature as “clock time” (Figure 3e1). (51,52) The increase in Ur activity due to the Ur reaction is an example of product autoactivation (Figure 3b2).
Compartmentalization and coupling to diffusion enrich the range of complex behaviors obtained in Ur networks. Effectively, the simple combination of the Ur nonlinear core (Figure 3b2) with transport phenomena (Figure 3c4) yields five types of complex behavior (Figure 3e1–e7), particularly because diffusion coefficients for H+ and other species are very different. Ur-loaded millimeter-sized particles exhibit quorum-sensing-like autoactivation (similarly to Figure 3e7), which is a consequence of autocatalysis and diffusion front propagation. (53) Miele et al. demonstrated the collective synchronized behavior of Ur-loaded microvesicles. (52) Muzika et al. showed sustained oscillations in a flow reactor with differential influx of urea and H+, (54) building on earlier work on bistability, (55) and noise-induced irregular oscillatory switching. (56) Oscillations were also obtained in a system with Ur-loaded lipid vesicles by combining the clock reaction inside of the vesicle with the influx of urea and H+ from the bulk phase. (57) Finally, oscillations in packed arrays of Ur-loaded beads with influx of urea and acid from the bulk solution were predicted theoretically but not yet experimentally proven. (58)
An alternative approach to obtain richer dynamic behavior from the urea–urease network is combining it with antagonistic acidifying enzymes (Figure 3c3). Heinen et al. showed that a urea–urease/ester–esterase network produces pulse-like pH responses under batch conditions with, first, a Ur activity-related increase followed by an esterase (Est) activity-induced decrease in the pH (see Figure 4d for network topology). The kinetic profile of the response can be finely tuned by changing the buffer and substrate composition. (59) Compartmentalization of Ur and Est units in a two-layered gel aided in programming of transient acidic pH flips. (60) This latter work also addressed the lack of nonlinearity of the Est response (as compared to Ur), which originates in the shape of the Est pH–activity profile and its autoinhibitory nature (Figure 3b6). An alternative acidifying enzyme is glucose oxidase (GOx), converting glucose to δ-gluconolactone, hydrolyzing spontaneously into gluconic acid. Wang achieved dynamic pH switching in response to the addition of urea and glucose in a system with Ur and GOx incorporated in pH-responsive polymersomes. (61) While Ur is very fast, all of the acidifying enzymes explored thus far exhibited certain limitations on rates. We hypothesize that coupling the urea–urease network to faster acidifying counterplayers would yield more robust dynamic behavior and possibly oscillating systems. The pH transition caused by the Ur-based ERNs can be coupled to a wide range of chemical and physical processes, (62) such as polymerization and sol–gel transitions, (59,63) gel growth, (64,65) and precipitation of CaCO3, (66) that all can serve as handles for functionalities as discussed in section 5.

Figure 4

Figure 4. Overview of enzymatic reaction networks that are controlled by different external stimuli. (a) Schematic representation of the trypsin oscillator where the enzyme activity is regulated by a photocleavable inhibitor. Adapted with permission from ref (86). Copyright 2020 John Wiley and Sons. (b) Photoswitchable inhibitor that can reversibly control the enzyme activity in an out-of-equilibrium system using light. Adapted with permission from ref (88). Copyright 2020 licensed under CC 4.0 American Chemical Society. (c) DASA-based polymersomes enable reversible control over the accessibility of substrate toward the active site of an enzyme by light. Adapted with permission from ref (93). Copyright 2022 licensed under CC 4.0 Springer Nature. (d) Ur–Est-based network where Ur increases the pH and Est decreases the pH. Adapted with permission from ref (65). Copyright 2021 John Wiley and sons. (e) Haber–Bosch process where NH3 can be generated from H2 and N2 using enzymes, hydrogenase, and nitrogenase by applying an electrochemical potential. Adapted with permission from ref (106). Copyright 2017 John Wiley and Sons. (f) Cascade network that is spatiotemporally controlled by sound. Adapted with permission from ref (111). Copyright 2022 licensed under CC 4.0 Springer Nature.

3.2. Networks Based on Proteases

A general route to autocatalysis is to exploit the formation of certain proteases from their inactive precursors (zymogens or proenzymes), as shown in Figure 3b1. Semenov et al. constructed a bienzymatic oscillatory network with a core reaction of trypsin (Tr)-mediated trypsinogen (Tg) activation modified with a delayed negative feedback loop via an aminopeptidase-activated trypsin proinhibitor (combination of motifs Figure 3b1, 3c1, and 3c2; full network topology is depicted in Figure 4a). (67) Further investigations of this trypsin oscillator included studies on the boundary conditions of the oscillatory regimes using a range of slightly different inhibitors in the negative feedback loop (68) and synchronization with external temperature oscillations. (69) Exploiting protease zymogen activation as a core step, Helwig et al. demonstrated adaptation to an external stimulus in an incoherent type 1 feed-forward loop network motif, comprising the chymotrypsinogen–chymotrypsin (Cg–Cr) pair and Tr. (70) The network responded with a peak to a persistent external stimulus (influx of Tr) and adapted over time, bringing the response Cr activity close to the original baseline, a behavior type in Figure 3e3. The possibility to use autocatalytic trypsinogen activation for signal enhancing was demonstrated in a diffusion-reaction system with trypsin and its inhibitor in a gel. (71) Finally, Kriukov et al. showed in a Tr–Tg network with an additional inhibition step that the combination of autocatalytic activation and flow leads to hysteresis, and the inhibition can be used to control the network dynamics in a history-dependent manner. (72)
A different and novel type of autoactivation in enzymatic systems was introduced by Pogodaev et al. using slow substrates (cleavable inhibitors) for proteinases Tr, Cr, and elastase (Els), as shown in Figure 3b3. (73) The reactions started in highly inhibited states, where the slow substrates occupied the active sites of the enzymes. With time, the peptides were cleaved into low-inhibitory fragments, thereby liberating more of the free enzymes, leading to an autocatalytic increase in enzyme activity. It was also demonstrated that in mixtures with Cr, Tr, and their corresponding slow substrates, crosstalk occurred, while Cr and Els remained orthogonal under these conditions, which opens roads to modular construction of modified networks. By converting a cleavable peptide inhibitor into a phosphate-modified proinhibitor, the behavior of the network was coupled to the activity of alkaline phosphatase, which acted as an initiator of the flip response in Cr activity. This is another example of using the motif of proinhibitor activation (Figure 3c1 and 3c2)) to connect behaviors of two enzymes. (73)

3.3. Networks Based on Oxidoreductases

In oxidoreductase networks, the nonlinearity often originates from the switching between redox and coordination forms of the enzymes (Figure 3b5).
The earliest example of an in vitro enzymatic oscillatory reaction is the peroxidase–oxidase (PO) reaction, first described by Yokota and Yamazaki in 1965. (74) Oscillations occur during the horseradish peroxidase (HRP)-catalyzed oxidation of NADH (reduced nicotinamide adenine dinucleotide) by oxygen in the presence of additives such as methylene blue and 2,4-dichlorophenol in a semibatch stirred reactor with an influx of NADH and diffusion of oxygen from a gas mixture and no outlet as shown in Figure 3d3. The PO reaction has a complex mechanism comprising multiple redox and coordination forms of the heme cofactor of HRP and very rich dynamics that include normal, period-doubled, and mixed-mode oscillations, quasi-periodicity, and chaos, shown in Figure 3e5 and 3e6. (75,76) It is hypothesized that many more new dynamic states of the PO reaction are still to be discovered using experimental parameter combinations that go beyond the current regimes, with the pH being an especially important control parameter. (76) We refer the reader to more specialized literature for further details. (75,77)
Recently, HRP was combined with GOx to produce tunable pulse responses in a stirred reactor (Figure 3e2) and form spatiotemporal patterns (Figure 3e8) in a nonstirred thin layer experiment. Remarkably, this example of complex dynamics is not comprised of individual steps of autocatalytic activation or inhibition, with the nonlinearity originating only in delayed feedback loops and substrate competition (Figure 3b4). (78) A unique aspect of the behavior of this network is the spontaneous formation of patterns of convection flows when not stirred.
Another enzyme exhibiting autocatalytic kinetics is hydrogenase, a metalloenzyme catalyzing the reaction H2 = 2H+ + 2e. The mechanism of autocatalysis differs from the simple acid–base activation/deactivation shown for urease and is thought to be connected to redox forms. (79) The hydrogenase reaction has been observed to produce reaction fronts and oscillations in a thin layer reaction–diffusion experiment via a mechanism that is not yet fully understood. (80)

3.4. Summary

In summary, synthetic in vitro ERNs with complex behavior have exploited a limited set of core reactions but demonstrated a large range of types of complex behavior. As the recent finding with hydrogenase demonstrates, identifying novel experimental realizations of theoretical network motifs is still a fruitful area of research. Expanding the types of enzymes and combining multiple enzymes into more complex networks are necessary to broaden the scope of accessible network motifs and to realize the theoretical motifs not found in real networks yet. Currently, only two out of seven EC classes of enzymes (oxidoreductases and hydrolases) are used as core reactions in complex ERNs.
An important open question is the design of an ERN which would sustain oscillations under batch conditions (stirred or nonstirred), an enzymatic analogue of the Belousov–Zhabotinsky (BZ) reaction. (247) The oscillators described in the sections 3.13.3 rely on fluxes of reagents (by means of flow reactors or transport processes between compartments). In contrast to the BZ reaction, all enzymatic networks reported thus far consume all “fuel” in a single reaction cycle under well-stirred conditions, thus making it impossible to sustain oscillations in batch. In general and as highlighted by the nonautocatalytic HRP/GOx network example, it is still an open fundamental question what network motifs are required to yield a certain desired type of nonlinear behavior. A strong coupling between experimental and theoretical studies is needed in this regard, especially when incorporating diffusion and other transport phenomena.

4. Influence of External Stimuli on ERNs

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It is difficult to engineer the properties of complex enzymatic reaction networks as their dynamic output requires all reaction rates to be adjusted to each other, while the formation of byproducts or the emergence of hidden interactions could prevent or disrupt the dynamic behavior of the system. (81) Fine-tuning rates by controlling the activity of individual enzyme activities is therefore desired. Multiple methods were developed to gain control over enzyme activity by applying an external stimulus such as light, pH, redox potential, heat, magnetic field, and sound. (81−83) In this section, we will focus on the use of external stimuli to control enzymatic reaction networks that consist of two or more enzymes.

4.1. Light

Light irradiation in combination with photocleavable or photoswitchable molecules allows for selective, rapid, and spatiotemporal control over enzyme activities. (81,84,85)
Pogodaev et al. used ultraviolet (UV) irradiation of a photocaged irreversible inhibitor to obtain control over the oscillations of the Tr oscillator discussed in section 3.2 and presented in Figure 4a. (86) The oscillations were dependent on the duration and timing of UV light, demonstrating that external stimuli can introduce extra complexity without disrupting the rest of the system. However, for many enzymatic systems it is desirable to obtain reversible control over the enzymatic activity. The groups of Feringa, König, and Szymanski have reported many examples where photoresponsive molecules are used to reversibly inhibit specific enzymes upon light irradiation. (83,85,87) For this, photoswitchable molecules such as azobenzenes, spiropyrans, diarylethenes, and donor–acceptor Stenhouse adducts (DASAs) could be used. (85)
The method to use photoswitchable molecules in combination with light is known as the chromophore-warhead strategy, where one of the two conformations acts as a stronger inhibitor. The warhead, a functional group that is known to inhibit the enzyme, can be covalently attached to a chromophore that can change their 3D structure upon light irradiation. (85) Using this strategy, Teders et al. recently designed an azobenzene-based inhibitor for Tr and Cr. (88,89) The activity of these enzymes could be reversibly adjusted under flow conditions upon different light intensity, duration, and wavelength of light, see Figure 4b. These systems exhibited an ultrasensitive response, illustrating potential for using light as an input for controlling complex dynamics in ERNs.
In addition to directly photoswitching molecules that interact with the enzyme (via the active site or allosteric interactions), one could alter the kinetics by controlling the accessibility of the substrate toward the enzyme, as demonstrated by encapsulating enzymes in vesicles or polymersomes and controlling the transport of substrate using light. (90−93) The latter example, presented in Figure 4c, was based on polymersomes containing a DASA dye in the polymer shell, allowing switching between semipermeable states. (93) These DASA polymersomes were filled with Est and then mixed with Ur containing polymersomes that were permeable and light insensitive. Upon green light irradiation, the Est–DASA polymersomes became semipermeable, which resulted in the conversion of ethyl acetate into acetic acid by Est. A concomitant pH decrease from pH ≈ 8 to pH ≈ 7 activated urea hydrolysis by Ur, which resulted in a pH rise. The pH increase by Ur resulted in the deprotonation of a pigment that absorbs green light in the protonated form. Therefore, the absorption of green light stopped, and the Est activity could be reactivated.

4.2. pH

All enzymes have a specific pH value at which their activity is maximized. (94,95) In pH-dependent networks, the pH can be altered by addition or generation of acid or base by another enzyme. Often used combinations contain Ur, Est, GOx, or HRP as sources of acid or base. (48,59,96) As described by Che et al., the addition of chemical fuel can influence the biocatalysis of pH-sensitive polymersomes loaded with HRP or Ur. (97) The polymersomes shrank in a high-pH buffer due to deprotonation, whereby they became impermeable and thereby inactivate the enzyme. Addition of hydrochloric acid (HCl) and urea (fuel) resulted in a decrease of pH, swelling of the polymersomes, and activation of the enzymes. The depletion of urea resulted in shrinkage of the polymersomes and a decrease in the enzymatic activity. The activation cycle could be repeated multiple times by addition of acid and fuel to create a pH-dependent system. Maity et al. constructed an Ur/Est loop by encapsulating Ur and Est in different hydrogel beads that formed pH fronts. (65) As described in section 3.1, the conversion of urea by Ur results in a pH increase which is counteracted by the conversion of ethyl acetate into acetic acid by Est, see Figure 4d. The same pH-dependent enzymatic reaction network was used to create enzymatic logic gates. (98)
Wang et al. developed a DNA-based hydrogel that contained GOx, acetylcholine esterase (AchE), and Ur to alter the pH. (99) GOx and AchE activity resulted in a decrease in pH, while the hydrolysis of urea by Ur increased the pH. Different enzyme compositions allowed control over the pH and thereby the stiffness of the hydrogel. Control over enzymatic batch processes was obtained with acid-producing enzymes and poly(methacrylic acid) (PMAA)-functionalized gold particles. First, these particles were dispersed in the reactor, and reaction-induced pH changes were allowed to take place. When the buffer capacity was exceeded, protonation of the PMAA led to aggregation of the functionalized gold nanoparticles, which could be resuspended upon addition of fresh buffer and substrate. (100) Instead of changing the pH of the bulk solution, Zhang and co-workers showed that the enzyme kinetics of immobilized enzymes could also be influenced by pH changes in the microenvironment. (95) The local pH around cytochrome C, which is most active under acidic conditions, could be lowered by immobilization on negatively charged high-density polyelectrolytes that can attract ions of opposite charge. The throughput of a cascade with immobilized cytochrome C and d-amino acid oxidase was increased 10-fold by lowering the local pH of cytochrome C in an alkaline environment where d-amino acid oxidase is most active.

4.3. Electrochemical Control

The redox potential can be used as an external stimulus to control the activity of enzymes. (101−103) Mallawarachchi et al. showed that the accessibility of the substrate toward the active site of hexokinase (HK) entrapped in an electroresponsive hydrogel was altered upon application of an electrochemical potential due to the reversible contraction and expansion of the hydrogel. (104) The reaction kinetics of enzymes can also be modified by trapping enzymatic cascades in a porous conducting metal oxide electrode material. This was done by Morello et al. to reversibly recycle a nicotinamide cofactor that was generated with an enzymatic cascade consisting of ferredoxin NADP+ reductase, l-malate NADP+ oxidoreductase, fumarase, l-aspartate ammonia-lyase, and carbonic anhydrase. (105) It was shown that the reaction direction and rate of ferrodoxin NADP+ reductase could be regulated based on the electrochemical potential which allowed control over the synthesis of aspartic acid from pyruvic acid or the reverse reaction. Furthermore, Milton et al. used a combination of nitrogenase and hydrogenase enzymes to electrify the production of NH3 from N2 and H2, which is depicted in Figure 4e. In this work, methyl viologen was used as an electron transfer agent between the enzymes and the electrodes to produce NH3 and an electrical current at the same time. (106)

4.4. Heat

Heat can be used as a parameter to influence the dynamics of ERNs since the rate constants of enzymes are dependent on temperature. (107,108) As mentioned in section 3.2, Maguire et al. studied the influence of temperature on the Tr oscillator. They investigated the effect of temperature perturbations under conditions close to the so-called tipping point, the boundary between the oscillatory and the steady state regime. (68) Close to the boundaries of the stable oscillatory regime (between 15 and 38 °C), sensitivity to short temperature perturbations increased, causing the recovery time to stable oscillations to increase as well. In another study, they investigated the use of small temperature oscillations to regulate the periodicity of the Tr oscillator. (69) For temperature oscillations with an amplitude of just 3 °C, the periodicity of stable oscillations could be shifted to match the externally induced periodicity through a process known as phase locking or synchronization. Outside the phase-locking regime, quasi-periodic behavior was observed. Since the temperature can have a significant influence on the dynamic behavior of a network, a method to locally adjust the temperature was developed by Zhang et al. This involved the embedding of enzymes on platinum nanoparticles decorated with thermoresponsive copolymers, allowing local heating by irradiation with near-infrared light. The polymer–enzyme hybrids formed aggregates in solution below a certain temperature and disassembled above that temperature, thereby adjusting the accessibility of the substrate toward the active site of the enzyme. In general, the formation of aggregates resulted in a decreased enzymatic activity compared to the disassembled state of the particles. (108) Thermoresponsive polymers were also used by Gobbo and co-workers to reversibly regulate enzyme activity by contractions of a polymer–protein-based protocell upon temperature changes. (109)

4.5. Other Control Factors

The Katz group presented an enzymatic system where a magnetic field was used to induce local pH changes, yielding reversible control over the enzyme activity. (110) Here, two types of enzyme-modified magnetic nanoparticles that contained amyloglucosidase (AMG) and Ur or Est were used. Upon applying a magnetic field, the nanoparticles with AMG and Ur displayed aggregation. Due to the aggregation, the enzymes were in closer proximity to each other and therefore showed higher enzyme activity. The AMG activity was monitored by a cascade reaction with GOx and HRP.
Furthermore, sound can be used as an external stimulus to control the dynamics of enzymatic cascades. Gradients and patterns of vibrations in an aqueous medium form upon application of sound, see Figure 4f. Dhasaiyan et al. found that at the minima of the vibration, gas dissolution was lower than that at the maxima of the vibration. This was exploited in an enzymatic cascade dependent on dissolved oxygen. First, GOx-FAD was reduced to GOx-FADH2 by glucose consumption. The dissolved oxygen was used to produce H2O2 that can be used by HRP to oxidize ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid). ABTS is a colorful compound that was used to visualize gradients in the enzyme activity of the cascade, see Figure 4f. (111) Please note, the compartmentalization of enzymes or the immobilization of enzymes on different types of support can enhance the reaction rate and therefore the total output of an enzymatic cascade. For more information about compartmentalization, (120−122) in DNA nanocages, (123−128) immobilization in metal organic frameworks (MOFs), (129−133) or immobilization of enzymes on other supports, (112−119) we would refer to other reviews.

5. Designing “Life-Like” Systems Using ERNs

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In previous sections, we discussed how ERNs can exhibit intricate, complex features such as feedback loops, bistability, ultrasensitivity, or adaptive behavior. Employing these complex features of enzyme-catalyzed reactions to trigger morphological transformations and modulate the physical properties could lead to the development of so-called life-like systems. In this section, we will delve into specific examples that highlight the design of complex systems utilizing enzymatic networks.

5.1. Dynamic Materials

Enzyme-catalyzed reactions typically occur under mild conditions and show high substrate specificity, making enzymes an attractive choice for the design of new dynamic materials. To make the link to materials, researchers studied the influence of enzymatic reactions on polymer chains in order to induce self-assembly or disassembly or other morphological reorganizations. (134,135) For example, Amir et al. reported the use of acid phosphatase to cleave phosphate moieties of hydrophilic monomers, resulting in the formation of an amphiphilic diblock copolymer of polyethylene glycol (PEG) and poly(4-hydroxystyrene) that assembled into spherical micelles. (136) In another example, the autocatalytic nature of urea-Ur was harnessed to control base-catalyzed thiol-Michael addition reaction for time-lapse hydrogelation. (63) Enzyme-catalyzed reactions have been utilized to initiate polymerization of monomeric building blocks. (137,138) Mao et al. used acid phosphatase and GOx to make hydrogels out of supramolecular and polymeric networks. (139) A similar strategy was later implemented to design printable hydrogels using GOx and HRP. (140) In this study, an acrylic acid-modified hydrogelator (NapFFK-acrylic acid) was used as a monomer, undergoing self-assembly to form hydrogels (gel-I, Figure 5a). GOx converted glucose to gluconic acid and subsequently reduced O2 to H2O2. Next, HRP utilized H2O2 to catalyze the formation of acetylacetone (AcAc) radicals via oxidation of AcAc. These radicals initiated polymerization of poly(ethylene glycol) methacrylate (PEGMA) with acrylic-modified hydrogelators, resulting in a cross-linked hydrogel (gel-II, Figure 5a). Gel-II showed very good mechanical properties with an approximately 16 times higher storage modulus compared to gel-I (without cross-linking). Klemperer et al. utilized a similar strategy to design cytocompatible bioinks with interpenetrating polymer network (IPN) formed under mild and aerobic conditions. (138)

Figure 5

Figure 5. Overview of dynamic materials designed with enzymatic reaction networks. (a) Enzymatic polymerization of hydrogels. Adapted with permission from ref (140). Copyright 2016 licensed under CC 3.0 Royal Society of Chemistry. (b) Schematic representation of cross-link degradation of a hydrogel controlled by an enzymatic network. Adapted with permission from ref (146). Copyright 2017 John Wiley and sons.

Moreover, enzyme-mediated de-crosslinking or cleavage has been exploited to install stimuli responsiveness in the presence of enzymes. Hydrogels containing enzyme-cleavable groups were used for designing responsive materials susceptible to enzymes like proteases, lipase, Est, and phosphatase activities. (141−144) Yang et al. demonstrated for the first time an enzyme-regulated reversible gel–sol transformation. They utilized kinase–phosphatase switch to regulate assembly of peptide-based hydrogelator (Nap-FFGEY). The addition of kinase and adenosine 5′-triphosphate (ATP) led to phosphorylation of hydrogelator, disrupting self-assembly, while phosphatase reversed this process, restoring the self-assembly property. (141) Interestingly, micelles formed from polymer–peptide block copolymers modified with substrates for different enzymes (protein kinase A (PKA), protein phosphatase-1 (PP1), and matrix-metalloproteinases MMP-2 and MMP-9) showed different morphologies and aggregation behavior upon enzymatic reactions. (145) In addition, Postma et al. reported a systematic approach in designing adaptive matter by integrating reaction networks with materials. (146) They designed a polyacrylamide (PAAm)-based hydrogel containing two orthogonal types of cross-links, which could either be enzymatically degraded or formed (Figure 5b). Tr rapidly cleaved the first cross-links, thus forming a liquid phase. Simultaneously, Tr slowly cleaved a copolymerized thioester (cross-link precursor), and these newly exposed thiol groups reacted rapidly with linker (poly(ethylene glycol)-bis-maleimide) to form a new gel. Upon addition of Tg and Cg, Tr was formed autocatalytically from Tg and thus speeding up the degradation and activation processes of the first and second cross-links, respectively. Tr also rapidly converted Cg into Cr, which induced even faster cleavage of copolymerized thioester. Finally, to set a threshold for activating the entire network, soybean trypsin inhibitor (STI) protein was introduced, which deactivated the enzymatic network by inhibiting Tr activity (Figure 5b).
Enzyme-catalyzed reaction products can also be used as triggering stimuli which influence polymeric assemblies to exhibit dynamic functions. For example, oxidases like GOx, sarcosine oxidase (SOx), choline oxidase (COx), and urate oxidase (UOx) generate H2O2 upon oxidation of their substrates. Hydrogels containing both hydrolases (AchE) and oxidases displayed a gel–sol transition in the presence of hydrolase substrate (acetylcholine) by the cascade generation of H2O2. (147) In earlier sections, we have discussed how enzymes like Ur, GOx, and Est are capable of controlling the pH of the system environment. Walther and co-workers have demonstrated how enzyme-mediated pH feedback can be introduced to design stimuli-responsive smart materials. For example, Heuser et al. integrated Ur-mediated pH feedback to a pH-responsive photonic gel composed of polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) diblock copolymers to control swelling of the photonic hydrogel, which in turn controls transient optical reflection. (148) Moreover, enzyme-mediated pH change was used to design temporal materials where gelation is controlled by a change in pH via enzymatic reactions. (59,65) To demonstrate pH-dependent transient gelation, Heinen et al. used a hybrid hydrogel made of DNA-containing polyacrylamide copolymer with pH-responsive DNA cross-linker (i-motif). This pH-responsive unit formed a tetraplex intramolecularly at low pH and as a result did not take part in cross-linking (sol state). With increasing pH, the unreactive tetraplexes unfold and induce hybridization with the copolymerized strand, resulting in a gel state. A sol–gel–sol transition was observed by the antagonistic effect of Ur-mediated pH increase and pH decrease by Est. (59) In short, enzyme-catalyzed chemical reactions have a diverse range of applications, from enabling cross-linking polymerization under mild conditions to directly controlling material properties to install responsiveness.

5.2. Enzyme-Powered Motile Systems

The inspiration for designing motile systems stems from various biological motor proteins which operate via conversion of chemical energy into mechanical motion by the hydrolysis of ATP. Besides these motor proteins, enzymes have shown increased diffusion during substrate turnover and can impart forces (ca. 10 pN) quite comparable with biological motor proteins. (149,150) In addition, an asymmetric distribution of reactant and product molecules during enzyme catalysis can create local gradients of concentration or an electrical field, which can lead to enhanced diffusion of enzymes. (151) However, active movement of enzymes in the presence of substrates is part of an ongoing debate, and we refer to other literature sources for a more thorough discussion. (152,153) A number of research groups have worked on endowing small micro/nanosized particles with enzymes in order to create motile systems. (154−156) These particles achieved motility by enzyme-catalyzed chemical reactions. For example, Ur can show ionic self-diffusiophoresis due to the generation of a local electric field from diffusivity differences between the oppositely charged ions (NH4+ and CO32–) formed from urea. (157) Ur has been extensively used as a catalytic engine to design self-propelling motors. (158−160) On the other hand, oxygen bubble generation from H2O2 by Cat or a pair of enzymes like GOx/Cat can propel microswimmers opposite to bubble formation. (161,162) One promising system is based on stomatocytes, bowl-shaped particles formed by deformation of polymersomes by osmotic pressure. During the deformation process, enzymes can be encapsulated in the nanocavity that is formed when the shell folds in on itself, and the enzymatic activity of the encapsulated enzymes provides a force that can propel these stomatocytes. (163) For example, a metabolic network of six enzymes was compartmentalized in stomatocytes, which was responsible for converting glucose (fuel) into motion (Figure 6a). This network started with an ATP-mediated activation module containing HK and pyruvate kinase with phosphoenolpyruvate (PEP, phosphate donor) and glucose (energy source). Pyruvate (the product of the first cycle) triggered the pyruvate–l-lactate cycle where l-lactate dehydrogenase (LDH) consumed pyruvate. But, l-lactate oxidase (LO) catalyzed the reverse reaction. NADH was regenerated by the conversion of glucose-6-phosphate by glucose-6-phosphate dehydrogenase (G6PDH) into 6-phosphogluconolactone. This resulted in net H2O2 production, which was converted to molecular oxygen by Cat. (164) Here, ATP determined the concentration of NADH, thus regulating the entire network. In another example, amyloid microphases loaded with alcohol dehydrogenase (ADH), SOx, and Cat exhibited microscopic motility. These self-assembled structures contained imidazole moieties, which hydrolyzed the 6-methoxy naphthyl alcohol ester of N-methyl glycine (starting substrate) to 6-methoxynaphthyl alcohol and N-methyl glycine. Subsequently, ADH converted 6-methoxynaphthyl alcohol to 6-methoxynaphthaldehyde (a fluorescent compound), while the cascade of SOx and Cat utilized N-methyl glycine to generate oxygen bubbles, responsible for the observed motility. (165) Increased diffusive movement of free enzymes and enzyme-coated particles toward higher substrate concentration has been reported and extensively studied. (150,166,167) The Wilson group designed Cat-powered PLGA (poly(lactic-co-glycolic acid) micromotors as chemotactic drug delivery vehicles, which can follow the H2O2 gradient produced by macrophage cells. (168) Joseph et al. demonstrated how coupling enzyme cascades (GOx and Cat) with asymmetric polymeric vehicles could be used to design potential chemotactic drug delivery vehicles for crossing the blood–brain barrier. (169)

Figure 6

Figure 6. Overview of enzyme-powered motile systems designed with enzymatic reaction networks. (a) Self-propulsion of stomatocytes by generating oxygen from glucose using an encapsulated metabolic enzymatic network. Adapted with permission from ref (162). Copyright 2022 American Chemical Society. (b) Illustration demonstrating expansion and contraction of a spring made of calcium alginate by antagonistic interaction of two enzymes, i.e., Ur and GOx. Adapted with permission from ref (169). Copyright 2021 Springer Nature.

Aside from enzyme-powered self-propulsion, oscillatory movement was achieved in self-assembled organoclay/DNA semipermeable microcapsules containing Cat and GOx. In the presence of H2O2, Cat produced oxygen bubbles which were taken up by GOx in the presence of glucose. The generation and consumption of oxygen bubbles was responsible for sustained oscillatory movement. The system was able to achieve multiple oscillations under a continuous flow of glucose and H2O2. (170) In other recent work by the same group, the interaction between protocells and their environment was explored by designing microactuators capable of chemically induced spring-like compression and relaxation. (171) Helical filaments of calcium alginate containing Ur-loaded proteinosomes were fabricated using microfluidics. When these coiled hydrogels were exposed to urea, carbonate ions were produced and started chelating the calcium ions (Ca2+) from the hydrogel cross-links. This Ur-mediated removal of Ca2+ ions led to the release of the stored elastic potential energy, which in turn induced extension of the helical filaments. To contract these extended filaments, they were exposed to proteinosomes containing GOx. In the presence of glucose, gluconic acid was produced, which dissolves CaCO3 and releases Ca2+. This protocell-mediated Ca2+ flux in turn restored cross-links in the calcium alginate matrix which led to a slow retraction of the extended filaments (Figure 6b). These examples highlight current endeavors to utilize enzymes to control motility of artificial systems, which can be useful in designing intelligent sensors and novel therapeutic vehicles. (189)

5.3. Communication in Compartmentalized Bioreactors

Compartmentalization is an effective strategy to segregate enzymes catalyzing sequential reactions and can be helpful to increase productivity by concentrating substrates and avoiding unwanted side reactions. (120,172,173) This spatial segregation, coupled with interactions between compartments with distinct chemistries, can further lead to the emergence of life-like properties in artificial systems like predator–prey behavior, (174−176) chemical signaling, (177−181) and chemostructural feedbacks. (171,182) In this context, Elani et al. designed a multicompartment vesicle-based platform to spatially segregate reaction processes where chemical signals can transverse from one compartment to another. A three step cascade by lactase, GOx and HRP was conducted where each enzyme catalyzed step was isolated in distinct compartment. (183) Wang et al. developed methodologies to design microarrays of giant unilamellar lipid vesicles (GUVs) containing GOx and HRP to facilitate controlled chemical signaling in the presence of melittin, a pore-forming peptide. (178) Buddingh’ et al. demonstrated chemical communication between giant vesicles through allosteric signal amplification. They used glycogen phosphorylase b (GPb) which switched to a high-activity state upon binding adenosine 5′-monophosphate (AMP). The sender vesicle contained apyrase which produced AMP in the presence of ATP (Figure 7a). The produced AMP diffused to the receiver vesicle, where a small ERN comprising phosphoglucomutase (PMG), G6PDH, GPb, and glycogen was compartmentalized. As AMP acts as allosteric activator of GPb, it induced conversion of glycogen to glucose-1-phosphate, followed by the conversion of glucose-1-phosphate to glucose-6-phosphate by PMG, and subsequent generation of NADH by G6PDH (Figure 7a). This allosteric amplification of the initial weak signal from the sender vesicles, achieved through GPb activation, resulted in a robust response (high NADH output) in the receiver vesicles. Remarkably, the strong signal amplification engineered into this pathway allowed long distance (5 mm) communication between senders and receiver vesicles. (179)

Figure 7

Figure 7. Overview of communication in compartmentalized bioreactors facilitated by enzymatic reaction networks. (a) Communication between sender and receiver GUVs where the sender produced AMP which allosterically activated ERN within receiver GUVs. Adapted with permission from ref (179). Copyright 2020 licensed under CC 4.0 Springer Nature. (b) Cartoon representation of artificial response–retaliation behavior among protocell communities. In the presence of glucose, GOx containing protease-sensitive proteinosome (P) released acid (2) which disassembled pH-sensitive protease containing coacervate (CT) and released protease (3). Coacervate CK recaptured protease and eventually destroyed P. Adapted with permission from ref (175). Copyright 2019 John Wiley and sons.

Qiao et al. achieved artificial response–retaliation behavior using ternary protocell populations. These populations included proteinase K-sensitive GOx-containing proteinosomes (P) that released H+ in the presence of glucose. (175) These also consisted of small pH-sensitive proteinase K-containing polypeptide (poly-d-lysine)/adenosine 5′-diphosphate (ADP) coacervates (CT) and pH-resistant positively charged polymer (poly(diallyldimethylammonium chloride), PDDA)/polysaccharide (dextransulfate, DS) coacervate droplets (CK) that adhered to negatively charged proteinosomes via electrostatic interactions (Figure 7b). Proteinase K was initially sequestered in CT coacervates, but the addition of glucose led to gluconic acid release from P, lowering the pH and causing disassembly of the coacervates CT. The released proteinase K was then sequestered by the proteinosome-adherent coacervates CK. Finally, proteinosomes were destroyed by proteinase K, and only CK remained. Expanding on the idea of communication between compartments, Chakraborty et al. demonstrated how a bioluminescent signal triggered prey–predator behavior in GUVs. Renillaluciferase (RLuc) containing GUVs (sender) produced blue light in the presence of coelenterazine, activating iLID and Nano proteins in the outer membranes of both sender and receiver GUVs. The interaction between iLID and Nano under blue light mediated the adhesion between the GUVs. GUV–GUV adhesion allowed transfer of Ca2+ from the sender to the receiver through unblocked α-hemolysin (α-HL) pores. Ca2+ activation of phospholipase A2 (PLA2) in prey GUVs led to phospholipid cleavage and collapse of receiver GUVs. (176) A recent study from the same group showcased bidirectional communication in GUVs through similar chemiluminescence-triggered adhesion, enabling exchange of H2O2 from the sender and Ca2+ from the receiver. Interestingly, GUVs separated when the signaling molecule production ceased. (180) Liu and Zhang et al. designed a three-layer tubular prototissue comprising concentrically arranged agarose hydrogel layers containing GOx, HRP, and CAT containing coacervates as the outer, middle, and inner layers, respectively. Glucose and hydroxyurea were added specifically to the exterior side of the model prototissue. Through inward diffusion, glucose was first processed by GOx containing a hydrogel layer, leading to H2O2 production. HRP in the middle layer converted hydroxyurea in the presence of H2O2 into nitric oxide (NO). Excess H2O2 was then consumed by Cat in the final layer, such that NO became the main output, which was further used to inhibit blood coagulation in samples located within the device’s internal lumen. (184)
Interestingly, enzymes fixed in a microchamber can act as “chemical pumps” in the presence of specific substrates. Density differences between the substrates and the enzyme-catalyzed products give rise to solutal buoyancy, in turn generating convective flows of the enclosed fluids. (185,186) This system can be understood as follows: when the density of the enzyme-catalyzed products exceeds that of the reactants, the fluid becomes denser, sliding down and away from the patch like an outward pump. Conversely, if the product density is lower, the fluid rises up, falls back down due to convective flows, and moves toward the enzyme patch, forming an inward pump. The fluid flow velocity is dependent on the enzymatic reaction rates. These micropumps have been utilized as proof-of-concept delivery vehicles to transport small molecules like insulin in the presence of a glucose stimulus or act as sensors to detect toxins that hamper enzymatic reactions. (185,187) Furthermore, simulations on flexible sheets with two enzyme patches have revealed complex motility behaviors. The first patch produces substrates for the second, while the products from the second patch inhibit the enzymes in the first patch. The spatial separation between these two enzyme patches led to the time delay required for chemical oscillations in the system. The resulting fluid flow induced oscillatory mechanical deformation, displaying diverse motility behaviors. (188) These strategies utilizing ERNs to enable communication between spatially segregated bioreactors are crucial for designing systems with diverse life-like applications.

6. Cell-Free Synthesis

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Cell-free biosynthesis is a rapidly growing field in which complex biochemical pathways are assembled in vitro. The aim of this field is to better understand how such pathways function and how they can be manipulated. The reconstruction of these pathways in vitro also provides a route to the synthesis of valuable organic molecules using environmentally friendly processes and without the need for culturing organisms. Reaction networks carried out in cell-free environments can, in principle, be much better controlled by optimizing the pH, temperature, and enzyme loading. Furthermore, competition with other enzymatic pathways is excluded, and enzymes produced in various organisms can be combined into a single pathway. (190) In this section, we will focus on the biochemical pathways that consist of multiple enzymes. For the two-step enzyme cascades, we refer the reader to the reviews in refs (172) and (191−193) and specifically for enzymatic cofactor regeneration to the reviews in refs (194) and (195). Importantly, only cell-free synthesis of small molecules is covered in this review; cell-free protein synthesis has been the topic of several reviews. (196−198)

6.1. Sugars as Substrates

Bowie’s group demonstrated how complex enzymatic chemical reaction networks can be used to synthesize valuable molecules starting from simple substrates such as glucose or phosphoenolpyruvate (PEP) (Figure 8a). All reactions were performed using purified enzymes in batch conditions. One of the first complex enzymatic reaction pathways included isoprene synthesis, which was synthesized from PEP in a combined glycolysis and mevalonate pathway containing in total 12 enzymes. (199) ADP, NADPH, and coenzyme A (CoA) regeneration cycles were employed to reuse the cofactors and to avoid CoA build up, which was causing inhibition of the forward direction of the mevalonate pathway. After this, the same group showed how a 27-enzyme cell-free system in batch converts glucose into different monoterpenes using glycolysis and mevalonate. (200) The choice for the final enzyme in the pathway, a limonene synthase, pinene synthase, or limonene synthase-N345A mutant, determined which monoterpene (limonene, pinene, or sabinene) was produced. The whole pathway also included a molecular purge valve (Figure 8b). (201) Molecular purge valves maintain cofactor balance in the system without the need to perfectly match stoichiometric cofactor formation and carbon consumption. Once there is a NADPH cofactor buildup, the NADP+-dependent reductase enzyme is starved of oxidized cofactor and the pathway shuts down. In the meantime, the purge valve is turned on and NAD+-dependent reductase and NADH-specific oxidase are activated. In this case, it was used to control the balance of NAD(P)H/NAD(P)+ using three enzymes: glyceraldehyde-3-phosphate dehydrogenase (Gap), mutant glyceraldehyde-3-phosphate dehydrogenase (mGap), and NADH oxidase (NoxE). This cascade regenerates NADPH, purges the excess of NADH, and continues generating carbon building blocks for the glycolysis pathway. It was demonstrated that excluding at least one enzyme of the molecular purge valve reduced limonene production drastically, while a system without the NoxE enzyme did not convert any glucose to limonene. Researchers managed to produce all three terpenes starting from 500 mM glucose over 7 days at high titers.

Figure 8

Figure 8. (a) A diagram of low-cost substrate glucose being converted into pyruvate or acetyl-CoA as an intermediate and then depending on the selected pathway to isoprene, (199) monoterpenes, (200) PHB, (202) or cannabinoids (203) using purified enzymes (in orange) or to n-butanol, (205) mevalonate, (206) or limonene (208) using crude cell lysate (in yellow). (b) The purge valve concept, where the purge valve is “OFF” when there is low NADPH concentration and “ON” at high NADPH concentration. Adapted with permission from ref (201). Copyright 2014 Springer Nature.

Another example of synthetic pathways producing valuable organics includes the so-called PBG (pentose–bifido–glycolysis) cycle in which the final product is polyhydroxybutyrate (PHB), a bioplastic. (202) The whole cycle consists of 20 enzymes from three partial pathways: the pentose phosphate pathway, the bifidobacterium shunt, and the glycolysis pathway. This ERN also contained two purge valves to regulate NAD(P)H concentrations as well as a metabolite salvage pathway which allowed erythrose-4-phosphate to re-enter the cycle. Two new enzymes, a G6PDH and a 6-phosphogluconate dehydrogenase (Gnd) mutant, had to be engineered for the purge valve to favor NAD+ instead of NADP+, as their wild forms do. A batch reaction containing all enzymes was productive for up to 55 h, and only the last enzyme of the cycle─PHB synthase (PhaC)─had to be added again after each 10 h cycle. This was done because the enzyme was covalently linked to the growing end of the PHB product, which meant the enzyme would be removed every time during sampling of the bioplastic out of the reaction vessel to quantitatively measure produced PHB. Valliere et al. constructed an even more complex pathway consisting of 23 enzymes, which was used to synthesize a variety of prenylated molecules, including cannabinoids, from glucose. (203) In their work, a modified glycolysis module was developed which included a purge valve to balance NADPH concentration and carbon flux. This module was connected to acetyl coenzyme A (acetyl-CoA) and mevalonate modules to form geranyl-pyrophosphate (GPP). From there, different substrates and their complementary enzymes were added to yield various prenylated compounds. Performing this pathway using only purified enzymes is also advantageous as GPP is toxic to cells in medium concentrations. (204) The original system containing pyruvate dehydrogenase (PDH) had to be modified as it was found that PDH was inhibited by 1,6-dihydroxynaphthalene (1,6-DHN), which is the preferred substrate for wild-type prenyltransferase (NphB) enzyme. Therefore, a PDH bypass system consisting of two additional enzymes to convert pyruvate via acetyl phosphate to acetyl-CoA was introduced. After it was proved that various prenylated aromatic compounds can be generated using prenyl transferases, the authors focused on optimizing cannabinoid production. The results showed that the production rate of the cannabinoid precursor cannabigerolic acid (CBGA) using the primary system reached a not particularly high titer. However, a new mutant CBGA synthase (M23) was designed to produce CBGA using olivetolic acid (OA) without side products, which helped to reach a higher titer. In combination with in situ product removal using a flow system, a final CBGA titer was increased even more.
All of these examples demonstrated complex cell-free purified enzymatic reaction networks and possibilities for future industrial applications. Obviously, some important aspects of any industrial application include optimization, cost reduction, and yield increase. The authors proposed employing stable enzymes that could be used for longer periods of time in order to reduce costs, introducing cofactor (ATP/NAD(P)H) regeneration cycles to increase yields, (204) recycling enzymes, and introducing cheaper purification methods. (202) Another potential improvement could come from enzyme immobilization, which allows for easier purification and optimization at a cost of potentially reduced enzyme activity upon immobilization.
Instead of using purified enzymes, in some networks it would be desirable to construct ERNs in crude cell lysate. Karim et al. established a cell-free protein synthesis-driven metabolic engineering (CFPS-ME) framework for fast enzyme selection and pathway construction. (205) As a model system, they chose a CoA-dependent n-butanol pathway starting from glucose including 17 steps in vitro. For this, the authors combined cell-free metabolic engineering (CFME), where heterologous enzymes are selectively overexpressed, and cell-free protein synthesis (CFPS). First, it was shown that mixing five crude lysates each of them with different selectively overexpressed enzymes via CFME can activate the whole pathway and produce n-butanol. Next, CFPS was modularly integrated in CFME to speed up the biosynthesis. Hydroxybutyryl-CoA dehydrogenase from Clostridia beijerinckii (Hbd2) lysate was excluded from the mixture of overexpressed enzymes, and instead, the CFPS was performed for Hbd2. As the downstream enzymes could not be activated without their substrates, the pathway would stay inactive until Hbd2 was synthesized. Therefore, first, CFPS of Hbd2 was performed for 3 h, and glucose was added to initiate the n-butanol synthesis pathway. Later, a similar experiment was repeated by excluding every other one of the five n-butanol pathway enzymes from overexpressed enzyme lysate mix and synthesizing them using CFPS for 3 h. After adding glucose, NAD, and coenzyme-A (CoA) to initiate the CFPS–ME pathway reactions, all variants produced n-butanol. This study demonstrated how the CFPS–ME method can be used for fast enzyme screening for ERNs. The same group also demonstrated the synthesis of mevalonate from glucose using enzyme-enriched lysate mixtures (206) and investigated a pathway from mevalonate to limonene without any optimization. (207) This research laid the groundwork for a combined study involving a full pathway from glucose to limonene using enzyme-enriched Escherichia coli (E. coli) lysates requiring in total 20 metabolic steps. (208) To begin with, the endogenous metabolism in E. coli crude cell lysate already consisted of all of the glycolytic enzymes which transformed glucose to acetyl-CoA. This part was then connected to a single extract enriched in three enzymes─acetyl-CoA acetyltransferase (ACAT), hydroxymethylglutaryl-CoA synthase (HMGS), and hydroxymethylglutaryl-CoA reductase (HMGR)─converting acetyl-CoA to mevalonate. Last, it was coupled to a module of six extracts each enriched in a single enzyme using heterologous overexpression in vivo─mevalonate kinase (MK), phosphomevalonate kinase (PMK), pyrophosphomevalonate decarboxylase (PMD), isopentenyl pyrophosphate isomerase (IDI), geranyl diphosphate synthase (GPPS), and limonene synthase (LS). After additional experiments and optimization, the full pathway produced final molecule limonene.
Cell-free biosynthesis points out attractive possibilities of performing enzymatic synthesis and creating complex ERNs without any cell membrane constraints and avoiding cell toxicity. This growing field offers the opportunity of producing valuable compounds using sustainable biosynthesis. In the ethanol industry, commonly accepted thresholds required for industrial production are yields of 90%, productivity of 1 g/L/h, and titers of 40 g/L. (202) Some of the discussed network final product titers are 12.5 ± 0.3, 14.9 ± 0.6, and 15.9 ± 0.4 g/L and yields of 88.3 ± 4.6%, 103.9 ± 8.1%, and 94.5 ± 4.7% for limonene, pinene, and sabinene, respectively. (200) These final product values are close to the threshold values used in the ethanol industry, and it gives high hopes that cell-free biosynthesis will be applied for industrial purposes.

6.2. CO2 as Substrate

In addition to glucose, CO2 is also a versatile starting material for the sustainable synthesis of valuable compounds. Many inorganic and metallic catalysts, like SnO2, TiO2, Cu, and transition metal complexes, have been successfully developed as catalysts to reduce CO2. (209) These inorganic catalysts in general show low selectivity; hence, the products are mainly limited to C1 and C2 compounds, including methanol and ethanol. Nature has evolved several metabolic networks to reduce CO2 into higher carbon compounds. However, many of the key carboxylase enzymes that couple CO2 to organic substrates have low efficiency. (210−212) A new-to-nature carboxylase, glycolyl-CoA carboxylase, was recently developed and used in the successful in vitro implementation of the previously hypothesized tartronyl-CoA (TaCo) pathway, which had been proposed as a direct route for the assimilation of glycolate into central carbon metabolism and expected to outperform all naturally evolved glycolate assimilation routes. (213) In contrast, in vitro construction of artificial CO2 fixation ERNs by freely combining different enzymes from various biological sources is another promising approach. The Erb group assembled the so-called crotonyl–coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle with 17 enzymes from nine different organisms to convert CO2 into malate at a rate of 5 nmol of CO2 per minute per milligram of protein, providing a seventh synthetic alternative to the six naturally evolved CO2 fixation pathways (Figure 9a). (214) This is comparable to the CO2 fixation rate (1–3 nmol of CO2 per minute per milligram of protein) of the Calvin–Benson–Bassham (CBB) cycle, which fixes more than 90% of carbon in nature. (215) Moreover, this CETCH cycle could be solar powered through the incorporation of chloroplast extracts. (216) Subsequently, the output glyoxylate from the CETCH cycle can be applied to produce acetyl-CoA, the central precursor for many natural compounds. Hence, a series of value-added compounds, including monoterpenes, sesquiterpenes, polyketides, and 6-deoxyerythronolide B, has been successfully produced from CO2 in one pot based on the CETCH cycle. (217,218) In addition, a small ERN with four enzymes (pyruvate carboxylase–oxaloacetate acetylhydrolase–acetate-CoA ligase–pyruvate ferredoxin oxidoreductase), named the POAP cycle, was constructed to couple two CO2 into one oxalate at the expense of two ATP and one NAD(P)H, reaching a rate of 8 nmol/min/mg CO2-fixing enzymes (Figure 9b). (219) For a more detailed overview, the enzymatic conversion of CO2 has been discussed in a recent review on the topic. (220)

Figure 9

Figure 9. Synthetic CO2 fixation pathways. (a) CETCH cycle converting CO2 to malate. Adapted with permission from ref (214). Copyright 2016 The American Association for the Advancement of Science. (b) POAP cycle converting CO2 to oxalate. Adapted with permission from ref (219). Copyright 2022 American Chemical Society. (c) Chemoenzymatic ASAS pathway converting CO2 to starch. Adapted with permission from ref (221). Copyright 2021 The American Association for the Advancement of Science. (d) Chemoenzymatic pathway converting CO2 to bioplastic polyhydroxybutyrate (PHB). Adapted with permission from ref (223). Copyright 2023 Royal Society of Chemistry.

6.3. Other Substrates

In addition to using sugar and CO2 as substrates, progress has been made on fabricating ERNs to upgrade sustainable C1 and C2 stocks, like methanol, ethanol, and acetate, to compounds with longer chains and hence higher value. Cai et al. designed and constructed an 11-enzyme ERN to convert C1 methanol to polymeric starch (Figure 9c). (221) Zhou et al. designed several ERNs to convert methanol to ethylene glycol, glycolic acid, and d-erythrose. (222) In addition, the Bioplastic PHB has been produced from methanol and acetate (Figure 9d). (223,224) Furthermore, Liu et al. managed to use ethanol to produce acetyl-CoA and regenerate ATP using the isoprenol production pathway. (225)

7. Future Development

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The ERNs discussed so far exhibit a diverse range of functionalities and emergent properties. Yet, they remain relatively small based on several reactions occurring at once. However, as the sections on control over enzyme reactivity and the construction of elaborate reaction cascades for the synthesis of complex organic molecules have demonstrated, the field is now in a strong position to develop ERNs with much more advanced functionalities. In this section, we will discuss two such possible functions (without the ambition of providing complete overviews of those fields): synthetic cells and molecular computers.

7.1. Toward Building a Synthetic Cell from the Bottom Up

Building a synthetic cell from the bottom up could address the fundamental questions on how life emerged and evolved from nonliving components and at the same time generate a wide range of applications. Any functioning synthetic cell will inevitably require the construction of (minimal) metabolic ERNs for the continuous supply of building blocks and energy to support other cellular processes and also response to external environmental changes. (226) ATP fuels many cellular processes and also serves as a building block for gene transcription and translation. Lee et al. successfully designed and built a photosynthetic artificial organelle consisting of an ATP synthase and two photoconverters in a giant vesicle. This synthetic organelle was capable of harvesting light to produce ATP and to drive endergonic reactions, like pyruvate carboxylase-mediated carbon fixation and actin polymerization into filaments (Figure 10a). (227) Similarly, Berhanu et al. designed a light-harvesting artificial organelle, which was composed of two kinds of membrane proteins─bacteriorhodopsin and F-type ATP synthase. In this work, the organelle, reconstituted inside a GUV together with a cell-free protein synthesis system, generated ATP required in transcription and at the same time powered the synthesis of GTP and translation (Figure 10b). (228) Additionally, Bailoni et al. reconstituted the l-arginine breakdown pathway of three cytosolic enzymes to phosphorylate ADP into ATP to fuel the sustainable formation of phospholipid headgroups in synthetic cells. (229) Blanken et al. managed to encode seven phospholipid-producing enzymes in a synthetic minigenome, which were then expressed within cell-like liposomes. This de novo-constructed ERN can make use of fatty acyl coenzyme A and glycerol-3-phosphate as precursors to produce two phospholipids phosphatidylethanolamine (PE) and phosphatidylglycerol (PG) simultaneously (Figure 10c). The balance of PE and PG is transcriptionally regulated by the activity of specific genes together with a metabolic feedback mechanism. (230) In addition, Partipilo et al. constructed a small ERN with three enzymes in liposomes to control the redox status of NADH and NADPH cofactors. (231)

Figure 10

Figure 10. (a) Schematic of the artificial organelle harvesting light to produce ATP and to drive endergonic reactions. Adapted with permission from ref (227). Copyright 2018 Springer Nature. (b) Schematic of self-constituting protein synthesis in artificial photosynthetic cells. Adapted with permission from ref (228). Copyright 2019 licensed under CC 4.0 Springer Nature. (c) Schematic of the production of phospholipids PE and PG by de novo-synthesized enzymes. Adapted with permission from ref (230). Copyright 2020 licensed under CC 4.0 Springer Nature.

Living cells maintain physicochemical homeostasis (including pH, ionic strength, osmotic pressure) to enable the internal components to function near their optimum. This homeostasis state was successfully achieved inside a cell-like system by Pols et al., who co-reconstituted the arginine-breakdown pathway and an ionic strength-gated ATP-driven osmolyte transporter inside vesicles. The cell-like vesicles shrank under increased medium osmolality, causing the internal ionic strength and pH to increase, leading to the inactivation of enzymes. Once the internal ionic strength reached a critical value, the transporter was activated and glycine betaine was pumped in with the accompaniment of the influx of water into the vesicles. Hence, the volume of vesicles increased, and the internal ionic strength and pH decreased. As a result, homeostasis of the ATP/ADP ratio, internal ionic strength, and pH was achieved. (232) All of these examples clearly demonstrate how enzymatic modules are crucial for mimicking complex metabolic functions of living systems in the field of designing synthetic cells.

7.2. Information Processing and Computation

An emerging direction for the research on and application of ERNs is special-purpose information processing and computation. So far, the development of Boolean gates and logic circuits has seen a long tradition in enzymatic network research and has been extensively reviewed several times (examples are shown in Figure 11a). (233,234) However, the development of systems based on digital paradigms has been slowing down, as the principles of Boolean logic and von Neumann computer architectures are challenging to adapt in biochemical systems, which are intrinsically of a nonbinary and dynamic nature. Instead, the potential application of analog and neuromorphic computation principles in biochemistry is increasingly appreciated. (235) While these developments are still in their infancy, here we focus on recent progress in this direction.

Figure 11

Figure 11. (a) Schematic overview of commonly used ERN designs for two Boolean logic gates (AND and XOR). The computational output is based on an arbitrary threshold for the concentration of a product molecule. (b) A design for an ERN resulting in a multi-input perceptron, investigated in Pandi et al. (238) The design uses so-called transduction reactions to convert a range of different inputs into the same “transducer” substrate. Weights can be set by modifying enzyme concentrations. The final addition results in a roughly sigmoidal response. (c) Design for a chemical neuron, as investigated by Okumura et al. (239) A single neuron is constructed from specific DNA templates (curved lines) enabled by components of the polymerase–exonuclease–nickase (PEN) toolbox (shaded circle). (d) Design for a chemical multilayer perceptron. Individual neurons can be combined by replacing fluorescence-generating reporter strands by input strands for other neurons.

Rather than recreating digital infrastructure directly, recent work on small ERNs has aimed to establish modules and motifs capable of analogue computation. One example of this shows the development of small ERNs that are capable of arithmetic operations. (236) The key insight here is that specific enzymatic interactions, such as cascading pathways, inhibitors, and parallel product conversion, can be interpreted as arithmetic functions if operated in the right parameter regime. Similarly, specific forms of substrate competition between different enzymes can lead to nonlinear Boolean logic as the theoretical study by Genot and co-workers shows. (237) This work essentially presents network motifs that act like a nonlinear and nonadditive switch of the starting substrates. This type of research showcases the intrinsic computational power of ERNs, circumventing the requirements necessary for developing explicit digital circuitry.
Recently, this approach has been expanded to enzymatic systems capable of more generalized types of computation. Inspired by concepts from machine learning and neuromorphic computation, Pandi et al. created in vitro metabolic perceptrons. (238) They employed computational retrosynthesis tools to design enzymatic models that can act as transducers (converting different incoming metabolite signals into one substrate) and nonlinear actuators (to achieve sigmoidal responses) and when combined together can act as analogue adders (to perform a weighted sum of incoming signals, e.g., a perceptron). A schematic overview of this is shown in Figure 11b. These perceptrons are essentially based on ultrasensitivity network motifs to create nonlinear weighted adders (sigmoidal sums) of multiple metabolites but do so in a generalizable and extensible manner. Modifying the transducer reactions changes how the incoming signals are weighted, allowing the perceptron to be controlled and trained similar to an artificial neural network.
A different approach is investigated in the work of Okumura et al., where neural networks capable of nonlinear classification have been created by assembling various components of the polymerase–exonuclease–nickase (PEN) toolbox, as shown in Figure 11c. (239) These enzymes, respectively, polymerize, degrade, and cut DNA strands. Neurons are constructed by supplying specific DNA template strands that convert DNA (or RNA) inputs either into a “reporter” DNA strand or into an “antireporter” DNA strand. The reporter strand can be transformed into a fluorescent signal by another DNA template strand. The antireporter strand polymerizes with the reporter strand, inhibiting its output. Thus, modifying the concentration of reporter and antireporter templates impacts fluorescence, respectively, in a positive or negative way, establishing an effective weight on input conversion. These effective weights can be further modified by incorporating so-called “fake” weight templates, which competitively inhibit the antireporter–reporter interaction. The reporter signal is further amplified by an amplification template, resulting in an ultrasensitive (sigmoidal) response. Finally, the location of the ultrasensitive regime can be adjusted by inclusion of a “bias” template, which operates similarly to the antireporter strand but is not input dependent. Polymerase and nickase drive these DNA reactions, while exonuclease can be used to degrade the fluorescent output over time. The interaction of all enzymes and DNA template strands together establishes a perceptron where all weights and biases can be fine tuned by controlling the concentration of the reporter, antireporter, fake, and bias template strands. By exchanging the reporter templates for strands that can serve as input to another perceptron (Figure 11d), a multilayer neural network was created that could accurately classify input concentration regimes. Importantly, the computational power in this work originates from the highly heterogeneous substrates used in the three-enzyme ERN, in contrast to the previous example of a metabolic network where it originates from the design of the ERN itself.
The above examples show the significant progress that has been made in enzymatic information processing by directly exploiting similarities between enzymatic interactions and analogous principles instead of recreating small Boolean operations and assembling those in a circuit. By either rationally fine tuning the parameter regime in small existing ERNs, by computationally designing larger ERNs to convert many different substrates into the same products, or by intelligently choosing which substrates (and specifically which templates) to exploit using just a small ERN, impressive feats of computation can be achieved. However, further developments have been mostly theoretical up until now, including work showcasing different enzymatic network motifs capable of noise filtering, (240) pattern recognition and pulse counting, (241) and systems that can convert information into work and vice versa. (242) It remains to be seen if these proposals can be realized experimentally, but they highlight the potential for ERNs in future information processing tasks.

8. Conclusion

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We have presented this comprehensive review of the field of in vitro enzymatic reaction networks in the hope to inspire a broad group of researchers to participate in expanding the field. Key design principles, basic emergent properties, and the need for an integrated approach comprising experiments, computational approaches, and analytical methods have been highlighted. Several key challenges for future research were identified: there are currently no “blueprints” for the design of ERNs with functions that go beyond “simple” temporal behavior. For example: we know how to create oscillatory networks, but we cannot design networks that design with a specific frequency and amplitude. Most ERNs reported to date have relatively simple topologies. To significantly expand current designs, we need to establish new strategies for the reverse design of functional ERNs and establish high-throughput methods for the rapid evaluation of networks consisting of multiple feedback loops. Instead of current “trial-and-error” experiments to optimize network properties, iterative “design–build–test–learn” cycles need to be established, and we are hopeful that advances in machine learning and artificial intelligence could be leveraged to identify novel network topologies with hitherto “undesignable” properties. We note that many experimental designs (especially if they are aimed at constructing materials with life-like properties) can only be realized within a relatively narrow scope of kinetic values. Simulations can significantly accelerate discoveries of the most relevant experimental regime, but this will need to be coupled to methods to control or expand the kinetic parameters of enzymes. Although current networks are incomparable in complexity to ERNs found in extant life, our overview on possibilities to control enzymatic activity together with the many examples of feedback loops gives us confidence significantly more elaborate reaction networks will be within reach in the next decade. Indeed, the successful attempts to construct complex reaction cascades and cycles to synthesize complex organic molecules are testament to the potential of more complex ERNs.
We foresee many new developments in the construction of life-like materials, where properties such as homeostasis, sensing the environment, and motility are all carried out by coupled enzymatic reaction networks. The design of intelligent materials by incorporating ERNs, which are able to learn from their environment to adopt desired features, could open up innovative avenues in material science. In section 4, we have explored the influence of external stimuli to regulate ERN activity, serving as physical learning rules for enhanced task performance. Interestingly, these materials potentially can exhibit task adaptiveness through training with different stimuli. (243) Another important direction for designing life-like materials would be to incorporate some type of fuel regeneration and methods to maintain such materials out of equilibrium as many of the properties associated with living systems (motility, homeostasis, regeneration) require dissipative systems and continuous input of energy. In section 5, we have introduced different strategies like energy production, compartmentalization, and communication using ERNs, which facilitate crosstalk between multiscale processes to design truly autonomous systems. (244) Such materials could then find applications as delivery vehicles in medicine, in soft robotics, or as interfaces between living systems and electronic systems. In section 6, we demonstrated the progress in cell-free biosynthesis, showcasing the in vitro design of metabolic modules for unraveling complex metabolic machinery and producing value-added chemicals. Looking ahead, we anticipate the development of ever more complex ERNs for designing novel catalytic networks with promising industrial applications. Significantly, this can augment the design of novel synthetic cells as chemical factories. Finally, enzymes are key in processing information in biology, but the potential of synthetic ERNs to achieve similar capabilities remains largely unrealized. (245) Building on the multifarious uses of enzymes in complex (and not so complex) reaction networks, there is much potential for designing novel computing paradigms in which enzymes are used to process multimodal and multiplexed information at the molecular level.

Author Information

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  • Corresponding Author
  • Authors
    • Souvik Ghosh - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The NetherlandsOrcidhttps://orcid.org/0009-0007-3956-6588
    • Mathieu G. Baltussen - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The NetherlandsOrcidhttps://orcid.org/0000-0001-7779-8899
    • Nikita M. Ivanov - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
    • Rianne Haije - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
    • Miglė Jakštaitė - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
    • Tao Zhou - Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The NetherlandsOrcidhttps://orcid.org/0000-0003-4520-9679
  • Author Contributions

    CRediT: Souvik Ghosh investigation, visualization, project administration, supervision, writing─original draft, writing─review and editing; Mathieu G. Baltussen investigation, visualization, writing─original draft, writing─review and editing; Nikita M. Ivanov investigation, visualization, writing─original draft, writing─review and editing; Rianne Haije investigation, visualization, writing─original draft, writing─review and editing; Miglė Jakštaitė investigation, visualization, writing─original draft, writing─review and editing; Tao Zhou investigation, visualization, writing─original draft, writing─review and editing; Wilhelm T. S. Huck conceptualization, investigation, visualization, funding acquisition, project administration, writing─original draft, supervision, writing─review and editing.

  • Notes
    The authors declare no competing financial interest.

Biographies

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Souvik Ghosh received his B.Sc.–M.Sc. dual degree from IISER Kolkata, India (2021). For his Master’s research, he designed enzyme-powered nanomotors based on cross-β-amyloid nanotubes in the system chemistry lab at IISER Kolkata. In the same year, he started his Ph.D. work on the design and modulation of complex enzymatic reaction networks for information processing under the supervision of Wilhelm T. S. Huck at Radboud University. His research interests include flow chemistry, photochemistry, enzymatic reaction networks, enzyme immobilization, and reservoir computation.

Mathieu G. Baltussen earned a double B.Sc. degree in Chemistry and Physics & Astronomy at Radboud University (2018) and a double M.Sc. honors degree in Nanomaterials Science and Experimental Physics at Utrecht University (2021). He is currently pursuing his Ph.D. degree under the supervision of Wilhelm T. S. Huck at Radboud University. His research interests focus on information processing and computation in (bio)chemical networks and developing techniques to use chemical reaction networks as computational systems.

Nikita M. Ivanov obtained his M.Sc. degree in Bioorganic Chemistry in 2019 from Lomonosov Moscow State University, Russia, with a diploma project on DNA aptamers to influenza hemagglutinin working with Alexei M. Kopylov. During these studies he also worked on the synthesis of perylene derivatives for nucleoside labeling and as antivirals in the laboratory of Vladimir A. Korshun at the Institute of Bioorganic Chemistry RAS, Moscow. In 2020, he started his Ph.D. work under supervision of Wilhelm T. S. Huck at Radboud University to focus on enzymatic reaction networks for molecular information processing and the physical chemistry of enzymes and photoinhibitors.

Rianne Haije received her B.Sc. and M.Sc. degrees in Chemistry from Radboud University. During this time, she focussed on different projects that included the development of an oscillating urease-based network, the synthesis of sialic acid-based inhibitors, and the development of tools to control enzymatic activity by using light. The goal of her Ph.D. work is to control dynamic reaction networks by using photoswitchable molecules in combination with light.

Miglė Jakštaitė obtained her B.Sc. degree in Chemistry from Vilnius University, Lithuania, in 2017. For her final thesis project, she investigated the synthesis of anionic molecular brushes containing glucuronate under the supervision of Tatjana Kavleiskaja. Later, she obtained her M.Sc. degree in Chemistry from Radboud University in 2019, where her main internship project focused on cell-free enzymatic reaction networks under the supervision of Wilhelm T. S. Huck. She rejoined the group of Wilhelm T. S. Huck to start her Ph.D. studies in 2019. Her research interest focuses on cell-free biosynthesis of valuable compounds in cascade reactions using immobilized enzymes.

Tao Zhou received his Ph.D. degree in Biochemistry at ETH Zurich, Switzerland, in 2020. During his Ph.D. work, he developed a biomimetic soft material-lipidic mesophases for enzymes in vitro to fulfill their full potentials in biocatalysis from room to cryogenic temperatures. Then, he conducted his postdoctoral research on complex enzymatic cascades at Radboud University in the laboratory of Wilhelm T. S. Huck. His research interests include biosynthesis, enzymatic reaction networks, enzyme immobilization, kinetic modeling, and flow chemistry.

Wilhelm T. S. Huck is a professor of Physical Organic Chemistry at Radboud University. After postdoctoral research at Harvard University, he took up a position in the Department of Chemistry at the University of Cambridge, where he became Director of the Melville Laboratory for Polymer Synthesis (2004) and Full Professor of Macromolecular Chemistry (2007). In 2010, he moved to Radboud University, where he developed a new line of research focusing on complex chemical systems. His group uses microfluidics, mathematical modeling, and, increasingly, AI and robotics to study chemical reaction networks.

Acknowledgments

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This work was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (ERC Adv. Grant Life-Inspired, Grant Agreement no. 833466), the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 862081 (CLASSY)), and a Spinoza Grant of The Netherlands Organisation for Scientific Research (NWO).

Abbreviations

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1,6-DHN

1,6-dihydroxynaphthalene

α-HL

α-hemolysin

ABTS

2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)

AcAc

acetylacetone

ACAT

acetyl-CoA acetyltransferase

acetyl-CoA

acetyl coenzyme A

AchE

acetylcholine esterase

ADH

alcohol dehydrogenase

ADP

adenosine 5/-diphosphate

AMG

amyloglucosidase

AMP

adenosine 5′-monophosphate

Ap

aminopeptidase

ATP

adenosine 5′-triphosphate

Cat

catalase

CBGA

cannabigerolic acid

CETCH

crotonyl-coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA

CFPS-ME

cell-free protein synthesis-driven metabolic engineering

CFPS

cell-free protein synthesis

CFME

cell-free metabolic engineering

Cg

chymotrypsinogen

CoA

coenzyme A

COx

choline oxidase

Cr

chymotrypsin

CytC

cytochrome C

DASAs

donor–acceptor Stenhouse adducts

DNA

deoxyribonucleic acid

DS

dextransulfate

E. coli

Escherichia coli

Els

elastase

ERNs

enzymatic reaction networks

Est

esterase

FAD

flavin adenine dinucleotide

FADH2

flavin adenine dinucleotide (hydroquinone form)

G6PDH

glucose-6-phosphate dehydrogenase

Gap

glyceraldehyde-3-phosphate dehydrogenase

Gnd

6-phosphogluconate dehydrogenase

GOx

glucose oxidase

GPb

glycogen phosphorylase b

GPP

geranyl-pyrophosphate

GPPS

geranyl diphosphate synthase

GUVs

giant unilamellar vesicles

Hbd2

hydroxybutyryl-CoA dehydrogenase

HMGR

hydroxymethylglutaryl-CoA reductase

HMGS

hydroxymethylglutaryl-CoA synthase

Hbd

hydroxybutyryl-CoA dehydrogenase

HK

hexokinase

HRP

horseradish peroxidase

IDI

isopentenyl pyrophosphate isomerase

LDH

l-lactate dehydrogenase

LO

l-lactate oxidase

LS

limonene synthase

M23

mutant CBGA synthase

mGap

mutant glyceraldehyde-3-phosphate dehydrogenase

MK

mevalonate kinase

MM

Michaelis–Menten

MMP

matrix-metalloproteinases

NAD

nicotinamide adenine dinucleotide

NADP

nicotinamide adenine dinucleotide phosphate

NO

nitric oxide

NOxE

NADH oxidase

NphB

wild-type prenyltransferase

OA

olivetolic acid

PAAM

polyacrylamide

PBG

pentose–bifido–glycolysis

PDDA

poly(diallyldimethylammonium chloride)

PDH

pyruvate dehydrogenase

PE

phosphatidylethanolamine

PEG

polyethylene glycol

PEGMA

poly(ethylene glycol) methacrylate

PEN

polymerase–exonuclease–nickase

PEP

phosphoenolpyruvate

PG

phosphatidylglycerol

PGLA

poly(lactic-co-glycolic acid)

PhaC

PHB synthase

PHB

polyhydroxybutyrate

PKA

protein kinase A

PLA2

phospholipase A2

PMD

pyrophosphomevalonate decarboxylase

PMG

phosphoglucomutase

PMK

phosphomevalonate kinase

PO

peroxidase–oxidase

PP1

protein phosphatase-1

STI

soybean trypsin inhibitor

RLuc

renillaluciferase

RNA

ribonucleic acid

SOx

sarcosine oxidase

TaCo

tartronyl-CoA

Tg

trypsinogen

Tr

trypsin

UOx

urate oxidase

Ur

urease

UV

ultraviolet

References

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This article references 247 other publications.

  1. 1
    Colin, R.; Sourjik, V. Emergent properties of bacterial chemotaxis pathway. Curr. Opin. Microbiol. 2017, 39, 2433,  DOI: 10.1016/j.mib.2017.07.004
  2. 2
    Typas, A.; Sourjik, V. Bacterial protein networks: properties and functions. Nat. Rev. Microbiol. 2015, 13 (9), 559572,  DOI: 10.1038/nrmicro3508
  3. 3
    Hunter, T. Protein Kinases and Phosphatases: The yin and yang of protein phosphorylation andsignaling. Cell 1995, 80, 225236,  DOI: 10.1016/0092-8674(95)90405-0
  4. 4
    Junttila, M. R.; Li, S. P.; Westermarck, J. Phosphatase-mediated crosstalk between MAPK signaling pathways in the regulation of cell survival. FASEB J. 2008, 22, 954965,  DOI: 10.1096/fj.06-7859rev
  5. 5
    Saravia, J.; Raynor, J. L.; Chapman, N. M.; Lim, S. A.; Chi, H. Signaling networks in immunometabolism. Cell Res. 2020, 30 (4), 328342,  DOI: 10.1038/s41422-020-0301-1
  6. 6
    Fink, T.; Lonzaric, J.; Praznik, A.; Plaper, T.; Merljak, E.; Leben, K.; Jerala, N.; Lebar, T.; Strmsek, Z.; Lapenta, F. Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat. Chem. Biol. 2019, 15 (2), 115122,  DOI: 10.1038/s41589-018-0181-6
  7. 7
    Feng, Z.; Wang, H.; Wang, F.; Oh, Y.; Berciu, C.; Cui, Q.; Egelman, E. H.; Xu, B. Artificial intracellular filaments. Cell Rep. Phys. Sci. 2020, 1 (7), 100085,  DOI: 10.1016/j.xcrp.2020.100085
  8. 8
    Kohyama, S.; Merino-Salomon, A.; Schwille, P. In vitro assembly, positioning and contraction of a division ring in minimal cells. Nat. Commun. 2022, 13 (1), 6098,  DOI: 10.1038/s41467-022-33679-x
  9. 9
    Fisher, A. K.; Freedman, B. G.; Bevan, D. R.; Senger, R. S. A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput. Struct. Biotechnol. J. 2014, 11 (18), 9199,  DOI: 10.1016/j.csbj.2014.08.010
  10. 10
    Hirschi, S.; Ward, T. R.; Meier, W. P.; Muller, D. J.; Fotiadis, D. Synthetic biology: bottom-up assembly of molecular systems. Chem. Rev. 2022, 122 (21), 1629416328,  DOI: 10.1021/acs.chemrev.2c00339
  11. 11
    Boguñá, M.; Bonamassa, I.; De Domenico, M.; Havlin, S.; Krioukov, D.; Serrano, M. Á. Network geometry. Nat. Rev. Phys. 2021, 3 (2), 114135,  DOI: 10.1038/s42254-020-00264-4
  12. 12
    Ravasz, E.; Somera, A. L.; Mongru, D. A.; Oltvai, Z. N.; Barabási, A.-L. Hierarchical organization of modularity in metabolic networks. Science 2002, 297, 15511555,  DOI: 10.1126/science.1073374
  13. 13
    Barabási, A.-L.; Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 2004, 5 (2), 101113,  DOI: 10.1038/nrg1272
  14. 14
    Serrano, M. A.; Boguñá, M.; Sagués, F. Uncovering the hidden geometry behind metabolic networks. Mol. Biosyst. 2012, 8 (3), 843850,  DOI: 10.1039/c2mb05306c
  15. 15
    Kim, H.; Smith, H. B.; Mathis, C.; Raymond, J.; Walker, S. Universal scaling across biochemical networks on Earth. Sci. Adv. 2019, 5, eaau0149,  DOI: 10.1126/sciadv.aau0149
  16. 16
    Gagler, D. C.; Karas, B.; Kempes, C. P.; Malloy, J.; Mierzejewski, V.; Goldman, A. D.; Kim, H.; Walker, S. I. Scaling laws in enzyme function reveal a new kind of biochemical universality. Proc. Natl. Acad. Sci. U. S. A. 2022, 119 (9), e2106655119,  DOI: 10.1073/pnas.2106655119
  17. 17
    Guell, O.; Sagues, F.; Serrano, M. A. Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis. PLoS Comput. Biol. 2014, 10 (5), e1003637,  DOI: 10.1371/journal.pcbi.1003637
  18. 18
    Sambamoorthy, G.; Raman, K. Understanding the evolution of functional redundancy in metabolic networks. Bioinformatics 2018, 34 (17), i981i987,  DOI: 10.1093/bioinformatics/bty604
  19. 19
    Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 2007, 8, 450461,  DOI: 10.1038/nrg2102
  20. 20
    Goldbeter, A.; Koshland, D. E. An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl. Acad. Sci. U. S. A. 1981, 78, 68406844,  DOI: 10.1073/pnas.78.11.6840
  21. 21
    Koshland, D. E., Jr.; Goldbeter, A.; Stock, J. B. Amplification and adaptation in regulatory and sensory systems. Science 1982, 217, 220225,  DOI: 10.1126/science.7089556
  22. 22
    Barkai, N.; Leibler, S. Robustness in simple biochemical networks. Nature 1997, 387, 913917,  DOI: 10.1038/43199
  23. 23
    Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U. Network motifs: simple building blocks of complex networks. Science. 2002, 298, 824827,  DOI: 10.1126/science.298.5594.824
  24. 24
    Tyson, J. J.; Novak, B. Functional motifs in biochemical reaction networks. Annu. Rev. Phys. Chem. 2010, 61, 219240,  DOI: 10.1146/annurev.physchem.012809.103457
  25. 25
    Novak, B.; Tyson, J. J. Design principles of biochemical oscillators. Nat. Rev. Mol. Cell Biol. 2008, 9 (12), 981991,  DOI: 10.1038/nrm2530
  26. 26
    Araujo, R. P.; Liotta, L. A. Universal structures for adaptation in biochemical reaction networks. Nat. Commun. 2023, 14 (1), 2251,  DOI: 10.1038/s41467-023-38011-9
  27. 27
    Shellman, E. R.; Burant, C. F.; Schnell, S. Network motifs provide signatures that characterize metabolism. Mol. Biosyst. 2013, 9 (3), 352360,  DOI: 10.1039/c2mb25346a
  28. 28
    Beber, M. E.; Fretter, C.; Jain, S.; Sonnenschein, N.; Muller-Hannemann, M.; Hutt, M. T. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks. J. R. Soc. Interface 2012, 9 (77), 34263435,  DOI: 10.1098/rsif.2012.0490
  29. 29
    Piephoff, D. E.; Wu, J.; Cao, J. Conformational nonequilibrium enzyme kinetics: generalized Michaelis-Menten equation. J. Phys. Chem. Lett. 2017, 8 (15), 36193623,  DOI: 10.1021/acs.jpclett.7b01210
  30. 30
    Rohwer, J. M.; Hanekom, A. J.; Crous, C.; Snoep, J. L.; Hofmeyr, J. H. Evaluation of a simplified generic bi-substrate rate equation for computational systems biology. Syst. Biol. (Stevenage) 2006, 153 (5), 33841,  DOI: 10.1049/ip-syb:20060026
  31. 31
    Transtrum, M. K.; Machta, B. B.; Brown, K. S.; Daniels, B. C.; Myers, C. R.; Sethna, J. P. Perspective: sloppiness and emergent theories in physics, biology, and beyond. J. Chem. Phys. 2015, 143 (1), 010901  DOI: 10.1063/1.4923066
  32. 32
    Gutenkunst, R. N.; Waterfall, J. J.; Casey, F. P.; Brown, K. S.; Myers, C. R.; Sethna, J. P. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 2007, 3 (10), e189,  DOI: 10.1371/journal.pcbi.0030189
  33. 33
    Transtrum, M. K.; Qiu, P. Model reduction by manifold boundaries. Phys. Rev. Lett. 2014, 113 (9), 098701  DOI: 10.1103/PhysRevLett.113.098701
  34. 34
    van de Schoot, R.; Depaoli, S.; King, R.; Kramer, B.; Märtens, K.; Tadesse, M. G.; Vannucci, M.; Gelman, A.; Veen, D.; Willemsen, J.; Yau, C. Bayesian statistics and modelling. Nat. Rev. Methods Primers 2021, 1, 16,  DOI: 10.1038/s43586-020-00001-2
  35. 35
    Choi, B.; Rempala, G. A.; Kim, J. K. Beyond the Michaelis-Menten equation: accurate and efficient estimation of enzyme kinetic parameters. Sci. Rep. 2017, 7 (1), 17018,  DOI: 10.1038/s41598-017-17072-z
  36. 36
    Linden, N. J.; Kramer, B.; Rangamani, P. Bayesian parameter estimation for dynamical models in systems biology. PLoS Comput. Biol. 2022, 18 (10), e1010651,  DOI: 10.1371/journal.pcbi.1010651
  37. 37
    Baltussen, M. G.; van de Wiel, J.; Fernandez Regueiro, C. L.; Jakstaite, M.; Huck, W. T. S. A Bayesian Approach to Extracting kinetic information from artificial enzymatic networks. Anal. Chem. 2022, 94 (20), 73117318,  DOI: 10.1021/acs.analchem.2c00659
  38. 38
    Gábor, A.; Villaverde, A. F.; Banga, J. R. Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC Syst. Biol. 2017, 11 (1), 54,  DOI: 10.1186/s12918-017-0428-y
  39. 39
    Villaverde, A. F.; Evans, N. D.; Chappell, M. J.; Banga, J. R. Input-dependent structural identifiability of nonlinear systems. IEEE Control Systems Letters 2019, 3 (2), 272277,  DOI: 10.1109/LCSYS.2018.2868608
  40. 40
    van Sluijs, B.; Maas, R. J. M.; van der Linden, A. J.; de Greef, T. F. A.; Huck, W. T. S. A microfluidic optimal experimental design platform for forward design of cell-free genetic networks. Nat. Commun. 2022, 13 (1), 3626,  DOI: 10.1038/s41467-022-31306-3
  41. 41
    Hold, C.; Billerbeck, S.; Panke, S. Forward design of a complex enzyme cascade reaction. Nat. Commun. 2016, 7, 12971,  DOI: 10.1038/ncomms12971
  42. 42
    Wong, A. S. Y.; Huck, W. T. S. Grip on complexity in chemical reaction networks. Beilstein J. Org. Chem. 2017, 13, 14861497,  DOI: 10.3762/bjoc.13.147
  43. 43
    Hanopolskyi, A. I.; Smaliak, V. A.; Novichkov, A. I.; Semenov, S. N. Autocatalysis: Kinetics, mechanisms and design. ChemSystemsChem. 2021, 3 (1), e2000026,  DOI: 10.1002/syst.202000026
  44. 44
    Bánsági, T.; Taylor, A. F. Exploitation of feedback in enzyme-catalysed reactions. Isr. J. Chem. 2018, 58 (6–7), 706713,  DOI: 10.1002/ijch.201700141
  45. 45
    Plasson, R.; Brandenburg, A.; Jullien, L.; Bersini, H. Autocatalyses. J. Phys. Chem. A 2011, 115 (28), 80738085,  DOI: 10.1021/jp110079p
  46. 46
    Budroni, M. A.; Rossi, F.; Rongy, L. From transport phenomena to systems chemistry: chemohydrodynamic oscillations in A+B⃗C systems. ChemSystemsChem. 2022, 4 (1), e202100023,  DOI: 10.1002/syst.202100023
  47. 47
    Menon, G.; Krishnan, J. Spatial localisation meets biomolecular networks. Nat. Commun. 2021, 12 (1), 5357,  DOI: 10.1038/s41467-021-24760-y
  48. 48
    Sharma, C.; Maity, I.; Walther, A. pH-feedback systems to program autonomous self-assembly and material lifecycles. Chem. Commun. 2023, 59, 11251144,  DOI: 10.1039/D2CC06402B
  49. 49
    Dúzs, B.; Molnár, I.; Lagzi, I.; Szalai, I. Reaction–diffusion dynamics of pH oscillators in oscillatory forced open spatial reactors. ACS Omega 2021, 6 (50), 3436734374,  DOI: 10.1021/acsomega.1c04269
  50. 50
    Duzs, B.; Lagzi, I.; Szalai, I. Functional rhythmic chemical systems governed by pH-driven kinetic feedback. ChemSystemsChem 2023, 5, e202200032,  DOI: 10.1002/syst.202200032
  51. 51
    Bubanja, I. N.; Bánsági, T.; Taylor, A. F. Kinetics of the urea–urease clock reaction with urease immobilized in hydrogel beads. Reac. Kinet. Mech. Catal. 2018, 123 (1), 177185,  DOI: 10.1007/s11144-017-1296-6
  52. 52
    Miele, Y.; Jones, S. J.; Rossi, F.; Beales, P. A.; Taylor, A. F. Collective behavior of urease pH clocks in nano- and microvesicles controlled by fast ammonia transport. J. Phys. Chem. Lett. 2022, 13 (8), 19791984,  DOI: 10.1021/acs.jpclett.2c00069
  53. 53
    Markovic, V. M.; Bánsági, T., Jr.; McKenzie, D.; Mai, A.; Pojman, J. A.; Taylor, A. F. Influence of reaction-induced convection on quorum sensing in enzyme-loaded agarose beads. Chaos 2019, 29 (3), 033130  DOI: 10.1063/1.5089295
  54. 54
    Muzika, F.; RuZicka, M.; Schreiberova, L.; Schreiber, I. Oscillations of pH in the urea-urease system in a membrane reactor. Phys. Chem. Chem. Phys. 2019, 21 (17), 86198622,  DOI: 10.1039/C9CP00630C
  55. 55
    Muzika, F.; Bánsági, T., Jr.; Schreiber, I.; Schreiberova, L.; Taylor, A. F. A bistable switch in pH in urease-loaded alginate beads. Chem. Commun. 2014, 50 (76), 1110711109,  DOI: 10.1039/C4CC03936J
  56. 56
    Hu, G.; Pojman, J. A.; Scott, S. K.; Wrobel, M. M.; Taylor, A. F. Base-catalyzed feedback in the urea-urease reaction. J. Phys. Chem. B 2010, 114 (44), 1405914063,  DOI: 10.1021/jp106532d
  57. 57
    Straube, A. V.; Winkelmann, S.; Schütte, C.; Höfling, F. Stochastic pH oscillations in a model of the urea-urease reaction confined to lipid vesicles. J. Phys. Chem. Lett. 2021, 12 (40), 98889893,  DOI: 10.1021/acs.jpclett.1c03016
  58. 58
    Bánsági, T.; Taylor, A. F. Switches induced by quorum sensing in a model of enzyme-loaded microparticles. J. R. Soc. Interface. 2018, 15, 20170945,  DOI: 10.1098/rsif.2017.0945
  59. 59
    Heinen, L.; Heuser, T.; Steinschulte, A.; Walther, A. Antagonistic enzymes in a biocatalytic pH feedback system program autonomous DNA hydrogel life cycles. Nano Lett. 2017, 17 (8), 49894995,  DOI: 10.1021/acs.nanolett.7b02165
  60. 60
    Fan, X.; Walther, A. Autonomous transient pH flips shaped by layered compartmentalization of antagonistic enzymatic reactions. Angew. Chem., Int. Ed. 2021, 60, 36193624,  DOI: 10.1002/anie.202009542
  61. 61
    Wang, X.; Moreno, S.; Boye, S.; Wen, P.; Zhang, K.; Formanek, P.; Lederer, A.; Voit, B.; Appelhans, D. Feedback-induced and oscillating pH regulation of a binary enzyme–polymersomes. System. Chem. Mater. 2021, 33 (17), 66926700,  DOI: 10.1021/acs.chemmater.1c00897
  62. 62
    Dúzs, B.; Lagzi, I.; Szalai, I. Functional rhythmic chemical systems governed by pH-Driven kinetic feedback. ChemSystemsChem 2023, 5 (2), e202200032,  DOI: 10.1002/syst.202200032
  63. 63
    Jee, E.; Bánsági, T., Jr.; Taylor, A. F.; Pojman, J. A. Temporal control of gelation and polymerization fronts driven by an autocatalytic enzyme reaction. Angew. Chem., Int. Ed. 2016, 55 (6), 21272131,  DOI: 10.1002/anie.201510604
  64. 64
    Mai, A. Q.; Bánsági, T., Jr.; Taylor, A. F.; Pojman, J. A., Sr. Reaction-diffusion hydrogels from urease enzyme particles for patterned coatings. Commun. Chem. 2021, 4 (1), 101,  DOI: 10.1038/s42004-021-00538-7
  65. 65
    Maity, I.; Sharma, C.; Lossada, F.; Walther, A. Feedback and communication in active hydrogel spheres with pH fronts: facile approaches to grow soft hydrogel structures. Angew. Chem., Int. Ed. 2021, 60 (41), 2253722546,  DOI: 10.1002/anie.202109735
  66. 66
    Rauner, N.; Buenger, L.; Schuller, S.; Tiller, J. C. Post-polymerization of urease-induced calcified, polymer hydrogels. Macromol. Rapid Commun. 2015, 36 (2), 224230,  DOI: 10.1002/marc.201400426
  67. 67
    Semenov, S. N.; Wong, A. S.; van der Made, R. M.; Postma, S. G.; Groen, J.; van Roekel, H. W.; de Greef, T. F.; Huck, W. T. Rational design of functional and tunable oscillating enzymatic networks. Nat. Chem. 2015, 7 (2), 160165,  DOI: 10.1038/nchem.2142
  68. 68
    Maguire, O. R.; Wong, A. S. Y.; Westerdiep, J. H.; Huck, W. T. S. Early warning signals in chemical reaction networks. Chem. Commun. 2020, 56 (26), 37253728,  DOI: 10.1039/D0CC01010C
  69. 69
    Maguire, O. R.; Wong, A. S. Y.; Baltussen, M. G.; van Duppen, P.; Pogodaev, A. A.; Huck, W. T. S. Dynamic environments as a tool to preserve desired output in a chemical reaction network. Chem. Eur. J. 2020, 26, 16761682,  DOI: 10.1002/chem.201904725
  70. 70
    Helwig, B.; van Sluijs, B.; Pogodaev, A. A.; Postma, S. G. J.; Huck, W. T. S. Bottom-up construction of an adaptive enzymatic reaction network. Angew. Chem., Int. Ed. 2018, 57 (43), 1406514069,  DOI: 10.1002/anie.201806944
  71. 71
    Semenov, S. N.; Markvoort, A. J.; de Greef, T. F.; Huck, W. T. Threshold sensing through a synthetic enzymatic reaction-diffusion network. Angew. Chem., Int. Ed. 2014, 53 (31), 80668069,  DOI: 10.1002/anie.201402327
  72. 72
    Kriukov, D. V.; Koyuncu, A. H.; Wong, A. S. Y. History dependence in a chemical reaction network enables dynamic switching. Small 2022, 18 (16), 2107523,  DOI: 10.1002/smll.202107523
  73. 73
    Pogodaev, A. A.; Fernández Regueiro, C. L.; Jakštaitė, M.; Hollander, M. J.; Huck, W. T. S. Modular design of small enzymatic reaction networks based on reversible and cleavable inhibitors. Angew. Chem., Int. Ed. 2019, 58 (41), 1453914543,  DOI: 10.1002/anie.201907995
  74. 74
    Yamazaki, I.; Yokota, K.; Nakajima, R. Oscillatory oxidations of reduced pyridine nucleotide by peroxidase. Biochem. Biophys. Res. Commun. 1965, 21 (6), 5826,  DOI: 10.1016/0006-291X(65)90525-5
  75. 75
    Folke Olsen, L. Complex dynamics in an unexplored simple model of the peroxidase-oxidase reaction. Chaos 2023, 33 (2), 023102  DOI: 10.1063/5.0129095
  76. 76
    Olsen, L. F.; Lunding, A. Chaos in the peroxidase-oxidase oscillator. Chaos 2021, 31 (1), 013119  DOI: 10.1063/5.0022251
  77. 77
    Scheeline, A.; Olson, D. L.; Williksen, E. P.; Horras, G. A.; Klein, M. L.; Larter, R. The peroxidase–oxidase oscillator and its constituent chemistries. Chem. Rev. 1997, 97 (3), 739756,  DOI: 10.1021/cr960081a
  78. 78
    Zhang, Y.; Tsitkov, S.; Hess, H. Complex dynamics in a two-enzyme reaction network with substrate competition. Nat. Catal. 2018, 1 (4), 276281,  DOI: 10.1038/s41929-018-0053-1
  79. 79
    Gyevi-Nagy, L.; Lantos, E.; Gehér-Herczegh, T.; Tóth, A.; Bagyinka, C.; Horváth, D. Reaction fronts of the autocatalytic hydrogenase reaction. J. Chem. Phys. 2018, 148 (16), 165103,  DOI: 10.1063/1.5022359
  80. 80
    Bagyinka, C.; Pankotai-Bodó, G.; Branca, R. M. M.; Debreczeny, M. Oscillating hydrogenase reaction. Int. J. Hydrogen Energy 2014, 39 (32), 1855118555,  DOI: 10.1016/j.ijhydene.2014.02.015
  81. 81
    Claaßen, C.; Gerlach, T.; Rother, D. Stimulus-responsive regulation of enzyme activity for one-step and multi-step syntheses. Adv. Synth. Catal. 2019, 361 (11), 23872401,  DOI: 10.1002/adsc.201900169
  82. 82
    Chen, Z.; Zhao, Y.; Liu, Y. Advanced strategies of enzyme activity regulation for biomedical applications. ChemBioChem 2022, 23 (21), e202200358,  DOI: 10.1002/cbic.202200358
  83. 83
    Hoorens, M. W. H.; Szymanski, W. Reversible, spatial and temporal control over protein activity using light. Trends Biochem. Sci. 2018, 43 (8), 567575,  DOI: 10.1016/j.tibs.2018.05.004
  84. 84
    Kneuttinger, A. C. A guide to designing photocontrol in proteins: methods, strategies and applications. Biol. Chem. 2022, 403 (5–6), 573613,  DOI: 10.1515/hsz-2021-0417
  85. 85
    Volarić, J.; Szymanski, W.; Simeth, N. A.; Feringa, B. L. Molecular photoswitches in aqueous environments. Chem. Soc. Rev. 2021, 50 (22), 1237712449,  DOI: 10.1039/D0CS00547A
  86. 86
    Pogodaev, A. A.; Lap, T. T.; Huck, W. T. S. The dynamics of an oscillating enzymatic reaction network is crucially determined by side reactions. ChemSystemsChem 2021, 3, e2000033,  DOI: 10.1002/syst.202000033
  87. 87
    Crespi, S.; Simeth, N. A.; König, B. Heteroaryl azo dyes as molecular photoswitches. Nat. Rev. Chem. 2019, 3, 133146,  DOI: 10.1038/s41570-019-0074-6
  88. 88
    Teders, M.; Pogodaev, A. A.; Bojanov, G.; Huck, W. T. S. Reversible photoswitchable inhibitors generate ultrasensitivity in out-of-equilibrium enzymatic reactions. J. Am. Chem. Soc. 2021, 143 (15), 57095716,  DOI: 10.1021/jacs.0c12956
  89. 89
    Teders, M.; Murray, N. R.; Huck, W. T. S. Reversible photoswitchable inhibitors enable wavelength-selective regulation of out-of-equilibrium bi-enzymatic systems. ChemSystemsChem 2021, 3 (6), e2100020,  DOI: 10.1002/syst.202100020
  90. 90
    Li, K.; Liu, M. D.; Huang, Q. X.; Liu, C. J.; Zhang, X. Z. Nanoplatforms with donor-acceptor Stenhouse adduct molecular switch for enzymatic reactions remotely controlled with near-infrared light. Sci. China Mater. 2023, 66, 375384,  DOI: 10.1007/s40843-022-2107-0
  91. 91
    Hindley, J. W.; Elani, Y.; McGilvery, C. M.; Ali, S.; Bevan, C. L.; Law, R. V.; Ces, O. Light-triggered enzymatic reactions in nested vesicle reactors. Nat. Commun. 2018, 9 (1), 1093,  DOI: 10.1038/s41467-018-03491-7
  92. 92
    Babii, O.; Afonin, S.; Diel, C.; Huhn, M.; Dommermuth, J.; Schober, T.; Koniev, S.; Hrebonkin, A.; Nesterov-Mueller, A.; Komarov, I. V. Diarylethene-based photoswitchable inhibitors of serine proteases. Angew. Chem., Int. Ed. 2021, 60 (40), 2178921794,  DOI: 10.1002/anie.202108847
  93. 93
    Rifaie-Graham, O.; Yeow, J.; Najer, A.; Wang, R.; Sun, R.; Zhou, K.; Dell, T. N.; Adrianus, C.; Thanapongpibul, C.; Chami, M. Photoswitchable gating of non-equilibrium enzymatic feedback in chemically communicating polymersome nanoreactors. Nat. Chem. 2023, 15 (1), 110118,  DOI: 10.1038/s41557-022-01062-4
  94. 94
    Bisswanger, H. Enzyme assays. Perspectives in Science 2014, 1 (1–6), 4155,  DOI: 10.1016/j.pisc.2014.02.005
  95. 95
    Zhang, Y.; Wang, Q.; Hess, H. Increasing enzyme cascade throughput by pH-engineering the microenvironment of individual enzymes. ACS Catal. 2017, 7 (3), 20472051,  DOI: 10.1021/acscatal.6b03431
  96. 96
    Fan, X.; Walther, A. pH feedback lifecycles programmed by enzymatic logic gates using common foods as fuels. Angew. Chem., Int. Ed. 2021, 60, 11398,  DOI: 10.1002/anie.202017003
  97. 97
    Che, H.; Cao, S.; van Hest, J. C. M. Feedback-induced temporal control of ″breathing″ polymersomes to create self-adaptive nanoreactors. J. Am. Chem. Soc. 2018, 140 (16), 53565359,  DOI: 10.1021/jacs.8b02387
  98. 98
    Wells, P. K.; Smutok, O.; Melman, A.; Katz, E. Switchable biocatalytic reactions controlled by interfacial pH changes produced by orthogonal biocatalytic processes. ACS Appl. Mater. Interfaces 2021, 13 (29), 3383033839,  DOI: 10.1021/acsami.1c07393
  99. 99
    Wang, C.; Fischer, A.; Ehrlich, A.; Nahmias, Y.; Willner, I. Biocatalytic reversible control of the stiffness of DNA-modified responsive hydrogels: applications in shape-memory, self-healing and autonomous controlled release of insulin. Chem. Sci. 2020, 11 (17), 45164524,  DOI: 10.1039/D0SC01319F
  100. 100
    Zhang, Y.; Nie, N.; Wang, H.; Tong, Z.; Xing, H.; Zhang, Y. Smart enzyme catalysts capable of self-separation by sensing the reaction extent. Biosens. Bioelectron. 2023, 239, 115585,  DOI: 10.1016/j.bios.2023.115585
  101. 101
    Jain, M.; Ravoo, B. J. Fuel-driven and enzyme-regulated redox-responsive supramolecular hydrogels. Angew. Chem., Int. Ed. 2021, 60 (38), 2106221068,  DOI: 10.1002/anie.202107917
  102. 102
    Eixelsberger, T.; Nidetzky, B. Enzymatic redox cascade for one-pot synthesis of uridine 5′-diphosphate xylose from uridine 5′-diphosphate glucose. Adv. Synth. Catal. 2014, 356 (17), 35753584,  DOI: 10.1002/adsc.201400766
  103. 103
    Kuk, S. K.; Singh, R. K.; Nam, D. H.; Singh, R.; Lee, J. K.; Park, C. B. Photoelectrochemical reduction of carbon dioxide to methanol through a highly efficient enzyme cascade. Angew. Chem., Int. Ed. 2017, 56 (14), 38273832,  DOI: 10.1002/anie.201611379
  104. 104
    Mallawarachchi, S.; Gejji, V.; Sierra, L. S.; Wang, H.; Fernando, S. Electrical field reversibly modulates enzyme kinetics of hexokinase entrapped in an electro-responsive hydrogel. ACS Appl. Bio Mater. 2019, 2 (12), 56765686,  DOI: 10.1021/acsabm.9b00748
  105. 105
    Morello, G.; Megarity, C. F.; Armstrong, F. A. The power of electrified nanoconfinement for energising, controlling and observing long enzyme cascades. Nat. Commun. 2021, 12 (1), 340,  DOI: 10.1038/s41467-020-20403-w
  106. 106
    Milton, R. D.; Cai, R.; Abdellaoui, S.; Leech, D.; De Lacey, A. L.; Pita, M.; Minteer, S. D. Bioelectrochemical Haber-Bosch process: an ammonia-producing H2/N2 fuel cell. Angew. Chem., Int. Ed. 2017, 56 (10), 26802683,  DOI: 10.1002/anie.201612500
  107. 107
    Cao, Y.; Wang, Y. Temperature-mediated regulation of enzymatic activity. ChemCatChem. 2016, 8 (17), 27402747,  DOI: 10.1002/cctc.201600406
  108. 108
    Zhang, S.; Wang, C.; Chang, H.; Zhang, Q.; Cheng, Y. Off-on switching of enzyme activity by near-infrared light-induced photothermal phase transition of nanohybrids. Sci. Adv. 2019, 5, eaaw4252,  DOI: 10.1126/sciadv.aaw4252
  109. 109
    Gobbo, P.; Patil, A. J.; Li, M.; Harniman, R.; Briscoe, W. H.; Mann, S. Programmed assembly of synthetic protocells into thermoresponsive prototissues. Nat. Mater. 2018, 17 (12), 11451153,  DOI: 10.1038/s41563-018-0183-5
  110. 110
    Szekeres, K.; Bollella, P.; Kim, Y.; Minko, S.; Melman, A.; Katz, E. Magneto-controlled enzyme activity with locally produced pH changes. J. Phys. Chem. Lett. 2021, 12 (10), 25232527,  DOI: 10.1021/acs.jpclett.1c00036
  111. 111
    Dhasaiyan, P.; Ghosh, T.; Lee, H. G.; Lee, Y.; Hwang, I.; Mukhopadhyay, R. D.; Park, K. M.; Shin, S.; Kang, I. S.; Kim, K. Cascade reaction networks within audible sound induced transient domains in a solution. Nat. Commun. 2022, 13 (1), 2372,  DOI: 10.1038/s41467-022-30124-x
  112. 112
    Küchler, A.; Yoshimoto, M.; Luginbühl, S.; Mavelli, F.; Walde, P. Enzymatic reactions in confined environments. Nat. Nanotechnol. 2016, 11 (5), 409420,  DOI: 10.1038/nnano.2016.54
  113. 113
    Hwang, E. T.; Lee, S. Multienzymatic Cascade Reactions Enzyme complex by immobilization. ACS Catal. 2019, 9 (5), 44024425,  DOI: 10.1021/acscatal.8b04921
  114. 114
    Ji, Q.; Wang, B.; Tan, J.; Zhu, L.; Li, L. Immobilized multienzymatic systems for catalysis of cascade reactions. Process Biochemistry 2016, 51 (9), 11931203,  DOI: 10.1016/j.procbio.2016.06.004
  115. 115
    Kazenwadel, F.; Franzreb, M.; Rapp, B. E. Synthetic enzyme supercomplexes: co-immobilization of enzyme cascades. Anal. Methods 2015, 7 (10), 40304037,  DOI: 10.1039/C5AY00453E
  116. 116
    Xu, K.; Chen, X.; Zheng, R.; Zheng, Y. Immobilization of multi-enzymes on support materials for efficient biocatalysis. Front. Bioeng. Biotechnol. 2020, 8, 660,  DOI: 10.3389/fbioe.2020.00660
  117. 117
    Bié, J.; Sepodes, B.; Fernandes, P. C. B.; Ribeiro, M. H. L. Enzyme immobilization and co-immobilization: main framework, advances and some applications. Processes 2022, 10 (3), 494,  DOI: 10.3390/pr10030494
  118. 118
    Benitez-Mateos, A. I.; Roura Padrosa, D.; Paradisi, F. Multistep enzyme cascades as a route towards green and sustainable pharmaceutical syntheses. Nat. Chem. 2022, 14 (5), 489499,  DOI: 10.1038/s41557-022-00931-2
  119. 119
    Liang, J.; Liang, K. Multi-enzyme cascade reactions in metal-organic frameworks. Chem. Rec. 2020, 20 (10), 11001116,  DOI: 10.1002/tcr.202000067
  120. 120
    Tsitkov, S.; Hess, H. Design principles for a compartmentalized enzyme cascade reaction. ACS Catal. 2019, 9, 24322439,  DOI: 10.1021/acscatal.8b04419
  121. 121
    Oh, W.; Jeong, D.; Park, J.-W. An Artificial compartmentalized biocatalytic cascade system constructed with enzyme-caged reticulate nanoporous membranes. Adv. Mater. Interfaces 2023, 10, 2300185,  DOI: 10.1002/admi.202300185
  122. 122
    Diamanti, E.; Andrés-Sanz, D.; Orrego, A. H.; Carregal-Romero, S.; López-Gallego, F. Surpassing substrate–enzyme competition by compartmentalization. ACS Catal. 2023, 13 (17), 1144111454,  DOI: 10.1021/acscatal.3c01965
  123. 123
    Aumiller, W. M., Jr.; Uchida, M.; Douglas, T. Protein cage assembly across multiple length scales. Chem. Soc. Rev. 2018, 47, 34333469,  DOI: 10.1039/C7CS00818J
  124. 124
    Jaekel, A.; Stegemann, P.; Saccà, B. Manipulating enzymes properties with DNA nanostructures. Molecules 2019, 24 (20), 3694,  DOI: 10.3390/molecules24203694
  125. 125
    Jiao, Y.; Shang, Y.; Li, N.; Ding, B. DNA-based enzymatic systems and their applications. iScience 2022, 25, 104018,  DOI: 10.1016/j.isci.2022.104018
  126. 126
    Engelen, W.; Janssen, B. M. G.; Merkx, M. DNA-based control of protein activity. Chem. Commun. 2016, 52, 35983610,  DOI: 10.1039/C5CC09853J
  127. 127
    Fu, J.; Wang, Z.; Liang, X. H.; Oh, S. W.; St. Iago-McRae, E.; Zhang, T. DNA-scaffolded proximity assembly and confinement of multienzyme reactions. Top. Curr. Chem. 2020, 378, 38,  DOI: 10.1007/s41061-020-0299-3
  128. 128
    Kröll, S.; Niemeyer, C. M. Nucleic acid-based enzyme cascades─current trends and future perspectives. Angew. Chem., Int. Ed. 2024, 63, e202314452,  DOI: 10.1002/anie.202314452
  129. 129
    Liang, J.; Liang, K. Multi-enzyme cascade reactions in metal-organic frameworks. Chem. Rec. 2020, 20, 1100,  DOI: 10.1002/tcr.202000067
  130. 130
    Dhakshinamoorthy, A.; Asiri, A. M.; Garcia, H. Integration of metal organic frameworks with enzymes as multifunctional solids for cascade catalysis. Dalton Trans. 2020, 49, 1105911072,  DOI: 10.1039/D0DT02045A
  131. 131
    Wang, X.; Lan, P. C.; Ma, S. Metal–organic frameworks for enzyme immobilization: beyond host matrix materials. ACS Cent. Sci. 2020, 6 (9), 14971506,  DOI: 10.1021/acscentsci.0c00687
  132. 132
    Lian, X.; Fang, Y.; Joseph, E.; Wang, Q.; Li, J.; Banerjee, S.; Lollar, C.; Wang, X.; Zhou, H.-C. Enzyme–MOF (metal–organic framework) composites. Chem. Soc. Rev. 2017, 46, 33863401,  DOI: 10.1039/C7CS00058H
  133. 133
    Liang, W.; Wied, P.; Carraro, F.; Sumby, C. J.; Nidetzky, B.; Tsung, C.-K.; Falcaro, P.; Doonan, C. J. Metal–organic framework-based enzyme biocomposites. Chem. Rev. 2021, 121 (3), 10771129,  DOI: 10.1021/acs.chemrev.0c01029
  134. 134
    Hu, J.; Zhang, G.; Liu, S. Enzyme-responsive polymeric assemblies, nanoparticles and hydrogels. Chem. Soc. Rev. 2012, 41 (18), 59335949,  DOI: 10.1039/c2cs35103j
  135. 135
    Li, P.; Zhong, Y.; Wang, X.; Hao, J. Enzyme-regulated healable polymeric hydrogels. ACS Cent. Sci. 2020, 6 (9), 15071522,  DOI: 10.1021/acscentsci.0c00768
  136. 136
    Amir, R. J.; Zhong, S.; Pochan, D. J.; Hawker, C. J. Enzymatically triggered self-assembly of block copolymers. J. Am. Chem. Soc. 2009, 131 (39), 1394913951,  DOI: 10.1021/ja9060917
  137. 137
    Kobayashi, S.; Uyama, H.; Kimura, S. Enzymatic polymerization. Chem. Rev. 2001, 101 (12), 37933818,  DOI: 10.1021/cr990121l
  138. 138
    Klemperer, R. G.; Shannon, M. R.; Ross Anderson, J. L.; Perriman, A. W. Bienzymatic generation of interpenetrating polymer networked engineered living materials with shape changing Pproperties. Adv. Mater. Technol. 2023, 8, 2300626,  DOI: 10.1002/admt.202300626
  139. 139
    Mao, Y.; Su, T.; Wu, Q.; Liao, C.; Wang, Q. Dual enzymatic formation of hybrid hydrogels with supramolecular-polymeric networks. Chem. Commun. 2014, 50 (92), 1442914432,  DOI: 10.1039/C4CC06472K
  140. 140
    Wei, Q.; Xu, M.; Liao, C.; Wu, Q.; Liu, M.; Zhang, Y.; Wu, C.; Cheng, L.; Wang, Q. Printable hybrid hydrogel by dual enzymatic polymerization with superactivity. Chem. Sci. 2016, 7 (4), 27482752,  DOI: 10.1039/C5SC02234G
  141. 141
    Yang, Z.; Liang, G.; Wang, L.; Xu, B. Using a kinase/phosphatase switch to regulate a supramolecular hydrogel and forming the supramolecular hydrogel in vivo. J. Am. Chem. Soc. 2006, 128 (9), 30383043,  DOI: 10.1021/ja057412y
  142. 142
    Knipe, J. M.; Chen, F.; Peppas, N. A. Enzymatic biodegradation of hydrogels for protein delivery targeted to the small intestine. Biomacromolecules 2015, 16 (3), 962972,  DOI: 10.1021/bm501871a
  143. 143
    Pappas, C. G.; Sasselli, I. R.; Ulijn, R. V. Biocatalytic pathway selection in transient tripeptide nanostructures. Angew. Chem., Int. Ed. 2015, 54 (28), 81198123,  DOI: 10.1002/anie.201500867
  144. 144
    Cook, A. B.; Decuzzi, P. Harnessing endogenous stimuli for responsive materials in theranostics. ACS Nano 2021, 15 (2), 20682098,  DOI: 10.1021/acsnano.0c09115
  145. 145
    Ku, T. H.; Chien, M. P.; Thompson, M. P.; Sinkovits, R. S.; Olson, N. H.; Baker, T. S.; Gianneschi, N. C. Controlling and switching the morphology of micellar nanoparticles with enzymes. J. Am. Chem. Soc. 2011, 133 (22), 83928395,  DOI: 10.1021/ja2004736
  146. 146
    Postma, S. G.; Vialshin, I. N.; Gerritsen, C. Y.; Bao, M.; Huck, W. T. Preprogramming complex hydrogel responses using enzymatic reaction networks. Angew. Chem., Int. Ed. 2017, 56 (7), 17941798,  DOI: 10.1002/anie.201610875
  147. 147
    Ikeda, M.; Tanida, T.; Yoshii, T.; Kurotani, K.; Onogi, S.; Urayama, K.; Hamachi, I. Installing logic-gate responses to a variety of biological substances in supramolecular hydrogel-enzyme hybrids. Nat. Chem. 2014, 6 (6), 511518,  DOI: 10.1038/nchem.1937
  148. 148
    Heuser, T.; Merindol, R.; Loescher, S.; Klaus, A.; Walther, A. Photonic devices out of equilibrium: transient memory, signal propagation, and sensing. Adv. Mater. 2017, 29, 1606842,  DOI: 10.1002/adma.201606842
  149. 149
    Hong, Y.; Velegol, D.; Chaturvedi, N.; Sen, A. Biomimetic behavior of synthetic particles: from microscopic randomness to macroscopic control. Phys. Chem. Chem. Phys. 2010, 12 (7), 14231435,  DOI: 10.1039/B917741H
  150. 150
    Sengupta, S.; Dey, K. K.; Muddana, H. S.; Tabouillot, T.; Ibele, M. E.; Butler, P. J.; Sen, A. Enzyme molecules as nanomotors. J. Am. Chem. Soc. 2013, 135 (4), 14061414,  DOI: 10.1021/ja3091615
  151. 151
    Zhao, X.; Gentile, K.; Mohajerani, F.; Sen, A. Powering motion with enzymes. Acc. Chem. Res. 2018, 51 (10), 23732381,  DOI: 10.1021/acs.accounts.8b00286
  152. 152
    Zhang, Y.; Hess, H. Enhanced diffusion of catalytically active enzymes. ACS Cen. Sci. 2019, 5 (6), 939948,  DOI: 10.1021/acscentsci.9b00228
  153. 153
    Zhang, Y.; Hess, H. Chemically-powered swimming and diffusion in the microscopic world. Nat. Rev. Chem. 2021, 5, 500510,  DOI: 10.1038/s41570-021-00281-6
  154. 154
    Patiño, T.; Arqué, X.; Mestre, R.; Palacios, L.; Sánchez, S. Fundamental aspects of enzyme-powered micro- and nanoswimmers. Acc. Chem. Res. 2018, 51 (11), 26622671,  DOI: 10.1021/acs.accounts.8b00288
  155. 155
    Wang, L.; Song, S.; van Hest, J.; Abdelmohsen, L.; Huang, X.; Sánchez, S. Biomimicry of cellular motility and communication based on synthetic soft-architectures. Small 2020, 16 (27), 1907680,  DOI: 10.1002/smll.201907680
  156. 156
    Hermanová, S.; Pumera, M. Biocatalytic micro- and nanomotors. Chem. Eur. J. 2020, 26, 1108,  DOI: 10.1002/chem.202084962
  157. 157
    Muddana, H. S.; Sengupta, S.; Mallouk, T. E.; Sen, A.; Butler, P. J. Substrate catalysis enhances single-enzyme diffusion. J. Am. Chem. Soc. 2010, 132 (7), 21102111,  DOI: 10.1021/ja908773a
  158. 158
    Hortelão, A. C.; García-Jimeno, S.; Cano-Sarabia, M.; Patiño, T.; Maspoch, D.; Sanchez, S. LipoBots: Using liposomal vesicles as protective shell of urease-based nanomotors. Adv. Funct. Mater. 2020, 30 (42), 2002767,  DOI: 10.1002/adfm.202002767
  159. 159
    Choi, H.; Cho, S. H.; Hahn, S. K. Urease-powered polydopamine nanomotors for intravesical therapy of bladder diseases. ACS Nano 2020, 14 (6), 66836692,  DOI: 10.1021/acsnano.9b09726
  160. 160
    Yang, Z.; Wang, L.; Gao, Z.; Hao, X.; Luo, M.; Yu, Z.; Guan, J. Ultrasmall enzyme-powered janus nanomotor working in blood circulation system. ACS Nano 2023, 17 (6), 60236035,  DOI: 10.1021/acsnano.3c00548
  161. 161
    Toebes, B. J.; Cao, F.; Wilson, D. A. Spatial control over catalyst positioning on biodegradable polymeric nanomotors. Nat. Commun. 2019, 10 (1), 5308,  DOI: 10.1038/s41467-019-13288-x
  162. 162
    Qiu, B.; Xie, L.; Zeng, J.; Liu, T.; Yan, M.; Zhou, S.; Liang, Q.; Tang, J.; Liang, K.; Kong, B. Interfacially super-assembled asymmetric and H2O2 sensitive multilayer-sandwich magnetic mesoporous silica nanomotors for detecting and removing heavy metal ions. Adv. Funct. Mater. 2021, 31, 2010694,  DOI: 10.1002/adfm.202010694
  163. 163
    Abdelmohsen, L. K.; Nijemeisland, M.; Pawar, G. M.; Janssen, G. J.; Nolte, R. J.; van Hest, J. C.; Wilson, D. A. Dynamic loading and unloading of proteins in polymeric stomatocytes: formation of an enzyme-loaded supramolecular nanomotor. ACS Nano 2016, 10 (2), 26522660,  DOI: 10.1021/acsnano.5b07689
  164. 164
    Nijemeisland, M.; Abdelmohsen, L. K.; Huck, W. T.; Wilson, D. A.; van Hest, J. C. A compartmentalized out-of-equilibrium enzymatic reaction network for sustained autonomous movement. ACS Cent. Sci. 2016, 2 (11), 843849,  DOI: 10.1021/acscentsci.6b00254
  165. 165
    Chatterjee, A.; Ghosh, S.; Ghosh, C.; Das, D. Fluorescent microswimmers based on cross-beta amyloid nanotubes and divergent cascade networks. Angew. Chem., Int. Ed. 2022, 61 (29), e202201547,  DOI: 10.1002/anie.202201547
  166. 166
    Dey, K. K.; Zhao, X.; Tansi, B. M.; Mendez-Ortiz, W. J.; Cordova-Figueroa, U. M.; Golestanian, R.; Sen, A. Micromotors powered by enzyme catalysis. Nano Lett. 2015, 15 (12), 83118315,  DOI: 10.1021/acs.nanolett.5b03935
  167. 167
    Zhao, X.; Palacci, H.; Yadav, V.; Spiering, M. M.; Gilson, M. K.; Butler, P. J.; Hess, H.; Benkovic, S. J.; Sen, A. Substrate-driven chemotactic assembly in an enzyme cascade. Nat. Chem. 2018, 10 (3), 311317,  DOI: 10.1038/nchem.2905
  168. 168
    Wang, J.; Toebes, B. J.; Plachokova, A. S.; Liu, Q.; Deng, D.; Jansen, J. A.; Yang, F.; Wilson, D. A. Self-propelled PLGA micromotor with chemotactic response to inflammation. Adv. Healthcare Mater. 2020, 9 (7), 1901710,  DOI: 10.1002/adhm.201901710
  169. 169
    Joseph, A.; Contini, C.; Cecchin, D.; Nyberg, S.; Ruiz-Perez, L.; Gaitzsch, J.; Fullstone, G.; Tian, X.; Azizi, J.; Preston, J.; Volpe, G.; Battaglia, G. Chemotactic synthetic vesicles: design and applications in blood-brain barrier crossing. Sci. Adv. 2017, 3, e1700362,  DOI: 10.1126/sciadv.1700362
  170. 170
    Kumar, B.; Patil, A. J.; Mann, S. Enzyme-powered motility in buoyant organoclay/DNA protocells. Nat. Chem. 2018, 10 (11), 1154116,  DOI: 10.1038/s41557-018-0119-3
  171. 171
    Gao, N.; Li, M.; Tian, L.; Patil, A. J.; Pavan Kumar, B.; Mann, S. Chemical-mediated translocation in protocell-based microactuators. Nat. Chem. 2021, 13 (9), 868879,  DOI: 10.1038/s41557-021-00728-9
  172. 172
    Wheeldon, I.; Minteer, S. D.; Banta, S.; Barton, S. C.; Atanassov, P.; Sigman, M. Substrate channelling as an approach to cascade reactions. Nat. Chem. 2016, 8 (4), 299309,  DOI: 10.1038/nchem.2459
  173. 173
    Hinzpeter, F.; Gerland, U.; Tostevin, F. Optimal compartmentalization strategies for metabolic microcompartments. Biophys. J. 2017, 112, 767779,  DOI: 10.1016/j.bpj.2016.11.3194
  174. 174
    Qiao, Y.; Li, M.; Booth, R.; Mann, S. Predatory behaviour in synthetic protocell communities. Nat. Chem. 2017, 9 (2), 110119,  DOI: 10.1038/nchem.2617
  175. 175
    Qiao, Y.; Li, M.; Qiu, D.; Mann, S. Response-retaliation behavior in synthetic protocell communities. Angew. Chem., Int. Ed. 2019, 58 (49), 1775817763,  DOI: 10.1002/anie.201909313
  176. 176
    Chakraborty, T.; Wegner, S. V. Cell to cell signaling through light in artificial cell communities: glowing predator lures prey. ACS Nano 2021, 15 (6), 94349444,  DOI: 10.1021/acsnano.1c01600
  177. 177
    Mason, A. F.; Buddingh, B. C.; Williams, D. S.; van Hest, J. C. M. Hierarchical self-assembly of a copolymer-stabilized coacervate protocell. J. Am. Chem. Soc. 2017, 139 (48), 1730917312,  DOI: 10.1021/jacs.7b10846
  178. 178
    Wang, X.; Tian, L.; Du, H.; Li, M.; Mu, W.; Drinkwater, B. W.; Han, X.; Mann, S. Chemical communication in spatially organized protocell colonies and protocell/living cell micro-arrays. Chem. Sci. 2019, 10 (41), 94469453,  DOI: 10.1039/C9SC04522H
  179. 179
    Buddingh’, B. C.; Elzinga, J.; van Hest, J. C. M. Intercellular communication between artificial cells by allosteric amplification of a molecular signal. Nat. Commun. 2020, 11 (1), 1652,  DOI: 10.1038/s41467-020-15482-8
  180. 180
    Ji, Y.; Chakraborty, T.; Wegner, S. V. Self-regulated and bidirectional communication in synthetic cell communities. ACS Nano 2023, 17 (10), 89929002,  DOI: 10.1021/acsnano.2c09908
  181. 181
    Taylor, H.; Gao, N.; Mann, S. Chemical communication and protocell-matrix dynamics in segregated colloidosome micro-colonies. Angew. Chem., Int. Ed. 2023, 62 (24), e202300932,  DOI: 10.1002/anie.202300932
  182. 182
    Fusi, G.; Del Giudice, D.; Skarsetz, O.; Di Stefano, S.; Walther, A. Autonomous soft robots empowered by chemical reaction networks. Adv. Mater. 2023, 35 (7), e2209870,  DOI: 10.1002/adma.202209870
  183. 183
    Elani, Y.; Law, R. V.; Ces, O. Vesicle-based artificial cells as chemical microreactors with spatially segregated reaction pathways. Nat. Commun. 2014, 5, 5305,  DOI: 10.1038/ncomms6305
  184. 184
    Liu, S.; Zhang, Y.; He, X.; Li, M.; Huang, J.; Yang, X.; Wang, K.; Mann, S.; Liu, J. Signal processing and generation of bioactive nitric oxide in a model prototissue. Nat. Commun. 2022, 13 (1), 5254,  DOI: 10.1038/s41467-022-32941-6
  185. 185
    Sengupta, S.; Patra, D.; Ortiz-Rivera, I.; Agrawal, A.; Shklyaev, S.; Dey, K. K.; Cordova-Figueroa, U.; Mallouk, T. E.; Sen, A. Self-powered enzyme micropumps. Nat. Chem. 2014, 6 (5), 415422,  DOI: 10.1038/nchem.1895
  186. 186
    Maiti, S.; Shklyaev, O. E.; Balazs, A. C.; Sen, A. Self-organization of fluids in a multienzymatic pump system. Langmuir 2019, 35 (10), 37243732,  DOI: 10.1021/acs.langmuir.8b03607
  187. 187
    Ortiz-Rivera, I.; Courtney, T. M.; Sen, A. Enzyme micropump-based inhibitor assays. Adv. Funct. Mater. 2016, 26, 21352142,  DOI: 10.1002/adfm.201504619
  188. 188
    Manna, R. K.; Shklyaev, O. E.; Balazs, A. C. Chemically driven multimodal locomotion of active, flexible sheets. Langmuir 2023, 39 (2), 780789,  DOI: 10.1021/acs.langmuir.2c02666
  189. 189
    Simo, C.; Serra-Casablancas, M.; Hortelao, A. C.; Di Carlo, V.; Guallar-Garrido, S.; Plaza-Garcia, S.; Rabanal, R. M.; Ramos-Cabrer, P.; Yague, B.; Aguado, L.; Bardia, L.; Tosi, S.; Gomez-Vallejo, V.; Martin, A.; Patino, T.; Julian, E.; Colombelli, J.; Llop, J.; Sanchez, S. Urease-powered nanobots for radionuclide bladder cancer therapy. Nat. Nanotechnol. 2024,  DOI: 10.1038/s41565-023-01577-y .
  190. 190
    Rollin, J. A.; Tam, T. K.; Zhang, Y. H. P. New biotechnology paradigm: cell-free biosystems for biomanufacturing. Green Chem. 2013, 15 (7), 17081719,  DOI: 10.1039/c3gc40625c
  191. 191
    Zhang, Y.; Hess, H. Toward rational design of high-efficiency enzyme cascades. ACS Catal. 2017, 7 (9), 60186027,  DOI: 10.1021/acscatal.7b01766
  192. 192
    Shi, J.; Wu, Y.; Zhang, S.; Tian, Y.; Yang, D.; Jiang, Z. Bioinspired construction of multi-enzyme catalytic systems. Chem. Soc. Rev. 2018, 47, 42954313,  DOI: 10.1039/C7CS00914C
  193. 193
    Wang, Z.; Sundara Sekar, B.; Li, Z. Recent advances in artificial enzyme cascades for the production of value-added chemicals. Bioresour. Technol. 2021, 323, 124551,  DOI: 10.1016/j.biortech.2020.124551
  194. 194
    Mordhorst, S.; Andexer, J. N. Round, round we go – strategies for enzymatic cofactor regeneration. Nat. Prod. Rep. 2020, 37, 13161333,  DOI: 10.1039/D0NP00004C
  195. 195
    Bachosz, K.; Zdarta, J.; Bilal, M.; Meyer, A. S.; Jesionowski, T. Enzymatic cofactor regeneration systems: A new perspective on efficiency assessment. Sci. Total Environ. 2023, 868, 161630,  DOI: 10.1016/j.scitotenv.2023.161630
  196. 196
    Monck, C.; Elani, Y.; Ceroni, F. Cell-free protein synthesis: biomedical applications and future perspectives. Chem. Eng. Res. Des. 2022, 177, 653658,  DOI: 10.1016/j.cherd.2021.11.025
  197. 197
    Perez, J. G.; Stark, J. C.; Jewett, M. C. Cell-free synthetic biology: engineering beyond the cell. Cold Spring Harb. Perspect. Biol. 2016, 8 (12), a023853,  DOI: 10.1101/cshperspect.a023853
  198. 198
    Silverman, A. D.; Karim, A. S.; Jewett, M. C. Cell-free gene expression: an expanded repertoire of applications. Nat. Rev. Genet. 2020, 21, 151170,  DOI: 10.1038/s41576-019-0186-3
  199. 199
    Korman, T. P.; Sahachartsiri, B.; Li, D.; Vinokur, J. M.; Eisenberg, D.; Bowie, J. U. A synthetic biochemistry system for the in vitro production of isoprene from glycolysis intermediates. Protein Sci. 2014, 23 (5), 576585,  DOI: 10.1002/pro.2436
  200. 200
    Korman, T. P.; Opgenorth, P. H.; Bowie, J. U. A synthetic biochemistry platform for cell free production of monoterpenes from glucose. Nat. Commun. 2017, 8, 15526,  DOI: 10.1038/ncomms15526
  201. 201
    Opgenorth, P. H.; Korman, T. P.; Bowie, J. U. A synthetic biochemistry molecular purge valve module that maintains redox balance. Nat. Commun. 2014, 5, 4113,  DOI: 10.1038/ncomms5113
  202. 202
    Opgenorth, P. H.; Korman, T. P.; Bowie, J. U. A synthetic biochemistry module for production of bio-based chemicals from glucose. Nat. Chem. Biol. 2016, 12 (6), 393395,  DOI: 10.1038/nchembio.2062
  203. 203
    Valliere, M. A.; Korman, T. P.; Woodall, N. B.; Khitrov, G. A.; Taylor, R. E.; Baker, D.; Bowie, J. U. A cell-free platform for the prenylation of natural products and application to cannabinoid production. Nat. Commun. 2019, 10 (1), 565,  DOI: 10.1038/s41467-019-08448-y
  204. 204
    Sarria, S.; Wong, B.; Martin, H. G.; Keasling, J. D.; Peralta-Yahya, P. Microbial synthesis of pinene. ACS Synth. Biol. 2014, 3 (7), 466475,  DOI: 10.1021/sb4001382
  205. 205
    Karim, A. S.; Jewett, M. C. A cell-free framework for rapid biosynthetic pathway prototyping and enzyme discovery. Metab. Eng. 2016, 36, 116126,  DOI: 10.1016/j.ymben.2016.03.002
  206. 206
    Dudley, Q. M.; Anderson, K. C.; Jewett, M. C. Cell-free mixing of Escherichia coli crude extracts to prototype and rationally engineer high-titer mevalonate synthesis. ACS Synth. Biol. 2016, 5 (12), 15781588,  DOI: 10.1021/acssynbio.6b00154
  207. 207
    Casini, A.; Chang, F. Y.; Eluere, R.; King, A. M.; Young, E. M.; Dudley, Q. M.; Karim, A.; Pratt, K.; Bristol, C.; Forget, A. A pressure test to make 10 molecules in 90 days: external evaluation of methods to engineer biology. J. Am. Chem. Soc. 2018, 140 (12), 43024316,  DOI: 10.1021/jacs.7b13292
  208. 208
    Dudley, Q. M.; Nash, C. J.; Jewett, M. C. Cell-free biosynthesis of limonene using enzyme-enriched Escherichia coli lysates. Synthetic Biology 2019, 4 (1), ysz003,  DOI: 10.1093/synbio/ysz003
  209. 209
    Nielsen, D. U.; Hu, X.-M.; Daasbjerg, K.; Skrydstrup, T. Chemically and electrochemically catalysed conversion of CO2 to CO with follow-up utilization to value-added chemicals. Nat. Catal. 2018, 1 (4), 244254,  DOI: 10.1038/s41929-018-0051-3
  210. 210
    Long, S. P.; Ainsworth, E. A.; Rogers, A.; Ort, D. R. Rising atmospheric carbon dioxide: plants FACE the future. Annu. Rev. Plant Biol. 2004, 55, 591628,  DOI: 10.1146/annurev.arplant.55.031903.141610
  211. 211
    Spreitzer, R. J.; Salvucci, M. E. Rubisco: structure, regulatory interactions, and possibilities for a better enzyme. Annu. Rev. Plant Biol. 2002, 53, 449475,  DOI: 10.1146/annurev.arplant.53.100301.135233
  212. 212
    Könneke, M.; Schubert, D. M.; Brown, P. C.; Hugler, M.; Standfest, S.; Schwander, T.; Schada von Borzyskowski, L.; Erb, T. J.; Stahl, D. A.; Berg, I. A. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (22), 82398244,  DOI: 10.1073/pnas.1402028111
  213. 213
    Scheffen, M.; Marchal, D. G.; Beneyton, T.; Schuller, S. K.; Klose, M.; Diehl, C.; Lehmann, J.; Pfister, P.; Carrillo, M.; He, H. A new-to-nature carboxylation module to improve natural and synthetic CO2 fixation. Nat. Catal. 2021, 4 (2), 105115,  DOI: 10.1038/s41929-020-00557-y
  214. 214
    Schwander, T.; Schada von Borzyskowski, L.; Burgener, S.; Cortina, N. S.; Erb, T. J. A synthetic pathway for the fixation of carbon dioxide in vitro. Science 2016, 354, 900904,  DOI: 10.1126/science.aah5237
  215. 215
    Gale, N. L.; Beck, J. V. Evidence for the Calvin cycle and hexose monophosphate pathway in Thiobacillus ferrooxidans. J. Bacteriol. 1967, 94, 10521059,  DOI: 10.1128/jb.94.4.1052-1059.1967
  216. 216
    Miller, T. E.; Beneyton, T.; Schwander, T.; Diehl, C.; Girault, M.; McLean, R.; Chotel, T.; Claus, P.; Cortina, N. S.; Baret, J.-C.; Erb, T. J. Light-powered CO2 fixation in a chloroplast mimic with natural and synthetic parts. Science 2020, 368, 649654,  DOI: 10.1126/science.aaz6802
  217. 217
    Sundaram, S.; Diehl, C.; Cortina, N. S.; Bamberger, J.; Paczia, N.; Erb, T. J. A modular in vitro platform for the production of terpenes and polyketides from CO2. Angew. Chem., Int. Ed. 2021, 60 (30), 1642016425,  DOI: 10.1002/anie.202102333
  218. 218
    Diehl, C.; Gerlinger, P. D.; Paczia, N.; Erb, T. J. Synthetic anaplerotic modules for the direct synthesis of complex molecules from CO2. Nat. Chem. Biol. 2023, 19 (2), 168175,  DOI: 10.1038/s41589-022-01179-0
  219. 219
    Xiao, L.; Liu, G.; Gong, F.; Zhu, H.; Zhang, Y.; Cai, Z.; Li, Y. A minimized synthetic carbon fixation cycle. ACS Catal. 2022, 12 (1), 799808,  DOI: 10.1021/acscatal.1c04151
  220. 220
    Bierbaumer, S.; Nattermann, M.; Schulz, L.; Zschoche, R.; Erb, T. J.; Winkler, C. K.; Tinzl, M.; Glueck, S. M. Enzymatic conversion of CO2: from natural to artificial utilization. Chem. Rev. 2023, 123 (9), 57025754,  DOI: 10.1021/acs.chemrev.2c00581
  221. 221
    Cai, T.; Sun, H.; Qiao, J.; Zhu, L.; Zhang, F.; Zhang, J.; Tang, Z.; Wei, X.; Yang, J.; Yuan, Q. Cell-free chemo-enzymatic starch synthesis from carbon dioxide. Science 2021, 373, 1523152,  DOI: 10.1126/science.abh4049
  222. 222
    Zhou, J.; Tian, X.; Yang, Q.; Zhang, Z.; Chen, C.; Cui, Z.; Ji, Y.; Schwaneberg, U.; Chen, B.; Tan, T. Three multi-enzyme cascade pathways for conversion of C1 to C2/C4 compounds. Chem. Catal. 2022, 2 (10), 26752690,  DOI: 10.1016/j.checat.2022.07.011
  223. 223
    Zhang, J.; Liu, D.; Liu, Y.; Chu, H.; Bai, J.; Cheng, J.; Zhao, H.; Fu, S.; Liu, H.; Fu, Y. Hybrid synthesis of polyhydroxybutyrate bioplastics from carbon dioxide. Green Chem. 2023, 25 (8), 32473255,  DOI: 10.1039/D3GC00387F
  224. 224
    Li, F.; Wei, X.; Zhang, L.; Liu, C.; You, C.; Zhu, Z. Installing a green engine to drive an enzyme cascade: a light-powered in vitro biosystem for poly(3-hydroxybutyrate) synthesis. Angew. Chem., Int. Ed. 2022, 61 (1), e202111054,  DOI: 10.1002/anie.202111054
  225. 225
    Liu, H.; Arbing, M. A.; Bowie, J. U. Expanding the use of ethanol as a feedstock for cell-free synthetic biochemistry by implementing acetyl-CoA and ATP generating pathways. Sci. Rep. 2022, 12 (1), 7700,  DOI: 10.1038/s41598-022-11653-3
  226. 226
    Bailoni, E.; Partipilo, M.; Coenradij, J.; Grundel, D. A. J.; Slotboom, D. J.; Poolman, B. Minimal out-of-equilibrium metabolism for synthetic cells: a membrane perspective. ACS Synth. Biol. 2023, 12 (4), 922946,  DOI: 10.1021/acssynbio.3c00062
  227. 227
    Lee, K. Y.; Park, S. J.; Lee, K. A.; Kim, S. H.; Kim, H.; Meroz, Y.; Mahadevan, L.; Jung, K. H.; Ahn, T. K.; Parker, K. K. Photosynthetic artificial organelles sustain and control ATP-dependent reactions in a protocellular system. Nat. Biotechnol. 2018, 36 (6), 530535,  DOI: 10.1038/nbt.4140
  228. 228
    Berhanu, S.; Ueda, T.; Kuruma, Y. Artificial photosynthetic cell producing energy for protein synthesis. Nat. Commun. 2019, 10 (1), 1325,  DOI: 10.1038/s41467-019-09147-4
  229. 229
    Bailoni, E.; Poolman, B. ATP recycling fuels sustainable glycerol 3-phosphate formation in synthetic cells fed by dynamic dialysis. ACS Synth. Biol. 2022, 11 (7), 23482360,  DOI: 10.1021/acssynbio.2c00075
  230. 230
    Blanken, D.; Foschepoth, D.; Serrao, A. C.; Danelon, C. Genetically controlled membrane synthesis in liposomes. Nat. Commun. 2020, 11 (1), 4317,  DOI: 10.1038/s41467-020-17863-5
  231. 231
    Partipilo, M.; Ewins, E. J.; Frallicciardi, J.; Robinson, T.; Poolman, B.; Slotboom, D. J. Minimal pathway for the regeneration of redox cofactors. JACS Au 2021, 1 (12), 22802293,  DOI: 10.1021/jacsau.1c00406
  232. 232
    Pols, T.; Sikkema, H. R.; Gaastra, B. F.; Frallicciardi, J.; Smigiel, W. M.; Singh, S.; Poolman, B. A synthetic metabolic network for physicochemical homeostasis. Nat. Commun. 2019, 10 (1), 4239,  DOI: 10.1038/s41467-019-12287-2
  233. 233
    Katz, E.; Privman, V. Enzyme-based logic systems for information processing. Chem. Soc. Rev. 2010, 39 (5), 18351857,  DOI: 10.1039/b806038j
  234. 234
    Benenson, Y. Biomolecular computing systems: principles, progress and potential. Nat. Rev. Genet. 2012, 13 (7), 455468,  DOI: 10.1038/nrg3197
  235. 235
    Grozinger, L.; Amos, M.; Gorochowski, T. E.; Carbonell, P.; Oyarzun, D. A.; Stoof, R.; Fellermann, H.; Zuliani, P.; Tas, H.; Goni-Moreno, A. Pathways to cellular supremacy in biocomputing. Nat. Commun. 2019, 10 (1), 5250,  DOI: 10.1038/s41467-019-13232-z
  236. 236
    Ivanov, N. M.; Baltussen, M. G.; Regueiro, C. L. F.; Derks, M.; Huck, W. T. S. Computing arithmetic functions using immobilised enzymatic reaction networks. Angew. Chem., Int. Ed. 2023, 62 (7), e202215759,  DOI: 10.1002/anie.202215759
  237. 237
    Genot, A. J.; Fujii, T.; Rondelez, Y. Computing with competition in biochemical networks. Phys. Rev. Lett. 2012, 109 (20), 208102,  DOI: 10.1103/PhysRevLett.109.208102
  238. 238
    Pandi, A.; Koch, M.; Voyvodic, P. L.; Soudier, P.; Bonnet, J.; Kushwaha, M.; Faulon, J. L. Metabolic perceptrons for neural computing in biological systems. Nat. Commun. 2019, 10 (1), 3880,  DOI: 10.1038/s41467-019-11889-0
  239. 239
    Okumura, S.; Gines, G.; Lobato-Dauzier, N.; Baccouche, A.; Deteix, R.; Fujii, T.; Rondelez, Y.; Genot, A. J. Nonlinear decision-making with enzymatic neural networks. Nature 2022, 610 (7932), 496501,  DOI: 10.1038/s41586-022-05218-7
  240. 240
    Hathcock, D.; Sheehy, J.; Weisenberger, C.; Ilker, E.; Hinczewski, M. Noise filtering and prediction in biological signaling networks. IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2016, 2 (1), 1630,  DOI: 10.1109/TMBMC.2016.2633269
  241. 241
    O’Brien, J.; Murugan, A. Temporal pattern recognition through analog molecular computation. ACS Synth. Biol. 2019, 8 (4), 826832,  DOI: 10.1021/acssynbio.8b00503
  242. 242
    McGrath, T.; Jones, N. S.; Ten Wolde, P. R.; Ouldridge, T. E. Biochemical machines for the interconversion of mutual information and work. Phys. Rev. Lett. 2017, 118 (2), 028101  DOI: 10.1103/PhysRevLett.118.028101
  243. 243
    Stern, M.; Murugan, A. Learning without neurons in physical systems. Annu. Rev. Condens. Matter Phys. 2023, 14, 41741,  DOI: 10.1146/annurev-conmatphys-040821-113439
  244. 244
    Shklyaev, O. E.; Balazs, A. C. Interlinking spatial dimensions and kinetic processes in dissipative materials to create synthetic systems with lifelike functionality. Nat. Nanotechnol. 2024, 19, 146,  DOI: 10.1038/s41565-023-01530-z
  245. 245
    Bray, D. Protein molecules as computational elements in living cells. Nature 1995, 376, 307312,  DOI: 10.1038/376307a0
  246. 246
    van Sluijs, B.; Zhou, T.; Helwig, B.; Baltussen, M. G.; Nelissen, F. H. T.; Heus, H. A.; Huck, W. T. S. Inverse design of enzymatic reaction network states. Nat. Commun. 2024, 15, 1602,  DOI: 10.1038/s41467-024-45886-9
  247. 247
    Zaikin, A. N.; Zhabotinsky, A. M. Concentration wave propagation in two-dimensional liquid-phase self-oscillating system. Nature 1970, 225, 535537,  DOI: 10.1038/225535b0

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  5. Wilhelm Huck, Miglė Jakštaitė, Tao Zhou, Frank Nelissen, Bob van Sluijs. Timed batch inputs unlock significantly higher yields for enzymatic cascades. 2025https://doi.org/10.21203/rs.3.rs-5917349/v1
  6. Ryan A. Herold, Christopher J. Schofield, Fraser A. Armstrong. Building Localized NADP(H) Recycling Circuits to Advance Enzyme Cascadetronics. Angewandte Chemie International Edition 2025, 64 (10) https://doi.org/10.1002/anie.202414176
  7. Ryan A. Herold, Christopher J. Schofield, Fraser A. Armstrong. Building Localized NADP(H) Recycling Circuits to Advance Enzyme Cascadetronics. Angewandte Chemie 2025, 137 (10) https://doi.org/10.1002/ange.202414176
  8. Clare F. Megarity, Ryan A. Herold, Fraser A. Armstrong. Extending protein-film electrochemistry across enzymology and biological inorganic chemistry to investigate, track and control the reactions of non-redox enzymes and spectroscopically silent metals. JBIC Journal of Biological Inorganic Chemistry 2025, 13 https://doi.org/10.1007/s00775-025-02105-0
  9. Ander de la Hoz, Patricio Caripán, Bernardo Moltedo, Nicole Ferrada, Rodrigo A. Contreras. A metabolomic approach of AI-driven enzymatic digestion of pumpkin seed flour for producing umami metabolites. International Journal of Gastronomy and Food Science 2025, 39 , 101117. https://doi.org/10.1016/j.ijgfs.2025.101117
  10. Cheng Lu, Ruijie Fang, Siyuan Tian, Mingzhu Hu, Jianan Wang, Jian Ding. Integrating protein contact networks for the engineering of thermostable lipase a. International Journal of Biological Macromolecules 2025, 5 , 141725. https://doi.org/10.1016/j.ijbiomac.2025.141725
  11. Mingyu Kim, Rahul Dev Mukhopadhyay, Kimoon Kim, Ilha Hwang. Spatiotemporal generation of alternating disparate pH domains via audible sound controlled opposing enzymatic reactions. Chemical Communications 2025, 61 (11) , 2321-2324. https://doi.org/10.1039/D4CC05850J
  12. Nikita M. Ivanov, Alexandar I. Slivkov, Wilhelm T. S. Huck. A Urease‐Based pH Photoswitch: A General Route to Light‐to‐pH Transduction. Angewandte Chemie International Edition 2025, 64 (3) https://doi.org/10.1002/anie.202415614
  13. Nikita M. Ivanov, Alexandar I. Slivkov, Wilhelm T. S. Huck. A Urease‐Based pH Photoswitch: A General Route to Light‐to‐pH Transduction. Angewandte Chemie 2025, 137 (3) https://doi.org/10.1002/ange.202415614
  14. Rif Harris, Nofar Berman, Ayala Lampel. Charge‐Mediated Interactions Affect Enzymatic Reactions in Peptide Condensates. ChemSystemsChem 2025, 7 (1) https://doi.org/10.1002/syst.202400055
  15. Masaki Itatani, Gábor Holló, Paola Albanese, Nadia Valletti, Sándor Kurunczi, Robert Horvath, Federico Rossi, István Lagzi. Temporal pH waveforms generated in an enzymatic reaction network in batch and cell-sized microcompartments. Cell Reports Physical Science 2025, 6 (1) , 102367. https://doi.org/10.1016/j.xcrp.2024.102367
  16. Rif Harris, Nofar Berman, Ayala Lampel. Coacervates as enzymatic microreactors. Chemical Society Reviews 2025, 55 https://doi.org/10.1039/D4CS01203H
  17. Attila K. Horváth, Qingyu Gao. Autoinhibition in (Bio)Chemistry: Identification and Mechanistic Classification. ChemBioChem 2024, 25 (24) https://doi.org/10.1002/cbic.202400505
  18. Leonardo S. G. Leite, Swarup Banerjee, Yihui Wei, Jackson Elowitt, Aurora E. Clark. Modern chemical graph theory. WIREs Computational Molecular Science 2024, 14 (5) https://doi.org/10.1002/wcms.1729
  19. Iuliia A. Poimenova, Madina M. Sozarukova, Daria-Maria V. Ratova, Vita N. Nikitina, Vladislav R. Khabibullin, Ivan V. Mikheev, Elena V. Proskurnina, Mikhail A. Proskurnin. Analytical Methods for Assessing Thiol Antioxidants in Biological Fluids: A Review. Molecules 2024, 29 (18) , 4433. https://doi.org/10.3390/molecules29184433
  20. Guido Panzarasa. Methylene glycol-sulfite pH-clocks for the time-programming of soft materials: advantages, limitations, and yet unexplored opportunities. Soft Matter 2024, 20 (31) , 6092-6102. https://doi.org/10.1039/D4SM00604F
  21. Wilhelm T. S. Huck. Oscillating chemical reaction networks stopped cold. Nature Chemical Engineering 2024, 1 (8) , 499-500. https://doi.org/10.1038/s44286-024-00092-8
  22. P. Jarne de Jong, Foteini Trigka, Michael M. Lerch. Towards Autonomous Materials–Challenges in Chemical Communication. ChemSystemsChem 2024, 6 (3) https://doi.org/10.1002/syst.202400036

Chemical Reviews

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  • Abstract

    Figure 1

    Figure 1. Overview of the topics discussed in the review. The image in “building synthetic cell” shows a cartoon representation of a synthetic cell (Credit: Graham Johnson/BaSyC consortium).

    Figure 2

    Figure 2. (a) A schematic representation of the modular organization of enzymatic networks in the cell. Strongly connected modules with their own specific functions share limited connections to different modules. In this network representation, enzymes are represented by nodes and substrates by edges. (b) An example of typical scaling relationships found in the connectedness of enzymes. Here, dots represent types of enzymes, with colors denoting different modules. k represents the number of connections an enzyme has in the ERN, while P(k) represents the frequency of that number of connections occurring. Inside cellular ERNs, these follow a power-law relationship P(k) ≈ k–1. (c) Examples of network motifs often encountered in ERNs. Again, enzymes are represented by nodes and substrates by edges. Edges with arrows indicate a positive interaction (e.g., a substrate that can be used as a reactant), while a flat end indicates an inhibitory effect. (d). Example of the difference in reaction velocity v as a function of substrate concentration between an enzyme with Michaelis–Menten (MM) kinetics and Hill-type kinetics with substrate affinity KM and turnover number kcat. (e) (Left) Example schematic of a full ERN where all interactions are included. (Right) A reduced model form where only essential interactions are maintained. (f) (Left) Bayesian analysis results in parameter estimate probability distributions instead of point estimates, allowing for nonsymmetric errors and standard deviations. (Right) Bayesian estimates can be used to generate a full probabilistic picture of possible enzymatic behavior.

    Figure 3

    Figure 3. Design of artificial ERNs with complex nonlinear dynamic behavior. (a) General design principle for ERNs with complex dynamic behavior. (b) Central reactions and motifs providing core nonlinearity. (c) Additional processes serving to alter the kinetics of the core motifs and connect different nodes. (d) Types of reactor or setup. (e) Examples of complex nonlinear dynamic behavior available in artificial ERNs. Illustrations: (e7) 50 μm polyacrylamide beads with immobilized urease in a solution containing 5 mM acetic buffer, 50 mM urea, and fluorescent dye SNARF-5,6, scale bar 100 μm, own data; (e8) redox forms of the dye ABTS in the solution containing glucose oxidase (GOx) and horseradish peroxidase (HRP). Adapted with permission from ref (78). Copyright 2018 Springer Nature.

    Figure 4

    Figure 4. Overview of enzymatic reaction networks that are controlled by different external stimuli. (a) Schematic representation of the trypsin oscillator where the enzyme activity is regulated by a photocleavable inhibitor. Adapted with permission from ref (86). Copyright 2020 John Wiley and Sons. (b) Photoswitchable inhibitor that can reversibly control the enzyme activity in an out-of-equilibrium system using light. Adapted with permission from ref (88). Copyright 2020 licensed under CC 4.0 American Chemical Society. (c) DASA-based polymersomes enable reversible control over the accessibility of substrate toward the active site of an enzyme by light. Adapted with permission from ref (93). Copyright 2022 licensed under CC 4.0 Springer Nature. (d) Ur–Est-based network where Ur increases the pH and Est decreases the pH. Adapted with permission from ref (65). Copyright 2021 John Wiley and sons. (e) Haber–Bosch process where NH3 can be generated from H2 and N2 using enzymes, hydrogenase, and nitrogenase by applying an electrochemical potential. Adapted with permission from ref (106). Copyright 2017 John Wiley and Sons. (f) Cascade network that is spatiotemporally controlled by sound. Adapted with permission from ref (111). Copyright 2022 licensed under CC 4.0 Springer Nature.

    Figure 5

    Figure 5. Overview of dynamic materials designed with enzymatic reaction networks. (a) Enzymatic polymerization of hydrogels. Adapted with permission from ref (140). Copyright 2016 licensed under CC 3.0 Royal Society of Chemistry. (b) Schematic representation of cross-link degradation of a hydrogel controlled by an enzymatic network. Adapted with permission from ref (146). Copyright 2017 John Wiley and sons.

    Figure 6

    Figure 6. Overview of enzyme-powered motile systems designed with enzymatic reaction networks. (a) Self-propulsion of stomatocytes by generating oxygen from glucose using an encapsulated metabolic enzymatic network. Adapted with permission from ref (162). Copyright 2022 American Chemical Society. (b) Illustration demonstrating expansion and contraction of a spring made of calcium alginate by antagonistic interaction of two enzymes, i.e., Ur and GOx. Adapted with permission from ref (169). Copyright 2021 Springer Nature.

    Figure 7

    Figure 7. Overview of communication in compartmentalized bioreactors facilitated by enzymatic reaction networks. (a) Communication between sender and receiver GUVs where the sender produced AMP which allosterically activated ERN within receiver GUVs. Adapted with permission from ref (179). Copyright 2020 licensed under CC 4.0 Springer Nature. (b) Cartoon representation of artificial response–retaliation behavior among protocell communities. In the presence of glucose, GOx containing protease-sensitive proteinosome (P) released acid (2) which disassembled pH-sensitive protease containing coacervate (CT) and released protease (3). Coacervate CK recaptured protease and eventually destroyed P. Adapted with permission from ref (175). Copyright 2019 John Wiley and sons.

    Figure 8

    Figure 8. (a) A diagram of low-cost substrate glucose being converted into pyruvate or acetyl-CoA as an intermediate and then depending on the selected pathway to isoprene, (199) monoterpenes, (200) PHB, (202) or cannabinoids (203) using purified enzymes (in orange) or to n-butanol, (205) mevalonate, (206) or limonene (208) using crude cell lysate (in yellow). (b) The purge valve concept, where the purge valve is “OFF” when there is low NADPH concentration and “ON” at high NADPH concentration. Adapted with permission from ref (201). Copyright 2014 Springer Nature.

    Figure 9

    Figure 9. Synthetic CO2 fixation pathways. (a) CETCH cycle converting CO2 to malate. Adapted with permission from ref (214). Copyright 2016 The American Association for the Advancement of Science. (b) POAP cycle converting CO2 to oxalate. Adapted with permission from ref (219). Copyright 2022 American Chemical Society. (c) Chemoenzymatic ASAS pathway converting CO2 to starch. Adapted with permission from ref (221). Copyright 2021 The American Association for the Advancement of Science. (d) Chemoenzymatic pathway converting CO2 to bioplastic polyhydroxybutyrate (PHB). Adapted with permission from ref (223). Copyright 2023 Royal Society of Chemistry.

    Figure 10

    Figure 10. (a) Schematic of the artificial organelle harvesting light to produce ATP and to drive endergonic reactions. Adapted with permission from ref (227). Copyright 2018 Springer Nature. (b) Schematic of self-constituting protein synthesis in artificial photosynthetic cells. Adapted with permission from ref (228). Copyright 2019 licensed under CC 4.0 Springer Nature. (c) Schematic of the production of phospholipids PE and PG by de novo-synthesized enzymes. Adapted with permission from ref (230). Copyright 2020 licensed under CC 4.0 Springer Nature.

    Figure 11

    Figure 11. (a) Schematic overview of commonly used ERN designs for two Boolean logic gates (AND and XOR). The computational output is based on an arbitrary threshold for the concentration of a product molecule. (b) A design for an ERN resulting in a multi-input perceptron, investigated in Pandi et al. (238) The design uses so-called transduction reactions to convert a range of different inputs into the same “transducer” substrate. Weights can be set by modifying enzyme concentrations. The final addition results in a roughly sigmoidal response. (c) Design for a chemical neuron, as investigated by Okumura et al. (239) A single neuron is constructed from specific DNA templates (curved lines) enabled by components of the polymerase–exonuclease–nickase (PEN) toolbox (shaded circle). (d) Design for a chemical multilayer perceptron. Individual neurons can be combined by replacing fluorescence-generating reporter strands by input strands for other neurons.

  • References


    This article references 247 other publications.

    1. 1
      Colin, R.; Sourjik, V. Emergent properties of bacterial chemotaxis pathway. Curr. Opin. Microbiol. 2017, 39, 2433,  DOI: 10.1016/j.mib.2017.07.004
    2. 2
      Typas, A.; Sourjik, V. Bacterial protein networks: properties and functions. Nat. Rev. Microbiol. 2015, 13 (9), 559572,  DOI: 10.1038/nrmicro3508
    3. 3
      Hunter, T. Protein Kinases and Phosphatases: The yin and yang of protein phosphorylation andsignaling. Cell 1995, 80, 225236,  DOI: 10.1016/0092-8674(95)90405-0
    4. 4
      Junttila, M. R.; Li, S. P.; Westermarck, J. Phosphatase-mediated crosstalk between MAPK signaling pathways in the regulation of cell survival. FASEB J. 2008, 22, 954965,  DOI: 10.1096/fj.06-7859rev
    5. 5
      Saravia, J.; Raynor, J. L.; Chapman, N. M.; Lim, S. A.; Chi, H. Signaling networks in immunometabolism. Cell Res. 2020, 30 (4), 328342,  DOI: 10.1038/s41422-020-0301-1
    6. 6
      Fink, T.; Lonzaric, J.; Praznik, A.; Plaper, T.; Merljak, E.; Leben, K.; Jerala, N.; Lebar, T.; Strmsek, Z.; Lapenta, F. Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat. Chem. Biol. 2019, 15 (2), 115122,  DOI: 10.1038/s41589-018-0181-6
    7. 7
      Feng, Z.; Wang, H.; Wang, F.; Oh, Y.; Berciu, C.; Cui, Q.; Egelman, E. H.; Xu, B. Artificial intracellular filaments. Cell Rep. Phys. Sci. 2020, 1 (7), 100085,  DOI: 10.1016/j.xcrp.2020.100085
    8. 8
      Kohyama, S.; Merino-Salomon, A.; Schwille, P. In vitro assembly, positioning and contraction of a division ring in minimal cells. Nat. Commun. 2022, 13 (1), 6098,  DOI: 10.1038/s41467-022-33679-x
    9. 9
      Fisher, A. K.; Freedman, B. G.; Bevan, D. R.; Senger, R. S. A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput. Struct. Biotechnol. J. 2014, 11 (18), 9199,  DOI: 10.1016/j.csbj.2014.08.010
    10. 10
      Hirschi, S.; Ward, T. R.; Meier, W. P.; Muller, D. J.; Fotiadis, D. Synthetic biology: bottom-up assembly of molecular systems. Chem. Rev. 2022, 122 (21), 1629416328,  DOI: 10.1021/acs.chemrev.2c00339
    11. 11
      Boguñá, M.; Bonamassa, I.; De Domenico, M.; Havlin, S.; Krioukov, D.; Serrano, M. Á. Network geometry. Nat. Rev. Phys. 2021, 3 (2), 114135,  DOI: 10.1038/s42254-020-00264-4
    12. 12
      Ravasz, E.; Somera, A. L.; Mongru, D. A.; Oltvai, Z. N.; Barabási, A.-L. Hierarchical organization of modularity in metabolic networks. Science 2002, 297, 15511555,  DOI: 10.1126/science.1073374
    13. 13
      Barabási, A.-L.; Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 2004, 5 (2), 101113,  DOI: 10.1038/nrg1272
    14. 14
      Serrano, M. A.; Boguñá, M.; Sagués, F. Uncovering the hidden geometry behind metabolic networks. Mol. Biosyst. 2012, 8 (3), 843850,  DOI: 10.1039/c2mb05306c
    15. 15
      Kim, H.; Smith, H. B.; Mathis, C.; Raymond, J.; Walker, S. Universal scaling across biochemical networks on Earth. Sci. Adv. 2019, 5, eaau0149,  DOI: 10.1126/sciadv.aau0149
    16. 16
      Gagler, D. C.; Karas, B.; Kempes, C. P.; Malloy, J.; Mierzejewski, V.; Goldman, A. D.; Kim, H.; Walker, S. I. Scaling laws in enzyme function reveal a new kind of biochemical universality. Proc. Natl. Acad. Sci. U. S. A. 2022, 119 (9), e2106655119,  DOI: 10.1073/pnas.2106655119
    17. 17
      Guell, O.; Sagues, F.; Serrano, M. A. Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis. PLoS Comput. Biol. 2014, 10 (5), e1003637,  DOI: 10.1371/journal.pcbi.1003637
    18. 18
      Sambamoorthy, G.; Raman, K. Understanding the evolution of functional redundancy in metabolic networks. Bioinformatics 2018, 34 (17), i981i987,  DOI: 10.1093/bioinformatics/bty604
    19. 19
      Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 2007, 8, 450461,  DOI: 10.1038/nrg2102
    20. 20
      Goldbeter, A.; Koshland, D. E. An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl. Acad. Sci. U. S. A. 1981, 78, 68406844,  DOI: 10.1073/pnas.78.11.6840
    21. 21
      Koshland, D. E., Jr.; Goldbeter, A.; Stock, J. B. Amplification and adaptation in regulatory and sensory systems. Science 1982, 217, 220225,  DOI: 10.1126/science.7089556
    22. 22
      Barkai, N.; Leibler, S. Robustness in simple biochemical networks. Nature 1997, 387, 913917,  DOI: 10.1038/43199
    23. 23
      Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U. Network motifs: simple building blocks of complex networks. Science. 2002, 298, 824827,  DOI: 10.1126/science.298.5594.824
    24. 24
      Tyson, J. J.; Novak, B. Functional motifs in biochemical reaction networks. Annu. Rev. Phys. Chem. 2010, 61, 219240,  DOI: 10.1146/annurev.physchem.012809.103457
    25. 25
      Novak, B.; Tyson, J. J. Design principles of biochemical oscillators. Nat. Rev. Mol. Cell Biol. 2008, 9 (12), 981991,  DOI: 10.1038/nrm2530
    26. 26
      Araujo, R. P.; Liotta, L. A. Universal structures for adaptation in biochemical reaction networks. Nat. Commun. 2023, 14 (1), 2251,  DOI: 10.1038/s41467-023-38011-9
    27. 27
      Shellman, E. R.; Burant, C. F.; Schnell, S. Network motifs provide signatures that characterize metabolism. Mol. Biosyst. 2013, 9 (3), 352360,  DOI: 10.1039/c2mb25346a
    28. 28
      Beber, M. E.; Fretter, C.; Jain, S.; Sonnenschein, N.; Muller-Hannemann, M.; Hutt, M. T. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks. J. R. Soc. Interface 2012, 9 (77), 34263435,  DOI: 10.1098/rsif.2012.0490
    29. 29
      Piephoff, D. E.; Wu, J.; Cao, J. Conformational nonequilibrium enzyme kinetics: generalized Michaelis-Menten equation. J. Phys. Chem. Lett. 2017, 8 (15), 36193623,  DOI: 10.1021/acs.jpclett.7b01210
    30. 30
      Rohwer, J. M.; Hanekom, A. J.; Crous, C.; Snoep, J. L.; Hofmeyr, J. H. Evaluation of a simplified generic bi-substrate rate equation for computational systems biology. Syst. Biol. (Stevenage) 2006, 153 (5), 33841,  DOI: 10.1049/ip-syb:20060026
    31. 31
      Transtrum, M. K.; Machta, B. B.; Brown, K. S.; Daniels, B. C.; Myers, C. R.; Sethna, J. P. Perspective: sloppiness and emergent theories in physics, biology, and beyond. J. Chem. Phys. 2015, 143 (1), 010901  DOI: 10.1063/1.4923066
    32. 32
      Gutenkunst, R. N.; Waterfall, J. J.; Casey, F. P.; Brown, K. S.; Myers, C. R.; Sethna, J. P. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 2007, 3 (10), e189,  DOI: 10.1371/journal.pcbi.0030189
    33. 33
      Transtrum, M. K.; Qiu, P. Model reduction by manifold boundaries. Phys. Rev. Lett. 2014, 113 (9), 098701  DOI: 10.1103/PhysRevLett.113.098701
    34. 34
      van de Schoot, R.; Depaoli, S.; King, R.; Kramer, B.; Märtens, K.; Tadesse, M. G.; Vannucci, M.; Gelman, A.; Veen, D.; Willemsen, J.; Yau, C. Bayesian statistics and modelling. Nat. Rev. Methods Primers 2021, 1, 16,  DOI: 10.1038/s43586-020-00001-2
    35. 35
      Choi, B.; Rempala, G. A.; Kim, J. K. Beyond the Michaelis-Menten equation: accurate and efficient estimation of enzyme kinetic parameters. Sci. Rep. 2017, 7 (1), 17018,  DOI: 10.1038/s41598-017-17072-z
    36. 36
      Linden, N. J.; Kramer, B.; Rangamani, P. Bayesian parameter estimation for dynamical models in systems biology. PLoS Comput. Biol. 2022, 18 (10), e1010651,  DOI: 10.1371/journal.pcbi.1010651
    37. 37
      Baltussen, M. G.; van de Wiel, J.; Fernandez Regueiro, C. L.; Jakstaite, M.; Huck, W. T. S. A Bayesian Approach to Extracting kinetic information from artificial enzymatic networks. Anal. Chem. 2022, 94 (20), 73117318,  DOI: 10.1021/acs.analchem.2c00659
    38. 38
      Gábor, A.; Villaverde, A. F.; Banga, J. R. Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC Syst. Biol. 2017, 11 (1), 54,  DOI: 10.1186/s12918-017-0428-y
    39. 39
      Villaverde, A. F.; Evans, N. D.; Chappell, M. J.; Banga, J. R. Input-dependent structural identifiability of nonlinear systems. IEEE Control Systems Letters 2019, 3 (2), 272277,  DOI: 10.1109/LCSYS.2018.2868608
    40. 40
      van Sluijs, B.; Maas, R. J. M.; van der Linden, A. J.; de Greef, T. F. A.; Huck, W. T. S. A microfluidic optimal experimental design platform for forward design of cell-free genetic networks. Nat. Commun. 2022, 13 (1), 3626,  DOI: 10.1038/s41467-022-31306-3
    41. 41
      Hold, C.; Billerbeck, S.; Panke, S. Forward design of a complex enzyme cascade reaction. Nat. Commun. 2016, 7, 12971,  DOI: 10.1038/ncomms12971
    42. 42
      Wong, A. S. Y.; Huck, W. T. S. Grip on complexity in chemical reaction networks. Beilstein J. Org. Chem. 2017, 13, 14861497,  DOI: 10.3762/bjoc.13.147
    43. 43
      Hanopolskyi, A. I.; Smaliak, V. A.; Novichkov, A. I.; Semenov, S. N. Autocatalysis: Kinetics, mechanisms and design. ChemSystemsChem. 2021, 3 (1), e2000026,  DOI: 10.1002/syst.202000026
    44. 44
      Bánsági, T.; Taylor, A. F. Exploitation of feedback in enzyme-catalysed reactions. Isr. J. Chem. 2018, 58 (6–7), 706713,  DOI: 10.1002/ijch.201700141
    45. 45
      Plasson, R.; Brandenburg, A.; Jullien, L.; Bersini, H. Autocatalyses. J. Phys. Chem. A 2011, 115 (28), 80738085,  DOI: 10.1021/jp110079p
    46. 46
      Budroni, M. A.; Rossi, F.; Rongy, L. From transport phenomena to systems chemistry: chemohydrodynamic oscillations in A+B⃗C systems. ChemSystemsChem. 2022, 4 (1), e202100023,  DOI: 10.1002/syst.202100023
    47. 47
      Menon, G.; Krishnan, J. Spatial localisation meets biomolecular networks. Nat. Commun. 2021, 12 (1), 5357,  DOI: 10.1038/s41467-021-24760-y
    48. 48
      Sharma, C.; Maity, I.; Walther, A. pH-feedback systems to program autonomous self-assembly and material lifecycles. Chem. Commun. 2023, 59, 11251144,  DOI: 10.1039/D2CC06402B
    49. 49
      Dúzs, B.; Molnár, I.; Lagzi, I.; Szalai, I. Reaction–diffusion dynamics of pH oscillators in oscillatory forced open spatial reactors. ACS Omega 2021, 6 (50), 3436734374,  DOI: 10.1021/acsomega.1c04269
    50. 50
      Duzs, B.; Lagzi, I.; Szalai, I. Functional rhythmic chemical systems governed by pH-driven kinetic feedback. ChemSystemsChem 2023, 5, e202200032,  DOI: 10.1002/syst.202200032
    51. 51
      Bubanja, I. N.; Bánsági, T.; Taylor, A. F. Kinetics of the urea–urease clock reaction with urease immobilized in hydrogel beads. Reac. Kinet. Mech. Catal. 2018, 123 (1), 177185,  DOI: 10.1007/s11144-017-1296-6
    52. 52
      Miele, Y.; Jones, S. J.; Rossi, F.; Beales, P. A.; Taylor, A. F. Collective behavior of urease pH clocks in nano- and microvesicles controlled by fast ammonia transport. J. Phys. Chem. Lett. 2022, 13 (8), 19791984,  DOI: 10.1021/acs.jpclett.2c00069
    53. 53
      Markovic, V. M.; Bánsági, T., Jr.; McKenzie, D.; Mai, A.; Pojman, J. A.; Taylor, A. F. Influence of reaction-induced convection on quorum sensing in enzyme-loaded agarose beads. Chaos 2019, 29 (3), 033130  DOI: 10.1063/1.5089295
    54. 54
      Muzika, F.; RuZicka, M.; Schreiberova, L.; Schreiber, I. Oscillations of pH in the urea-urease system in a membrane reactor. Phys. Chem. Chem. Phys. 2019, 21 (17), 86198622,  DOI: 10.1039/C9CP00630C
    55. 55
      Muzika, F.; Bánsági, T., Jr.; Schreiber, I.; Schreiberova, L.; Taylor, A. F. A bistable switch in pH in urease-loaded alginate beads. Chem. Commun. 2014, 50 (76), 1110711109,  DOI: 10.1039/C4CC03936J
    56. 56
      Hu, G.; Pojman, J. A.; Scott, S. K.; Wrobel, M. M.; Taylor, A. F. Base-catalyzed feedback in the urea-urease reaction. J. Phys. Chem. B 2010, 114 (44), 1405914063,  DOI: 10.1021/jp106532d
    57. 57
      Straube, A. V.; Winkelmann, S.; Schütte, C.; Höfling, F. Stochastic pH oscillations in a model of the urea-urease reaction confined to lipid vesicles. J. Phys. Chem. Lett. 2021, 12 (40), 98889893,  DOI: 10.1021/acs.jpclett.1c03016
    58. 58
      Bánsági, T.; Taylor, A. F. Switches induced by quorum sensing in a model of enzyme-loaded microparticles. J. R. Soc. Interface. 2018, 15, 20170945,  DOI: 10.1098/rsif.2017.0945
    59. 59
      Heinen, L.; Heuser, T.; Steinschulte, A.; Walther, A. Antagonistic enzymes in a biocatalytic pH feedback system program autonomous DNA hydrogel life cycles. Nano Lett. 2017, 17 (8), 49894995,  DOI: 10.1021/acs.nanolett.7b02165
    60. 60
      Fan, X.; Walther, A. Autonomous transient pH flips shaped by layered compartmentalization of antagonistic enzymatic reactions. Angew. Chem., Int. Ed. 2021, 60, 36193624,  DOI: 10.1002/anie.202009542
    61. 61
      Wang, X.; Moreno, S.; Boye, S.; Wen, P.; Zhang, K.; Formanek, P.; Lederer, A.; Voit, B.; Appelhans, D. Feedback-induced and oscillating pH regulation of a binary enzyme–polymersomes. System. Chem. Mater. 2021, 33 (17), 66926700,  DOI: 10.1021/acs.chemmater.1c00897
    62. 62
      Dúzs, B.; Lagzi, I.; Szalai, I. Functional rhythmic chemical systems governed by pH-Driven kinetic feedback. ChemSystemsChem 2023, 5 (2), e202200032,  DOI: 10.1002/syst.202200032
    63. 63
      Jee, E.; Bánsági, T., Jr.; Taylor, A. F.; Pojman, J. A. Temporal control of gelation and polymerization fronts driven by an autocatalytic enzyme reaction. Angew. Chem., Int. Ed. 2016, 55 (6), 21272131,  DOI: 10.1002/anie.201510604
    64. 64
      Mai, A. Q.; Bánsági, T., Jr.; Taylor, A. F.; Pojman, J. A., Sr. Reaction-diffusion hydrogels from urease enzyme particles for patterned coatings. Commun. Chem. 2021, 4 (1), 101,  DOI: 10.1038/s42004-021-00538-7
    65. 65
      Maity, I.; Sharma, C.; Lossada, F.; Walther, A. Feedback and communication in active hydrogel spheres with pH fronts: facile approaches to grow soft hydrogel structures. Angew. Chem., Int. Ed. 2021, 60 (41), 2253722546,  DOI: 10.1002/anie.202109735
    66. 66
      Rauner, N.; Buenger, L.; Schuller, S.; Tiller, J. C. Post-polymerization of urease-induced calcified, polymer hydrogels. Macromol. Rapid Commun. 2015, 36 (2), 224230,  DOI: 10.1002/marc.201400426
    67. 67
      Semenov, S. N.; Wong, A. S.; van der Made, R. M.; Postma, S. G.; Groen, J.; van Roekel, H. W.; de Greef, T. F.; Huck, W. T. Rational design of functional and tunable oscillating enzymatic networks. Nat. Chem. 2015, 7 (2), 160165,  DOI: 10.1038/nchem.2142
    68. 68
      Maguire, O. R.; Wong, A. S. Y.; Westerdiep, J. H.; Huck, W. T. S. Early warning signals in chemical reaction networks. Chem. Commun. 2020, 56 (26), 37253728,  DOI: 10.1039/D0CC01010C
    69. 69
      Maguire, O. R.; Wong, A. S. Y.; Baltussen, M. G.; van Duppen, P.; Pogodaev, A. A.; Huck, W. T. S. Dynamic environments as a tool to preserve desired output in a chemical reaction network. Chem. Eur. J. 2020, 26, 16761682,  DOI: 10.1002/chem.201904725
    70. 70
      Helwig, B.; van Sluijs, B.; Pogodaev, A. A.; Postma, S. G. J.; Huck, W. T. S. Bottom-up construction of an adaptive enzymatic reaction network. Angew. Chem., Int. Ed. 2018, 57 (43), 1406514069,  DOI: 10.1002/anie.201806944
    71. 71
      Semenov, S. N.; Markvoort, A. J.; de Greef, T. F.; Huck, W. T. Threshold sensing through a synthetic enzymatic reaction-diffusion network. Angew. Chem., Int. Ed. 2014, 53 (31), 80668069,  DOI: 10.1002/anie.201402327
    72. 72
      Kriukov, D. V.; Koyuncu, A. H.; Wong, A. S. Y. History dependence in a chemical reaction network enables dynamic switching. Small 2022, 18 (16), 2107523,  DOI: 10.1002/smll.202107523
    73. 73
      Pogodaev, A. A.; Fernández Regueiro, C. L.; Jakštaitė, M.; Hollander, M. J.; Huck, W. T. S. Modular design of small enzymatic reaction networks based on reversible and cleavable inhibitors. Angew. Chem., Int. Ed. 2019, 58 (41), 1453914543,  DOI: 10.1002/anie.201907995
    74. 74
      Yamazaki, I.; Yokota, K.; Nakajima, R. Oscillatory oxidations of reduced pyridine nucleotide by peroxidase. Biochem. Biophys. Res. Commun. 1965, 21 (6), 5826,  DOI: 10.1016/0006-291X(65)90525-5
    75. 75
      Folke Olsen, L. Complex dynamics in an unexplored simple model of the peroxidase-oxidase reaction. Chaos 2023, 33 (2), 023102  DOI: 10.1063/5.0129095
    76. 76
      Olsen, L. F.; Lunding, A. Chaos in the peroxidase-oxidase oscillator. Chaos 2021, 31 (1), 013119  DOI: 10.1063/5.0022251
    77. 77
      Scheeline, A.; Olson, D. L.; Williksen, E. P.; Horras, G. A.; Klein, M. L.; Larter, R. The peroxidase–oxidase oscillator and its constituent chemistries. Chem. Rev. 1997, 97 (3), 739756,  DOI: 10.1021/cr960081a
    78. 78
      Zhang, Y.; Tsitkov, S.; Hess, H. Complex dynamics in a two-enzyme reaction network with substrate competition. Nat. Catal. 2018, 1 (4), 276281,  DOI: 10.1038/s41929-018-0053-1
    79. 79
      Gyevi-Nagy, L.; Lantos, E.; Gehér-Herczegh, T.; Tóth, A.; Bagyinka, C.; Horváth, D. Reaction fronts of the autocatalytic hydrogenase reaction. J. Chem. Phys. 2018, 148 (16), 165103,  DOI: 10.1063/1.5022359
    80. 80
      Bagyinka, C.; Pankotai-Bodó, G.; Branca, R. M. M.; Debreczeny, M. Oscillating hydrogenase reaction. Int. J. Hydrogen Energy 2014, 39 (32), 1855118555,  DOI: 10.1016/j.ijhydene.2014.02.015
    81. 81
      Claaßen, C.; Gerlach, T.; Rother, D. Stimulus-responsive regulation of enzyme activity for one-step and multi-step syntheses. Adv. Synth. Catal. 2019, 361 (11), 23872401,  DOI: 10.1002/adsc.201900169
    82. 82
      Chen, Z.; Zhao, Y.; Liu, Y. Advanced strategies of enzyme activity regulation for biomedical applications. ChemBioChem 2022, 23 (21), e202200358,  DOI: 10.1002/cbic.202200358
    83. 83
      Hoorens, M. W. H.; Szymanski, W. Reversible, spatial and temporal control over protein activity using light. Trends Biochem. Sci. 2018, 43 (8), 567575,  DOI: 10.1016/j.tibs.2018.05.004
    84. 84
      Kneuttinger, A. C. A guide to designing photocontrol in proteins: methods, strategies and applications. Biol. Chem. 2022, 403 (5–6), 573613,  DOI: 10.1515/hsz-2021-0417
    85. 85
      Volarić, J.; Szymanski, W.; Simeth, N. A.; Feringa, B. L. Molecular photoswitches in aqueous environments. Chem. Soc. Rev. 2021, 50 (22), 1237712449,  DOI: 10.1039/D0CS00547A
    86. 86
      Pogodaev, A. A.; Lap, T. T.; Huck, W. T. S. The dynamics of an oscillating enzymatic reaction network is crucially determined by side reactions. ChemSystemsChem 2021, 3, e2000033,  DOI: 10.1002/syst.202000033
    87. 87
      Crespi, S.; Simeth, N. A.; König, B. Heteroaryl azo dyes as molecular photoswitches. Nat. Rev. Chem. 2019, 3, 133146,  DOI: 10.1038/s41570-019-0074-6
    88. 88
      Teders, M.; Pogodaev, A. A.; Bojanov, G.; Huck, W. T. S. Reversible photoswitchable inhibitors generate ultrasensitivity in out-of-equilibrium enzymatic reactions. J. Am. Chem. Soc. 2021, 143 (15), 57095716,  DOI: 10.1021/jacs.0c12956
    89. 89
      Teders, M.; Murray, N. R.; Huck, W. T. S. Reversible photoswitchable inhibitors enable wavelength-selective regulation of out-of-equilibrium bi-enzymatic systems. ChemSystemsChem 2021, 3 (6), e2100020,  DOI: 10.1002/syst.202100020
    90. 90
      Li, K.; Liu, M. D.; Huang, Q. X.; Liu, C. J.; Zhang, X. Z. Nanoplatforms with donor-acceptor Stenhouse adduct molecular switch for enzymatic reactions remotely controlled with near-infrared light. Sci. China Mater. 2023, 66, 375384,  DOI: 10.1007/s40843-022-2107-0
    91. 91
      Hindley, J. W.; Elani, Y.; McGilvery, C. M.; Ali, S.; Bevan, C. L.; Law, R. V.; Ces, O. Light-triggered enzymatic reactions in nested vesicle reactors. Nat. Commun. 2018, 9 (1), 1093,  DOI: 10.1038/s41467-018-03491-7
    92. 92
      Babii, O.; Afonin, S.; Diel, C.; Huhn, M.; Dommermuth, J.; Schober, T.; Koniev, S.; Hrebonkin, A.; Nesterov-Mueller, A.; Komarov, I. V. Diarylethene-based photoswitchable inhibitors of serine proteases. Angew. Chem., Int. Ed. 2021, 60 (40), 2178921794,  DOI: 10.1002/anie.202108847
    93. 93
      Rifaie-Graham, O.; Yeow, J.; Najer, A.; Wang, R.; Sun, R.; Zhou, K.; Dell, T. N.; Adrianus, C.; Thanapongpibul, C.; Chami, M. Photoswitchable gating of non-equilibrium enzymatic feedback in chemically communicating polymersome nanoreactors. Nat. Chem. 2023, 15 (1), 110118,  DOI: 10.1038/s41557-022-01062-4
    94. 94
      Bisswanger, H. Enzyme assays. Perspectives in Science 2014, 1 (1–6), 4155,  DOI: 10.1016/j.pisc.2014.02.005
    95. 95
      Zhang, Y.; Wang, Q.; Hess, H. Increasing enzyme cascade throughput by pH-engineering the microenvironment of individual enzymes. ACS Catal. 2017, 7 (3), 20472051,  DOI: 10.1021/acscatal.6b03431
    96. 96
      Fan, X.; Walther, A. pH feedback lifecycles programmed by enzymatic logic gates using common foods as fuels. Angew. Chem., Int. Ed. 2021, 60, 11398,  DOI: 10.1002/anie.202017003
    97. 97
      Che, H.; Cao, S.; van Hest, J. C. M. Feedback-induced temporal control of ″breathing″ polymersomes to create self-adaptive nanoreactors. J. Am. Chem. Soc. 2018, 140 (16), 53565359,  DOI: 10.1021/jacs.8b02387
    98. 98
      Wells, P. K.; Smutok, O.; Melman, A.; Katz, E. Switchable biocatalytic reactions controlled by interfacial pH changes produced by orthogonal biocatalytic processes. ACS Appl. Mater. Interfaces 2021, 13 (29), 3383033839,  DOI: 10.1021/acsami.1c07393
    99. 99
      Wang, C.; Fischer, A.; Ehrlich, A.; Nahmias, Y.; Willner, I. Biocatalytic reversible control of the stiffness of DNA-modified responsive hydrogels: applications in shape-memory, self-healing and autonomous controlled release of insulin. Chem. Sci. 2020, 11 (17), 45164524,  DOI: 10.1039/D0SC01319F
    100. 100
      Zhang, Y.; Nie, N.; Wang, H.; Tong, Z.; Xing, H.; Zhang, Y. Smart enzyme catalysts capable of self-separation by sensing the reaction extent. Biosens. Bioelectron. 2023, 239, 115585,  DOI: 10.1016/j.bios.2023.115585
    101. 101
      Jain, M.; Ravoo, B. J. Fuel-driven and enzyme-regulated redox-responsive supramolecular hydrogels. Angew. Chem., Int. Ed. 2021, 60 (38), 2106221068,  DOI: 10.1002/anie.202107917
    102. 102
      Eixelsberger, T.; Nidetzky, B. Enzymatic redox cascade for one-pot synthesis of uridine 5′-diphosphate xylose from uridine 5′-diphosphate glucose. Adv. Synth. Catal. 2014, 356 (17), 35753584,  DOI: 10.1002/adsc.201400766
    103. 103
      Kuk, S. K.; Singh, R. K.; Nam, D. H.; Singh, R.; Lee, J. K.; Park, C. B. Photoelectrochemical reduction of carbon dioxide to methanol through a highly efficient enzyme cascade. Angew. Chem., Int. Ed. 2017, 56 (14), 38273832,  DOI: 10.1002/anie.201611379
    104. 104
      Mallawarachchi, S.; Gejji, V.; Sierra, L. S.; Wang, H.; Fernando, S. Electrical field reversibly modulates enzyme kinetics of hexokinase entrapped in an electro-responsive hydrogel. ACS Appl. Bio Mater. 2019, 2 (12), 56765686,  DOI: 10.1021/acsabm.9b00748
    105. 105
      Morello, G.; Megarity, C. F.; Armstrong, F. A. The power of electrified nanoconfinement for energising, controlling and observing long enzyme cascades. Nat. Commun. 2021, 12 (1), 340,  DOI: 10.1038/s41467-020-20403-w
    106. 106
      Milton, R. D.; Cai, R.; Abdellaoui, S.; Leech, D.; De Lacey, A. L.; Pita, M.; Minteer, S. D. Bioelectrochemical Haber-Bosch process: an ammonia-producing H2/N2 fuel cell. Angew. Chem., Int. Ed. 2017, 56 (10), 26802683,  DOI: 10.1002/anie.201612500
    107. 107
      Cao, Y.; Wang, Y. Temperature-mediated regulation of enzymatic activity. ChemCatChem. 2016, 8 (17), 27402747,  DOI: 10.1002/cctc.201600406
    108. 108
      Zhang, S.; Wang, C.; Chang, H.; Zhang, Q.; Cheng, Y. Off-on switching of enzyme activity by near-infrared light-induced photothermal phase transition of nanohybrids. Sci. Adv. 2019, 5, eaaw4252,  DOI: 10.1126/sciadv.aaw4252
    109. 109
      Gobbo, P.; Patil, A. J.; Li, M.; Harniman, R.; Briscoe, W. H.; Mann, S. Programmed assembly of synthetic protocells into thermoresponsive prototissues. Nat. Mater. 2018, 17 (12), 11451153,  DOI: 10.1038/s41563-018-0183-5
    110. 110
      Szekeres, K.; Bollella, P.; Kim, Y.; Minko, S.; Melman, A.; Katz, E. Magneto-controlled enzyme activity with locally produced pH changes. J. Phys. Chem. Lett. 2021, 12 (10), 25232527,  DOI: 10.1021/acs.jpclett.1c00036
    111. 111
      Dhasaiyan, P.; Ghosh, T.; Lee, H. G.; Lee, Y.; Hwang, I.; Mukhopadhyay, R. D.; Park, K. M.; Shin, S.; Kang, I. S.; Kim, K. Cascade reaction networks within audible sound induced transient domains in a solution. Nat. Commun. 2022, 13 (1), 2372,  DOI: 10.1038/s41467-022-30124-x
    112. 112
      Küchler, A.; Yoshimoto, M.; Luginbühl, S.; Mavelli, F.; Walde, P. Enzymatic reactions in confined environments. Nat. Nanotechnol. 2016, 11 (5), 409420,  DOI: 10.1038/nnano.2016.54
    113. 113
      Hwang, E. T.; Lee, S. Multienzymatic Cascade Reactions Enzyme complex by immobilization. ACS Catal. 2019, 9 (5), 44024425,  DOI: 10.1021/acscatal.8b04921
    114. 114
      Ji, Q.; Wang, B.; Tan, J.; Zhu, L.; Li, L. Immobilized multienzymatic systems for catalysis of cascade reactions. Process Biochemistry 2016, 51 (9), 11931203,  DOI: 10.1016/j.procbio.2016.06.004
    115. 115
      Kazenwadel, F.; Franzreb, M.; Rapp, B. E. Synthetic enzyme supercomplexes: co-immobilization of enzyme cascades. Anal. Methods 2015, 7 (10), 40304037,  DOI: 10.1039/C5AY00453E
    116. 116
      Xu, K.; Chen, X.; Zheng, R.; Zheng, Y. Immobilization of multi-enzymes on support materials for efficient biocatalysis. Front. Bioeng. Biotechnol. 2020, 8, 660,  DOI: 10.3389/fbioe.2020.00660
    117. 117
      Bié, J.; Sepodes, B.; Fernandes, P. C. B.; Ribeiro, M. H. L. Enzyme immobilization and co-immobilization: main framework, advances and some applications. Processes 2022, 10 (3), 494,  DOI: 10.3390/pr10030494
    118. 118
      Benitez-Mateos, A. I.; Roura Padrosa, D.; Paradisi, F. Multistep enzyme cascades as a route towards green and sustainable pharmaceutical syntheses. Nat. Chem. 2022, 14 (5), 489499,  DOI: 10.1038/s41557-022-00931-2
    119. 119
      Liang, J.; Liang, K. Multi-enzyme cascade reactions in metal-organic frameworks. Chem. Rec. 2020, 20 (10), 11001116,  DOI: 10.1002/tcr.202000067
    120. 120
      Tsitkov, S.; Hess, H. Design principles for a compartmentalized enzyme cascade reaction. ACS Catal. 2019, 9, 24322439,  DOI: 10.1021/acscatal.8b04419
    121. 121
      Oh, W.; Jeong, D.; Park, J.-W. An Artificial compartmentalized biocatalytic cascade system constructed with enzyme-caged reticulate nanoporous membranes. Adv. Mater. Interfaces 2023, 10, 2300185,  DOI: 10.1002/admi.202300185
    122. 122
      Diamanti, E.; Andrés-Sanz, D.; Orrego, A. H.; Carregal-Romero, S.; López-Gallego, F. Surpassing substrate–enzyme competition by compartmentalization. ACS Catal. 2023, 13 (17), 1144111454,  DOI: 10.1021/acscatal.3c01965
    123. 123
      Aumiller, W. M., Jr.; Uchida, M.; Douglas, T. Protein cage assembly across multiple length scales. Chem. Soc. Rev. 2018, 47, 34333469,  DOI: 10.1039/C7CS00818J
    124. 124
      Jaekel, A.; Stegemann, P.; Saccà, B. Manipulating enzymes properties with DNA nanostructures. Molecules 2019, 24 (20), 3694,  DOI: 10.3390/molecules24203694
    125. 125
      Jiao, Y.; Shang, Y.; Li, N.; Ding, B. DNA-based enzymatic systems and their applications. iScience 2022, 25, 104018,  DOI: 10.1016/j.isci.2022.104018
    126. 126
      Engelen, W.; Janssen, B. M. G.; Merkx, M. DNA-based control of protein activity. Chem. Commun. 2016, 52, 35983610,  DOI: 10.1039/C5CC09853J
    127. 127
      Fu, J.; Wang, Z.; Liang, X. H.; Oh, S. W.; St. Iago-McRae, E.; Zhang, T. DNA-scaffolded proximity assembly and confinement of multienzyme reactions. Top. Curr. Chem. 2020, 378, 38,  DOI: 10.1007/s41061-020-0299-3
    128. 128
      Kröll, S.; Niemeyer, C. M. Nucleic acid-based enzyme cascades─current trends and future perspectives. Angew. Chem., Int. Ed. 2024, 63, e202314452,  DOI: 10.1002/anie.202314452
    129. 129
      Liang, J.; Liang, K. Multi-enzyme cascade reactions in metal-organic frameworks. Chem. Rec. 2020, 20, 1100,  DOI: 10.1002/tcr.202000067
    130. 130
      Dhakshinamoorthy, A.; Asiri, A. M.; Garcia, H. Integration of metal organic frameworks with enzymes as multifunctional solids for cascade catalysis. Dalton Trans. 2020, 49, 1105911072,  DOI: 10.1039/D0DT02045A
    131. 131
      Wang, X.; Lan, P. C.; Ma, S. Metal–organic frameworks for enzyme immobilization: beyond host matrix materials. ACS Cent. Sci. 2020, 6 (9), 14971506,  DOI: 10.1021/acscentsci.0c00687
    132. 132
      Lian, X.; Fang, Y.; Joseph, E.; Wang, Q.; Li, J.; Banerjee, S.; Lollar, C.; Wang, X.; Zhou, H.-C. Enzyme–MOF (metal–organic framework) composites. Chem. Soc. Rev. 2017, 46, 33863401,  DOI: 10.1039/C7CS00058H
    133. 133
      Liang, W.; Wied, P.; Carraro, F.; Sumby, C. J.; Nidetzky, B.; Tsung, C.-K.; Falcaro, P.; Doonan, C. J. Metal–organic framework-based enzyme biocomposites. Chem. Rev. 2021, 121 (3), 10771129,  DOI: 10.1021/acs.chemrev.0c01029
    134. 134
      Hu, J.; Zhang, G.; Liu, S. Enzyme-responsive polymeric assemblies, nanoparticles and hydrogels. Chem. Soc. Rev. 2012, 41 (18), 59335949,  DOI: 10.1039/c2cs35103j
    135. 135
      Li, P.; Zhong, Y.; Wang, X.; Hao, J. Enzyme-regulated healable polymeric hydrogels. ACS Cent. Sci. 2020, 6 (9), 15071522,  DOI: 10.1021/acscentsci.0c00768
    136. 136
      Amir, R. J.; Zhong, S.; Pochan, D. J.; Hawker, C. J. Enzymatically triggered self-assembly of block copolymers. J. Am. Chem. Soc. 2009, 131 (39), 1394913951,  DOI: 10.1021/ja9060917
    137. 137
      Kobayashi, S.; Uyama, H.; Kimura, S. Enzymatic polymerization. Chem. Rev. 2001, 101 (12), 37933818,  DOI: 10.1021/cr990121l
    138. 138
      Klemperer, R. G.; Shannon, M. R.; Ross Anderson, J. L.; Perriman, A. W. Bienzymatic generation of interpenetrating polymer networked engineered living materials with shape changing Pproperties. Adv. Mater. Technol. 2023, 8, 2300626,  DOI: 10.1002/admt.202300626
    139. 139
      Mao, Y.; Su, T.; Wu, Q.; Liao, C.; Wang, Q. Dual enzymatic formation of hybrid hydrogels with supramolecular-polymeric networks. Chem. Commun. 2014, 50 (92), 1442914432,  DOI: 10.1039/C4CC06472K
    140. 140
      Wei, Q.; Xu, M.; Liao, C.; Wu, Q.; Liu, M.; Zhang, Y.; Wu, C.; Cheng, L.; Wang, Q. Printable hybrid hydrogel by dual enzymatic polymerization with superactivity. Chem. Sci. 2016, 7 (4), 27482752,  DOI: 10.1039/C5SC02234G
    141. 141
      Yang, Z.; Liang, G.; Wang, L.; Xu, B. Using a kinase/phosphatase switch to regulate a supramolecular hydrogel and forming the supramolecular hydrogel in vivo. J. Am. Chem. Soc. 2006, 128 (9), 30383043,  DOI: 10.1021/ja057412y
    142. 142
      Knipe, J. M.; Chen, F.; Peppas, N. A. Enzymatic biodegradation of hydrogels for protein delivery targeted to the small intestine. Biomacromolecules 2015, 16 (3), 962972,  DOI: 10.1021/bm501871a
    143. 143
      Pappas, C. G.; Sasselli, I. R.; Ulijn, R. V. Biocatalytic pathway selection in transient tripeptide nanostructures. Angew. Chem., Int. Ed. 2015, 54 (28), 81198123,  DOI: 10.1002/anie.201500867
    144. 144
      Cook, A. B.; Decuzzi, P. Harnessing endogenous stimuli for responsive materials in theranostics. ACS Nano 2021, 15 (2), 20682098,  DOI: 10.1021/acsnano.0c09115
    145. 145
      Ku, T. H.; Chien, M. P.; Thompson, M. P.; Sinkovits, R. S.; Olson, N. H.; Baker, T. S.; Gianneschi, N. C. Controlling and switching the morphology of micellar nanoparticles with enzymes. J. Am. Chem. Soc. 2011, 133 (22), 83928395,  DOI: 10.1021/ja2004736
    146. 146
      Postma, S. G.; Vialshin, I. N.; Gerritsen, C. Y.; Bao, M.; Huck, W. T. Preprogramming complex hydrogel responses using enzymatic reaction networks. Angew. Chem., Int. Ed. 2017, 56 (7), 17941798,  DOI: 10.1002/anie.201610875
    147. 147
      Ikeda, M.; Tanida, T.; Yoshii, T.; Kurotani, K.; Onogi, S.; Urayama, K.; Hamachi, I. Installing logic-gate responses to a variety of biological substances in supramolecular hydrogel-enzyme hybrids. Nat. Chem. 2014, 6 (6), 511518,  DOI: 10.1038/nchem.1937
    148. 148
      Heuser, T.; Merindol, R.; Loescher, S.; Klaus, A.; Walther, A. Photonic devices out of equilibrium: transient memory, signal propagation, and sensing. Adv. Mater. 2017, 29, 1606842,  DOI: 10.1002/adma.201606842
    149. 149
      Hong, Y.; Velegol, D.; Chaturvedi, N.; Sen, A. Biomimetic behavior of synthetic particles: from microscopic randomness to macroscopic control. Phys. Chem. Chem. Phys. 2010, 12 (7), 14231435,  DOI: 10.1039/B917741H
    150. 150
      Sengupta, S.; Dey, K. K.; Muddana, H. S.; Tabouillot, T.; Ibele, M. E.; Butler, P. J.; Sen, A. Enzyme molecules as nanomotors. J. Am. Chem. Soc. 2013, 135 (4), 14061414,  DOI: 10.1021/ja3091615
    151. 151
      Zhao, X.; Gentile, K.; Mohajerani, F.; Sen, A. Powering motion with enzymes. Acc. Chem. Res. 2018, 51 (10), 23732381,  DOI: 10.1021/acs.accounts.8b00286
    152. 152
      Zhang, Y.; Hess, H. Enhanced diffusion of catalytically active enzymes. ACS Cen. Sci. 2019, 5 (6), 939948,  DOI: 10.1021/acscentsci.9b00228
    153. 153
      Zhang, Y.; Hess, H. Chemically-powered swimming and diffusion in the microscopic world. Nat. Rev. Chem. 2021, 5, 500510,  DOI: 10.1038/s41570-021-00281-6
    154. 154
      Patiño, T.; Arqué, X.; Mestre, R.; Palacios, L.; Sánchez, S. Fundamental aspects of enzyme-powered micro- and nanoswimmers. Acc. Chem. Res. 2018, 51 (11), 26622671,  DOI: 10.1021/acs.accounts.8b00288
    155. 155
      Wang, L.; Song, S.; van Hest, J.; Abdelmohsen, L.; Huang, X.; Sánchez, S. Biomimicry of cellular motility and communication based on synthetic soft-architectures. Small 2020, 16 (27), 1907680,  DOI: 10.1002/smll.201907680
    156. 156
      Hermanová, S.; Pumera, M. Biocatalytic micro- and nanomotors. Chem. Eur. J. 2020, 26, 1108,  DOI: 10.1002/chem.202084962
    157. 157
      Muddana, H. S.; Sengupta, S.; Mallouk, T. E.; Sen, A.; Butler, P. J. Substrate catalysis enhances single-enzyme diffusion. J. Am. Chem. Soc. 2010, 132 (7), 21102111,  DOI: 10.1021/ja908773a
    158. 158
      Hortelão, A. C.; García-Jimeno, S.; Cano-Sarabia, M.; Patiño, T.; Maspoch, D.; Sanchez, S. LipoBots: Using liposomal vesicles as protective shell of urease-based nanomotors. Adv. Funct. Mater. 2020, 30 (42), 2002767,  DOI: 10.1002/adfm.202002767
    159. 159
      Choi, H.; Cho, S. H.; Hahn, S. K. Urease-powered polydopamine nanomotors for intravesical therapy of bladder diseases. ACS Nano 2020, 14 (6), 66836692,  DOI: 10.1021/acsnano.9b09726
    160. 160
      Yang, Z.; Wang, L.; Gao, Z.; Hao, X.; Luo, M.; Yu, Z.; Guan, J. Ultrasmall enzyme-powered janus nanomotor working in blood circulation system. ACS Nano 2023, 17 (6), 60236035,  DOI: 10.1021/acsnano.3c00548
    161. 161
      Toebes, B. J.; Cao, F.; Wilson, D. A. Spatial control over catalyst positioning on biodegradable polymeric nanomotors. Nat. Commun. 2019, 10 (1), 5308,  DOI: 10.1038/s41467-019-13288-x
    162. 162
      Qiu, B.; Xie, L.; Zeng, J.; Liu, T.; Yan, M.; Zhou, S.; Liang, Q.; Tang, J.; Liang, K.; Kong, B. Interfacially super-assembled asymmetric and H2O2 sensitive multilayer-sandwich magnetic mesoporous silica nanomotors for detecting and removing heavy metal ions. Adv. Funct. Mater. 2021, 31, 2010694,  DOI: 10.1002/adfm.202010694
    163. 163
      Abdelmohsen, L. K.; Nijemeisland, M.; Pawar, G. M.; Janssen, G. J.; Nolte, R. J.; van Hest, J. C.; Wilson, D. A. Dynamic loading and unloading of proteins in polymeric stomatocytes: formation of an enzyme-loaded supramolecular nanomotor. ACS Nano 2016, 10 (2), 26522660,  DOI: 10.1021/acsnano.5b07689
    164. 164
      Nijemeisland, M.; Abdelmohsen, L. K.; Huck, W. T.; Wilson, D. A.; van Hest, J. C. A compartmentalized out-of-equilibrium enzymatic reaction network for sustained autonomous movement. ACS Cent. Sci. 2016, 2 (11), 843849,  DOI: 10.1021/acscentsci.6b00254
    165. 165
      Chatterjee, A.; Ghosh, S.; Ghosh, C.; Das, D. Fluorescent microswimmers based on cross-beta amyloid nanotubes and divergent cascade networks. Angew. Chem., Int. Ed. 2022, 61 (29), e202201547,  DOI: 10.1002/anie.202201547
    166. 166
      Dey, K. K.; Zhao, X.; Tansi, B. M.; Mendez-Ortiz, W. J.; Cordova-Figueroa, U. M.; Golestanian, R.; Sen, A. Micromotors powered by enzyme catalysis. Nano Lett. 2015, 15 (12), 83118315,  DOI: 10.1021/acs.nanolett.5b03935
    167. 167
      Zhao, X.; Palacci, H.; Yadav, V.; Spiering, M. M.; Gilson, M. K.; Butler, P. J.; Hess, H.; Benkovic, S. J.; Sen, A. Substrate-driven chemotactic assembly in an enzyme cascade. Nat. Chem. 2018, 10 (3), 311317,  DOI: 10.1038/nchem.2905
    168. 168
      Wang, J.; Toebes, B. J.; Plachokova, A. S.; Liu, Q.; Deng, D.; Jansen, J. A.; Yang, F.; Wilson, D. A. Self-propelled PLGA micromotor with chemotactic response to inflammation. Adv. Healthcare Mater. 2020, 9 (7), 1901710,  DOI: 10.1002/adhm.201901710
    169. 169
      Joseph, A.; Contini, C.; Cecchin, D.; Nyberg, S.; Ruiz-Perez, L.; Gaitzsch, J.; Fullstone, G.; Tian, X.; Azizi, J.; Preston, J.; Volpe, G.; Battaglia, G. Chemotactic synthetic vesicles: design and applications in blood-brain barrier crossing. Sci. Adv. 2017, 3, e1700362,  DOI: 10.1126/sciadv.1700362
    170. 170
      Kumar, B.; Patil, A. J.; Mann, S. Enzyme-powered motility in buoyant organoclay/DNA protocells. Nat. Chem. 2018, 10 (11), 1154116,  DOI: 10.1038/s41557-018-0119-3
    171. 171
      Gao, N.; Li, M.; Tian, L.; Patil, A. J.; Pavan Kumar, B.; Mann, S. Chemical-mediated translocation in protocell-based microactuators. Nat. Chem. 2021, 13 (9), 868879,  DOI: 10.1038/s41557-021-00728-9
    172. 172
      Wheeldon, I.; Minteer, S. D.; Banta, S.; Barton, S. C.; Atanassov, P.; Sigman, M. Substrate channelling as an approach to cascade reactions. Nat. Chem. 2016, 8 (4), 299309,  DOI: 10.1038/nchem.2459
    173. 173
      Hinzpeter, F.; Gerland, U.; Tostevin, F. Optimal compartmentalization strategies for metabolic microcompartments. Biophys. J. 2017, 112, 767779,  DOI: 10.1016/j.bpj.2016.11.3194
    174. 174
      Qiao, Y.; Li, M.; Booth, R.; Mann, S. Predatory behaviour in synthetic protocell communities. Nat. Chem. 2017, 9 (2), 110119,  DOI: 10.1038/nchem.2617
    175. 175
      Qiao, Y.; Li, M.; Qiu, D.; Mann, S. Response-retaliation behavior in synthetic protocell communities. Angew. Chem., Int. Ed. 2019, 58 (49), 1775817763,  DOI: 10.1002/anie.201909313
    176. 176
      Chakraborty, T.; Wegner, S. V. Cell to cell signaling through light in artificial cell communities: glowing predator lures prey. ACS Nano 2021, 15 (6), 94349444,  DOI: 10.1021/acsnano.1c01600
    177. 177
      Mason, A. F.; Buddingh, B. C.; Williams, D. S.; van Hest, J. C. M. Hierarchical self-assembly of a copolymer-stabilized coacervate protocell. J. Am. Chem. Soc. 2017, 139 (48), 1730917312,  DOI: 10.1021/jacs.7b10846
    178. 178
      Wang, X.; Tian, L.; Du, H.; Li, M.; Mu, W.; Drinkwater, B. W.; Han, X.; Mann, S. Chemical communication in spatially organized protocell colonies and protocell/living cell micro-arrays. Chem. Sci. 2019, 10 (41), 94469453,  DOI: 10.1039/C9SC04522H
    179. 179
      Buddingh’, B. C.; Elzinga, J.; van Hest, J. C. M. Intercellular communication between artificial cells by allosteric amplification of a molecular signal. Nat. Commun. 2020, 11 (1), 1652,  DOI: 10.1038/s41467-020-15482-8
    180. 180
      Ji, Y.; Chakraborty, T.; Wegner, S. V. Self-regulated and bidirectional communication in synthetic cell communities. ACS Nano 2023, 17 (10), 89929002,  DOI: 10.1021/acsnano.2c09908
    181. 181
      Taylor, H.; Gao, N.; Mann, S. Chemical communication and protocell-matrix dynamics in segregated colloidosome micro-colonies. Angew. Chem., Int. Ed. 2023, 62 (24), e202300932,  DOI: 10.1002/anie.202300932
    182. 182
      Fusi, G.; Del Giudice, D.; Skarsetz, O.; Di Stefano, S.; Walther, A. Autonomous soft robots empowered by chemical reaction networks. Adv. Mater. 2023, 35 (7), e2209870,  DOI: 10.1002/adma.202209870
    183. 183
      Elani, Y.; Law, R. V.; Ces, O. Vesicle-based artificial cells as chemical microreactors with spatially segregated reaction pathways. Nat. Commun. 2014, 5, 5305,  DOI: 10.1038/ncomms6305
    184. 184
      Liu, S.; Zhang, Y.; He, X.; Li, M.; Huang, J.; Yang, X.; Wang, K.; Mann, S.; Liu, J. Signal processing and generation of bioactive nitric oxide in a model prototissue. Nat. Commun. 2022, 13 (1), 5254,  DOI: 10.1038/s41467-022-32941-6
    185. 185
      Sengupta, S.; Patra, D.; Ortiz-Rivera, I.; Agrawal, A.; Shklyaev, S.; Dey, K. K.; Cordova-Figueroa, U.; Mallouk, T. E.; Sen, A. Self-powered enzyme micropumps. Nat. Chem. 2014, 6 (5), 415422,  DOI: 10.1038/nchem.1895
    186. 186
      Maiti, S.; Shklyaev, O. E.; Balazs, A. C.; Sen, A. Self-organization of fluids in a multienzymatic pump system. Langmuir 2019, 35 (10), 37243732,  DOI: 10.1021/acs.langmuir.8b03607
    187. 187
      Ortiz-Rivera, I.; Courtney, T. M.; Sen, A. Enzyme micropump-based inhibitor assays. Adv. Funct. Mater. 2016, 26, 21352142,  DOI: 10.1002/adfm.201504619
    188. 188
      Manna, R. K.; Shklyaev, O. E.; Balazs, A. C. Chemically driven multimodal locomotion of active, flexible sheets. Langmuir 2023, 39 (2), 780789,  DOI: 10.1021/acs.langmuir.2c02666
    189. 189
      Simo, C.; Serra-Casablancas, M.; Hortelao, A. C.; Di Carlo, V.; Guallar-Garrido, S.; Plaza-Garcia, S.; Rabanal, R. M.; Ramos-Cabrer, P.; Yague, B.; Aguado, L.; Bardia, L.; Tosi, S.; Gomez-Vallejo, V.; Martin, A.; Patino, T.; Julian, E.; Colombelli, J.; Llop, J.; Sanchez, S. Urease-powered nanobots for radionuclide bladder cancer therapy. Nat. Nanotechnol. 2024,  DOI: 10.1038/s41565-023-01577-y .
    190. 190
      Rollin, J. A.; Tam, T. K.; Zhang, Y. H. P. New biotechnology paradigm: cell-free biosystems for biomanufacturing. Green Chem. 2013, 15 (7), 17081719,  DOI: 10.1039/c3gc40625c
    191. 191
      Zhang, Y.; Hess, H. Toward rational design of high-efficiency enzyme cascades. ACS Catal. 2017, 7 (9), 60186027,  DOI: 10.1021/acscatal.7b01766
    192. 192
      Shi, J.; Wu, Y.; Zhang, S.; Tian, Y.; Yang, D.; Jiang, Z. Bioinspired construction of multi-enzyme catalytic systems. Chem. Soc. Rev. 2018, 47, 42954313,  DOI: 10.1039/C7CS00914C
    193. 193
      Wang, Z.; Sundara Sekar, B.; Li, Z. Recent advances in artificial enzyme cascades for the production of value-added chemicals. Bioresour. Technol. 2021, 323, 124551,  DOI: 10.1016/j.biortech.2020.124551
    194. 194
      Mordhorst, S.; Andexer, J. N. Round, round we go – strategies for enzymatic cofactor regeneration. Nat. Prod. Rep. 2020, 37, 13161333,  DOI: 10.1039/D0NP00004C
    195. 195
      Bachosz, K.; Zdarta, J.; Bilal, M.; Meyer, A. S.; Jesionowski, T. Enzymatic cofactor regeneration systems: A new perspective on efficiency assessment. Sci. Total Environ. 2023, 868, 161630,  DOI: 10.1016/j.scitotenv.2023.161630
    196. 196
      Monck, C.; Elani, Y.; Ceroni, F. Cell-free protein synthesis: biomedical applications and future perspectives. Chem. Eng. Res. Des. 2022, 177, 653658,  DOI: 10.1016/j.cherd.2021.11.025
    197. 197
      Perez, J. G.; Stark, J. C.; Jewett, M. C. Cell-free synthetic biology: engineering beyond the cell. Cold Spring Harb. Perspect. Biol. 2016, 8 (12), a023853,  DOI: 10.1101/cshperspect.a023853
    198. 198
      Silverman, A. D.; Karim, A. S.; Jewett, M. C. Cell-free gene expression: an expanded repertoire of applications. Nat. Rev. Genet. 2020, 21, 151170,  DOI: 10.1038/s41576-019-0186-3
    199. 199
      Korman, T. P.; Sahachartsiri, B.; Li, D.; Vinokur, J. M.; Eisenberg, D.; Bowie, J. U. A synthetic biochemistry system for the in vitro production of isoprene from glycolysis intermediates. Protein Sci. 2014, 23 (5), 576585,  DOI: 10.1002/pro.2436
    200. 200
      Korman, T. P.; Opgenorth, P. H.; Bowie, J. U. A synthetic biochemistry platform for cell free production of monoterpenes from glucose. Nat. Commun. 2017, 8, 15526,  DOI: 10.1038/ncomms15526
    201. 201
      Opgenorth, P. H.; Korman, T. P.; Bowie, J. U. A synthetic biochemistry molecular purge valve module that maintains redox balance. Nat. Commun. 2014, 5, 4113,  DOI: 10.1038/ncomms5113
    202. 202
      Opgenorth, P. H.; Korman, T. P.; Bowie, J. U. A synthetic biochemistry module for production of bio-based chemicals from glucose. Nat. Chem. Biol. 2016, 12 (6), 393395,  DOI: 10.1038/nchembio.2062
    203. 203
      Valliere, M. A.; Korman, T. P.; Woodall, N. B.; Khitrov, G. A.; Taylor, R. E.; Baker, D.; Bowie, J. U. A cell-free platform for the prenylation of natural products and application to cannabinoid production. Nat. Commun. 2019, 10 (1), 565,  DOI: 10.1038/s41467-019-08448-y
    204. 204
      Sarria, S.; Wong, B.; Martin, H. G.; Keasling, J. D.; Peralta-Yahya, P. Microbial synthesis of pinene. ACS Synth. Biol. 2014, 3 (7), 466475,  DOI: 10.1021/sb4001382
    205. 205
      Karim, A. S.; Jewett, M. C. A cell-free framework for rapid biosynthetic pathway prototyping and enzyme discovery. Metab. Eng. 2016, 36, 116126,  DOI: 10.1016/j.ymben.2016.03.002
    206. 206
      Dudley, Q. M.; Anderson, K. C.; Jewett, M. C. Cell-free mixing of Escherichia coli crude extracts to prototype and rationally engineer high-titer mevalonate synthesis. ACS Synth. Biol. 2016, 5 (12), 15781588,  DOI: 10.1021/acssynbio.6b00154
    207. 207
      Casini, A.; Chang, F. Y.; Eluere, R.; King, A. M.; Young, E. M.; Dudley, Q. M.; Karim, A.; Pratt, K.; Bristol, C.; Forget, A. A pressure test to make 10 molecules in 90 days: external evaluation of methods to engineer biology. J. Am. Chem. Soc. 2018, 140 (12), 43024316,  DOI: 10.1021/jacs.7b13292
    208. 208
      Dudley, Q. M.; Nash, C. J.; Jewett, M. C. Cell-free biosynthesis of limonene using enzyme-enriched Escherichia coli lysates. Synthetic Biology 2019, 4 (1), ysz003,  DOI: 10.1093/synbio/ysz003
    209. 209
      Nielsen, D. U.; Hu, X.-M.; Daasbjerg, K.; Skrydstrup, T. Chemically and electrochemically catalysed conversion of CO2 to CO with follow-up utilization to value-added chemicals. Nat. Catal. 2018, 1 (4), 244254,  DOI: 10.1038/s41929-018-0051-3
    210. 210
      Long, S. P.; Ainsworth, E. A.; Rogers, A.; Ort, D. R. Rising atmospheric carbon dioxide: plants FACE the future. Annu. Rev. Plant Biol. 2004, 55, 591628,  DOI: 10.1146/annurev.arplant.55.031903.141610
    211. 211
      Spreitzer, R. J.; Salvucci, M. E. Rubisco: structure, regulatory interactions, and possibilities for a better enzyme. Annu. Rev. Plant Biol. 2002, 53, 449475,  DOI: 10.1146/annurev.arplant.53.100301.135233
    212. 212
      Könneke, M.; Schubert, D. M.; Brown, P. C.; Hugler, M.; Standfest, S.; Schwander, T.; Schada von Borzyskowski, L.; Erb, T. J.; Stahl, D. A.; Berg, I. A. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (22), 82398244,  DOI: 10.1073/pnas.1402028111
    213. 213
      Scheffen, M.; Marchal, D. G.; Beneyton, T.; Schuller, S. K.; Klose, M.; Diehl, C.; Lehmann, J.; Pfister, P.; Carrillo, M.; He, H. A new-to-nature carboxylation module to improve natural and synthetic CO2 fixation. Nat. Catal. 2021, 4 (2), 105115,  DOI: 10.1038/s41929-020-00557-y
    214. 214
      Schwander, T.; Schada von Borzyskowski, L.; Burgener, S.; Cortina, N. S.; Erb, T. J. A synthetic pathway for the fixation of carbon dioxide in vitro. Science 2016, 354, 900904,  DOI: 10.1126/science.aah5237
    215. 215
      Gale, N. L.; Beck, J. V. Evidence for the Calvin cycle and hexose monophosphate pathway in Thiobacillus ferrooxidans. J. Bacteriol. 1967, 94, 10521059,  DOI: 10.1128/jb.94.4.1052-1059.1967
    216. 216
      Miller, T. E.; Beneyton, T.; Schwander, T.; Diehl, C.; Girault, M.; McLean, R.; Chotel, T.; Claus, P.; Cortina, N. S.; Baret, J.-C.; Erb, T. J. Light-powered CO2 fixation in a chloroplast mimic with natural and synthetic parts. Science 2020, 368, 649654,  DOI: 10.1126/science.aaz6802
    217. 217
      Sundaram, S.; Diehl, C.; Cortina, N. S.; Bamberger, J.; Paczia, N.; Erb, T. J. A modular in vitro platform for the production of terpenes and polyketides from CO2. Angew. Chem., Int. Ed. 2021, 60 (30), 1642016425,  DOI: 10.1002/anie.202102333
    218. 218
      Diehl, C.; Gerlinger, P. D.; Paczia, N.; Erb, T. J. Synthetic anaplerotic modules for the direct synthesis of complex molecules from CO2. Nat. Chem. Biol. 2023, 19 (2), 168175,  DOI: 10.1038/s41589-022-01179-0
    219. 219
      Xiao, L.; Liu, G.; Gong, F.; Zhu, H.; Zhang, Y.; Cai, Z.; Li, Y. A minimized synthetic carbon fixation cycle. ACS Catal. 2022, 12 (1), 799808,  DOI: 10.1021/acscatal.1c04151
    220. 220
      Bierbaumer, S.; Nattermann, M.; Schulz, L.; Zschoche, R.; Erb, T. J.; Winkler, C. K.; Tinzl, M.; Glueck, S. M. Enzymatic conversion of CO2: from natural to artificial utilization. Chem. Rev. 2023, 123 (9), 57025754,  DOI: 10.1021/acs.chemrev.2c00581
    221. 221
      Cai, T.; Sun, H.; Qiao, J.; Zhu, L.; Zhang, F.; Zhang, J.; Tang, Z.; Wei, X.; Yang, J.; Yuan, Q. Cell-free chemo-enzymatic starch synthesis from carbon dioxide. Science 2021, 373, 1523152,  DOI: 10.1126/science.abh4049
    222. 222
      Zhou, J.; Tian, X.; Yang, Q.; Zhang, Z.; Chen, C.; Cui, Z.; Ji, Y.; Schwaneberg, U.; Chen, B.; Tan, T. Three multi-enzyme cascade pathways for conversion of C1 to C2/C4 compounds. Chem. Catal. 2022, 2 (10), 26752690,  DOI: 10.1016/j.checat.2022.07.011
    223. 223
      Zhang, J.; Liu, D.; Liu, Y.; Chu, H.; Bai, J.; Cheng, J.; Zhao, H.; Fu, S.; Liu, H.; Fu, Y. Hybrid synthesis of polyhydroxybutyrate bioplastics from carbon dioxide. Green Chem. 2023, 25 (8), 32473255,  DOI: 10.1039/D3GC00387F
    224. 224
      Li, F.; Wei, X.; Zhang, L.; Liu, C.; You, C.; Zhu, Z. Installing a green engine to drive an enzyme cascade: a light-powered in vitro biosystem for poly(3-hydroxybutyrate) synthesis. Angew. Chem., Int. Ed. 2022, 61 (1), e202111054,  DOI: 10.1002/anie.202111054
    225. 225
      Liu, H.; Arbing, M. A.; Bowie, J. U. Expanding the use of ethanol as a feedstock for cell-free synthetic biochemistry by implementing acetyl-CoA and ATP generating pathways. Sci. Rep. 2022, 12 (1), 7700,  DOI: 10.1038/s41598-022-11653-3
    226. 226
      Bailoni, E.; Partipilo, M.; Coenradij, J.; Grundel, D. A. J.; Slotboom, D. J.; Poolman, B. Minimal out-of-equilibrium metabolism for synthetic cells: a membrane perspective. ACS Synth. Biol. 2023, 12 (4), 922946,  DOI: 10.1021/acssynbio.3c00062
    227. 227
      Lee, K. Y.; Park, S. J.; Lee, K. A.; Kim, S. H.; Kim, H.; Meroz, Y.; Mahadevan, L.; Jung, K. H.; Ahn, T. K.; Parker, K. K. Photosynthetic artificial organelles sustain and control ATP-dependent reactions in a protocellular system. Nat. Biotechnol. 2018, 36 (6), 530535,  DOI: 10.1038/nbt.4140
    228. 228
      Berhanu, S.; Ueda, T.; Kuruma, Y. Artificial photosynthetic cell producing energy for protein synthesis. Nat. Commun. 2019, 10 (1), 1325,  DOI: 10.1038/s41467-019-09147-4
    229. 229
      Bailoni, E.; Poolman, B. ATP recycling fuels sustainable glycerol 3-phosphate formation in synthetic cells fed by dynamic dialysis. ACS Synth. Biol. 2022, 11 (7), 23482360,  DOI: 10.1021/acssynbio.2c00075
    230. 230
      Blanken, D.; Foschepoth, D.; Serrao, A. C.; Danelon, C. Genetically controlled membrane synthesis in liposomes. Nat. Commun. 2020, 11 (1), 4317,  DOI: 10.1038/s41467-020-17863-5
    231. 231
      Partipilo, M.; Ewins, E. J.; Frallicciardi, J.; Robinson, T.; Poolman, B.; Slotboom, D. J. Minimal pathway for the regeneration of redox cofactors. JACS Au 2021, 1 (12), 22802293,  DOI: 10.1021/jacsau.1c00406
    232. 232
      Pols, T.; Sikkema, H. R.; Gaastra, B. F.; Frallicciardi, J.; Smigiel, W. M.; Singh, S.; Poolman, B. A synthetic metabolic network for physicochemical homeostasis. Nat. Commun. 2019, 10 (1), 4239,  DOI: 10.1038/s41467-019-12287-2
    233. 233
      Katz, E.; Privman, V. Enzyme-based logic systems for information processing. Chem. Soc. Rev. 2010, 39 (5), 18351857,  DOI: 10.1039/b806038j
    234. 234
      Benenson, Y. Biomolecular computing systems: principles, progress and potential. Nat. Rev. Genet. 2012, 13 (7), 455468,  DOI: 10.1038/nrg3197
    235. 235
      Grozinger, L.; Amos, M.; Gorochowski, T. E.; Carbonell, P.; Oyarzun, D. A.; Stoof, R.; Fellermann, H.; Zuliani, P.; Tas, H.; Goni-Moreno, A. Pathways to cellular supremacy in biocomputing. Nat. Commun. 2019, 10 (1), 5250,  DOI: 10.1038/s41467-019-13232-z
    236. 236
      Ivanov, N. M.; Baltussen, M. G.; Regueiro, C. L. F.; Derks, M.; Huck, W. T. S. Computing arithmetic functions using immobilised enzymatic reaction networks. Angew. Chem., Int. Ed. 2023, 62 (7), e202215759,  DOI: 10.1002/anie.202215759
    237. 237
      Genot, A. J.; Fujii, T.; Rondelez, Y. Computing with competition in biochemical networks. Phys. Rev. Lett. 2012, 109 (20), 208102,  DOI: 10.1103/PhysRevLett.109.208102
    238. 238
      Pandi, A.; Koch, M.; Voyvodic, P. L.; Soudier, P.; Bonnet, J.; Kushwaha, M.; Faulon, J. L. Metabolic perceptrons for neural computing in biological systems. Nat. Commun. 2019, 10 (1), 3880,  DOI: 10.1038/s41467-019-11889-0
    239. 239
      Okumura, S.; Gines, G.; Lobato-Dauzier, N.; Baccouche, A.; Deteix, R.; Fujii, T.; Rondelez, Y.; Genot, A. J. Nonlinear decision-making with enzymatic neural networks. Nature 2022, 610 (7932), 496501,  DOI: 10.1038/s41586-022-05218-7
    240. 240
      Hathcock, D.; Sheehy, J.; Weisenberger, C.; Ilker, E.; Hinczewski, M. Noise filtering and prediction in biological signaling networks. IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2016, 2 (1), 1630,  DOI: 10.1109/TMBMC.2016.2633269
    241. 241
      O’Brien, J.; Murugan, A. Temporal pattern recognition through analog molecular computation. ACS Synth. Biol. 2019, 8 (4), 826832,  DOI: 10.1021/acssynbio.8b00503
    242. 242
      McGrath, T.; Jones, N. S.; Ten Wolde, P. R.; Ouldridge, T. E. Biochemical machines for the interconversion of mutual information and work. Phys. Rev. Lett. 2017, 118 (2), 028101  DOI: 10.1103/PhysRevLett.118.028101
    243. 243
      Stern, M.; Murugan, A. Learning without neurons in physical systems. Annu. Rev. Condens. Matter Phys. 2023, 14, 41741,  DOI: 10.1146/annurev-conmatphys-040821-113439
    244. 244
      Shklyaev, O. E.; Balazs, A. C. Interlinking spatial dimensions and kinetic processes in dissipative materials to create synthetic systems with lifelike functionality. Nat. Nanotechnol. 2024, 19, 146,  DOI: 10.1038/s41565-023-01530-z
    245. 245
      Bray, D. Protein molecules as computational elements in living cells. Nature 1995, 376, 307312,  DOI: 10.1038/376307a0
    246. 246
      van Sluijs, B.; Zhou, T.; Helwig, B.; Baltussen, M. G.; Nelissen, F. H. T.; Heus, H. A.; Huck, W. T. S. Inverse design of enzymatic reaction network states. Nat. Commun. 2024, 15, 1602,  DOI: 10.1038/s41467-024-45886-9
    247. 247
      Zaikin, A. N.; Zhabotinsky, A. M. Concentration wave propagation in two-dimensional liquid-phase self-oscillating system. Nature 1970, 225, 535537,  DOI: 10.1038/225535b0