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Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

  • Savas Konur*
    Savas Konur
    Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.
    *Email: [email protected]
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    Orcidhttps://orcid.org/0000-0002-0642-9452
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  • Laurentiu Mierla
    Laurentiu Mierla
    Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.
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  • Harold Fellermann
    Harold Fellermann
    Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.
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    Orcidhttps://orcid.org/0000-0001-5861-1945
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  • Christophe Ladroue
    Christophe Ladroue
    Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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  • Bradley Brown
    Bradley Brown
    Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.
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  • Anil Wipat
    Anil Wipat
    Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.
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    Orcidhttps://orcid.org/0000-0001-7310-4191
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  • Jamie Twycross
    Jamie Twycross
    School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, U.K.
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  • Boyang Peter Dun
    Boyang Peter Dun
    Department of Computer Science, Stanford University, Stanford, California 94305, United States
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  • Sara Kalvala
    Sara Kalvala
    Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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  • Marian Gheorghe
    Marian Gheorghe
    Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.
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  • , and 
  • Natalio Krasnogor*
    Natalio Krasnogor
    Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.
    *Email: [email protected]
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    Orcidhttps://orcid.org/0000-0002-2651-4320
Cite this: ACS Synth. Biol. 2021, 10, 8, 1931–1945
Publication Date (Web):August 2, 2021
https://doi.org/10.1021/acssynbio.1c00143

Copyright © 2022 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

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As DNA sequencing and synthesis technology become cheaper and more easily accessible, (1) the scale and complexity of engineering biology projects is set to grow. Furthermore, rapidly converging biotechnology and computing science are accelerating the adoption of synthetic biology across scientific disciplines as well as industrial applications. Some of the rapid progress being made include advances in genome editing via CRISPR-Cas9 variants (e.g., refs (2,3)), biomedical materials design, (4) biofilms engineering as nanofactories, (5) synthetic biology routes to functional materials, (6) environmental remediation, (7) the engineered transformation of an E. coli strain so it can manufacture all its carbon biomass directly via CO2 consumption, (8) novel biomedical sensors, (9) to the creation of in vivo DNA-based memory devices. (10) These advances have also prompted progress in computer aided biology, ranging from computational simulations, (11−14) to metabolic engineering optimization tools, (15) new domain specific languages from describing engineered circuits, (16) to new biological barcodes for version control systems of living cells. (17) Along with rapid developments in this field, a number of software suits for modeling and analyzing synthetic biology have been developed. Among these tools are iBioSim, (18) Cello, (19) Gro, (20) Tinkercell, (21) Eugene, (22) and Proto. (23)
Yet, contemporary progress in synthetic biology is largely driven by a cumbersome trial and error cycle in the laboratory. Various computational tools, which include computer aides for designing and analyzing synthetic biological circuits, have been released to assist this development process. These tools commonly support a specific functionality, such as genetic design specification, sequence specification, or simulation. For example, Gene Designer, (24) Eugene, (25) GenoCAD, (26) GEC, (27) MoSeC, (28) and PartsGenie (29) allow a user to express, and in part compile, genetic sequence designs. Other tools, such as COPASI (30) and VCell, (31) support biomolecular simulation. Interoperability between these tools is often impractical due to the lack of a common file format able to capture the information required by each individual tool. There have been attempts to capture their common semantics through standard meta-languages such as SBOL (32) and SBML, (33) but it is difficult to reverse-translate the low-level description obtained from one tool into the input required by the next. Full interoperability, while a laudable task, is very difficult to achieve. The Clotho toolset was an attempt to create a fully integrated set of app-style tools, (34) but the software development difficulties for such a project are very significant. When taking biological constructs from idea to working prototype through a series of refinements, it becomes increasingly harder to maintain the various formal specifications while ensuring that each of them is up-to-date and consistent with every other object models.
Because of the difficulty in interoperability, synthetic biologists are typically forced to either dispense with some of the available tool support or work with a set of independent formalizations of their synthetic design specifications—in which case crucial details may be lost.
In this paper, we present the Infobiotics Workbench (https://infobiotics.org), a computer-aided design suite for synthetic biology that assists synthetic biologists in an informed, iterative workflow of system specification, verification, simulation, and biocompilation. The Infobiotics Workbench (IBW) features a unique domain-specific Infobiotics Language (IBL), which integrates these different computational aspects into a single specification file. Unlike conventional approaches, IBL defines a simple combined grammar for modeling, verification, and biocompilation statements, which would otherwise require using complex formalisms and sophisticated transformations for utilizing independent tools and performing different computational analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the manual translation requirement for standalone applications.
IBW replaces an older version. (35) It now presents several new state of the art features, including (i) an intuitive and expressive synthetic biology design language, (ii) a simulation component that implements all the variants of Gillespie’s stochastic simulation algorithms (SSA), (iii) a prediction tool that selects the best performing SSA using machine learning algorithms, (iv) a parallel implementation of Gillespie algorithms executed on cloud GPU clusters, (v) a verification component that allows verifying queries and design requirements using natural language queries, (vi) a biocompilation module that allows automated compilation of a specified synthetic circuit into eventual genetic sequence information and (vii) import from/export to standard data exchange formats, e.g., SBOL and SBML. This makes IBW a unique platform that integrates these unique features. A comprehensive comparison of IBW with the existing tools is provided in Table S1 of the Supporting Information.
The workbench adopts well-established design principles that guide both experienced and nonexperienced biologists in refining a putative functionality into a formally specified document for fabricating a nucleotide sequence after undergoing some computational analysis.
IBW’s unique features are underpinned by strong theoretical foundations spanning advanced stochastic simulation algorithms, high-performance computing, formal verification, ontology languages, biological property mining, constraint satisfaction algorithms, and machine learning.

Results and Discussions

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The Infobiotics Language

Infobiotics Workbench (IBW) relies on the Infobiotics Language (IBL), a domain-specific language based on synthetic biology. IBL is very unique in the sense that it integrates all computational tasks into one single language. Namely, it gives directives that indicate what simulations to run, what verifications to calculate, and what synthetic circuits to biocompile. Integrating these directives into the same domain-specific language enables us to fully support an engineering-inspired workflow of iterative design, simulation, verification, and compilation of synthetic biological systems.
IBL has used terms from ontologies available in the literature, e.g., SBOL (32,36) and SyBiOnt (37) as well as Gene Ontology (38) and Sequence Ontology. (39) The language has also been complemented with particular terms that are tailored to the potential user base such as RULEs to capture chemical reactions and PROCESSes to reuse (groups of rules) and provide a more abstract representation (and decomposition) of complex processes (see Figure 1).

Figure 1

Figure 1. Hierarchical representation of biological entities. A RULE represents a chemical reaction; a PROCESS is a group of rules and provides a more abstract representation (and decomposition) of complex processes; a DEVICE refers to an assembly of genetic parts and biological building blocks; a CELL represents a bacterial cell.

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IBL provides statements to declare biological entities and their interactions along with statements to (a) annotate PROCESSes with stochastic rates for stochastic simulation; (b) annotate PROCESSes, DEVICEs, and CELLs with verification statements to be verified; and (c) annotate GENEs, PROMOTERs, and other biological entities with sequence information or links to online repository entries required for biomatter compilation. IBL further allows the annotation of designs with custom information for use-cases not directly supported by the core language.
IBL permits a robust encapsulation and hierarchal structure of biological entities (see Figure 1). The language has the following structure:

  • Molecular species represent any entity, from PROTEIN to DNA to MOLECULE.

  • RULEs are used to define a transformation, most often a chemical transformation.

  • PROCESSes are collections of RULEs. They make it easier to reuse rules and typically represent a more abstract process, e.g., constitutive protein expression.

  • DEVICEs are collection of PROCESSes and RULEs. They refer to device in the synthetic biology sense: a piece of DNA made up of parts.

  • SYSTEMs contain DEVICEs and RULEs.

  • PLASMIDs are collections of SYSTEMs, DEVICEs and RULEs. They are useful when more than one type of plasmid is used in the cell.

  • CHROMOSOMEs are collections of SYSTEMs, DEVICEs, and RULEs.

  • CELLs refer to biological cells. They are a collection of PLASMIDs, SYSTEMs, DEVICEs, PROCESSes, and RULEs.

  • REGIONs contain CELLs, PROCESSes, RULEs, and molecular species.

IBL allows the user to declare common biological parts, including PROMOTER, GENE, RIBOSOME, TERMINATOR, CLONING SITE, DNA, RNA, and RBS with optional declaration of sequences either in online repositories or in a built-in database as well as nongenetic molecular components such as PROTEIN and MOLECULE (see Algorithm 1). Thus, declared biological parts can be connected to each other via reaction rules, which can be declared as either reversible or irreversible. Since complexification is particularly important in biological systems, e.g., in the quaternary structure of proteins or genetic regulation, complexes can be used in rules without the need to explicitly declare them.
To ease the integration of experimental knowledge, IBL supports specification of concentrations and reaction rate constants in SI units and automatically converts between units where possible. Concentrations can be specified in molar (i.e., mol/liter), millimolar, micromolar, and nanomolar units. Alternatively, concentration can also be specified as an amount of molecules, which can be interconverted with molar concentration using a defined cell volume (which defaults to one femtoliter). Similarly, reaction rate constants can be specified in the units per second, per minute, and per hour in case of unimolecular reactions, or per mole per second, etc. for bimolecular reactions.
As an example, consider the regulation of the Plac promoter by LacI. The IBL segment shown in Algorithm 2 first declares the LacI protein and its reversible dimerization reaction (lines 1–4), followed by the promoter and the reversible regulator binding (lines 5–8).
To support scalable, modular, and reusable designs, IBL allows for the declaration of PROCESS, which groups together a set of rules involving abstract biological entities (see Algorithm 1). Once defined, processes can be instantiated for specific components. For example, one can specify a process that collects all of the reactions that constitute general regulated gene expression, which then can be instantiated for different transcription factors and genes. In this manner, processes introduce a layer of modularity and abstraction similar to function declarations in imperative programming languages. This is particularly convenient in conjunction with an “import” statement that allows references to process definitions across files. In this manner, general purpose libraries for recurrent biological processes can be built and later reused to declare specific biological circuits with relatively little effort.
In addition to PROCESS, IBL also provides the means to group molecules, rules, and process instantiations into DEVICE (see Algorithm 3) to capture the action of an entire genetic device, i.e., a strand of DNA containing several promoters, genes, and their encoded proteins.
In addition to logical encapsulation, IBL supports physical encapsulation of molecules, processes, and devices into CELLs, as well as colocation of molecules, rules, processes, and cells into REGIONs. By default, cells and regions physically separate molecules by introducing boundaries. However, IBL supports mechanisms to define translocation of molecules from cells into regions and vice versa, which allows for expressing secretion and uptake processes as well as cell-to-cell communication. Thus, IBW can model populations of potentially mixed bacterial cohorts. Algorithm 4 gives an example.
IBL also allows incorporating verification and biocompilation directives into various compartments, e.g., devices, cells and processes, to make sure that certain constraints are met at the design stage. For example, the following verification and compilation statements in Algorithm 5 can be added within the device defined in Algorithm 3.

The Infobiotics Workbench

IBW (https://infobiotics.org) provides an in silico laboratory for synthetic biology and a single, integrated, user-friendly, computer aided design platform for Synthetic Biology parts and devices.
A screenshot of the IBW user interface is shown in Figure 2. IBW provides support for the specification of synthetic devices, their simulation, verification, and biomatter compilation.

Figure 2

Figure 2. A screenshot of IBW in its biocompilation perspective. The integrated development environment features a project navigator (left), source code editor (top center), biocompilation controller (right) and compilation result window (bottom center). The toolbar and menu provides access to typical development features including refactoring and collaborative versioning tools such as git.

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Simulation

IBW features two types of simulation environments.

CPU-Based Simulation

NGSS (40) is a high performance CPU-based simulation environment, which implements eight exact and one approximating variant of Gillespie’s stochastic simulation algorithm (Direct Method (DM), Optimized DM, Sorting DM, Logarithmic DM, Partial Propensity DM, Next Reaction Method, First Reaction Method, Composition Rejection, and Tau Leaping).
Depending on the simulated reaction network, runtime performance of these algorithms was found to differ by orders of magnitude with no single winning strategy that would outperform competitors on all model systems. Using machine learning techniques, we are able to predict the best performing simulation algorithm for a given reaction network.
Our method relies on representing stochastic models as graphs of dependencies between reactions or species. Using these graphs, we are able to build a topological profile of each model (e.g., number of vertices and edges, graph density, graph degree etc.). Machine learning algorithms can learn how these topological and graph-theoretic features of the underlying network of a model affect the simulation time of SSAs by analyzing the performance (reactions executed per second of CPU time) of each algorithm measured using benchmark models (obtained from the BioModels databased (41)). The predictor performs a model topology analysis and uses the results to predict the fastest SSA for that model.
We have implemented this method as a performance benchmarking suite, SSA Predictor, (13) which allows for a direct and unbiased comparison of stochastic simulation algorithms. SSA Predictor is fully integrated in IBW. So, when a model is simulated, IBW automatically chooses the fastest predicted SSA algorithm, and performs the simulation using this algorithm.
Apart from being implemented for best performance, the simulator also employs MPI to distribute multiple simulation runs on computing clusters and OpenMP for multicore systems.

GPU-Accelerated Simulation

IBW also integrates a GPU parallel implementation of the Gillespie SSA, taking advantage of the CUDA platform. The implementation is designed to run in an HPC environment, with which IBW communicates for submitting simulation requests and retrieving results. The performance of the GPU implementation of the Gillespie SSA mainly comes from computing the species evolutions for all the runs, in parallel, for each individual time point. Moreover, it also takes advantage of the parallel implementation for other different internal processes from the Gillespie SSA workflow, such as using the Hillis Steele algorithm (42) for computing the propensity prefix sums.
GPU provides significant performance gains compared to the CPU counterpart. As Figure 3 suggests, the GPU simulator provides a significant performance gain compared to the CPU simulator.

Figure 3

Figure 3. Performance benchmark comparison of IBW’s CPU and GPU simulators using the quorum sensing model (see Quorum Sensing section). The GPU simulator provides a much faster and efficient alternative compared to the CPU simulator (implementing serial algorithms) as it uses parallel algorithms, run in high performance environments.

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Verification

Designing biological systems can be complex, and molecular interactions that could disrupt the entire system’s behavior are easy to miss. To guard against such design flaws, IBW provides the means to annotate designs with verification statements that the behavior of the specified circuit must satisfy.
Formal verification describes a family of techniques for exhaustively analyzing the logical correctness of a system. The essence of formal verification is analyzing a logical requirement (typically stated in temporal logic (43,44)) against all possible behaviors of the system in question. Formal verification is a well-established research field, and it has many applications, e.g., safety-critical systems, (45) concurrent systems, (46) distributed systems, (47) network protocols, (48) stochastic systems, (49) multiagent systems, (50,51) pervasive systems, (52−54) swarm robotics, (55) and biology (56) as well as some engineering applications. (57−59)
Formal verification is used to validate the system correctness in relation to a desired functionality. In conventional validation methods, the analysis of system behavior mainly relies on “simulation”. However, many important system properties cannot be inferred using simulation. Also, simulation does not guarantee that the system is error-free and reliable because simulation can only show “presence of errors, not their absence”.
Verification statements allow checking for high-level properties and are thus comparable to unit and integration tests in software development. By integrating verification in the core IBL specification, we ensure that test cases can be defined at or near the point of process definition. This prevents potential divergence of specification and verification, which can easily occur in continuous development.
Commonly, verification statements are expressed using temporal logics. (60−62) However, devising correct temporal logic clauses for an intended verification statement can introduce an undesired layer of complexity. To ease verification, IBL therefore allows the user to express statements as text-based natural language narratives.
The verification component has another important feature. It provides a set of so-called property patterns based on the most frequent properties in biology. (56,63) The idea is to categorize most recurring properties into a pattern system by defining a generic representation of instances of numerous properties in the literature.
To identify the most relevant and frequent patterns, we have extensively surveyed the case studies addressing the formal analysis of biological and biochemical systems and derived the property patterns for a variety of common verification scenarios. In Algorithm 6, we provide some example patterns currently implemented in IBW.
Users can integrate verification in their design workflow by instantiating any of these verification statements (presented in Algorithm 6) in IBL models. By tweaking the variables in these patterns they obtain the desired system properties that are required to be satisfied. The verification results indicate that the model either satisfies the properties or that it violates them. A model does not satisfy the properties if the model has an error (e.g., sufficient amount of proteins cannot be produced) or the design is faulty (e.g., certain proteins are never produced). For the former case, the user should refine the model parameters and then repeat the entire process. For the later case, the verification results can be considered as a counterexample and used to revise the design of the system.
IBW automatically translates the verification statements into the syntax of the corresponding temporal logics, which is then delegated to model-checking tools for exhaustive formal verification. The type of property pattern thereby dictates the choice of the verification tool. Currently, IBW interfaces with both nonstochastic—NuSVM (64)—and stochastic—PRISM, (65) MC2 (66)—model-checking tools to check qualitative and quantitative properties, respectively. Stochastic model checkers are useful to verify properties about the likelihood of the observation of certain behavior, whereas nonstochastic model checkers are useful when quantitative analysis might not be needed if, for example, we only want to observe the detection of molecular species rather than measuring their concentration. In such cases, we can only rely on qualitative analysis, where we can apply some abstraction methods, e.g., removing kinetic constants from stochastic models, to reduce the model complexity.
Although formal verification is very useful for system analysis, it has a major drawback: state explosion problem, (67) meaning that the state space grows so quickly that model checking cannot stay scalable. This is especially the case for large models (very common in biology) because model checkers search this large state space exhaustively.
IBW adopts the best practices and new research developments in this field to make the verification process much faster and very efficient for the users. Unlike safety critical systems, where even one in a billion chance of system failure cannot be allowed, biological systems are more tolerable to approximate results based on a high confidence value. So, instead of using analytic methods that calculate precise results, we can use statistical methods that provide approximate results based on a confidence interval. For this reason, when performing verification, wherever possible, IBW uses statistical model checking. (68) The core idea of statistical model checking is to simulate the system for finitely many runs according to the distribution defined by the system, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification. (69) Since the state space is explored partially, the performance is increased. Due to its advantage on scalability, statistical model checking offers much faster results.
We improve statistical model checking further by taking advantage of our GPU parallel implementation of the Gillespie SSA running in an HPC environment. The significant performance gain obtained in our simulations reported in the Simulation section is also utilized in the verification process.
IBW reduces the verification time even further by allowing the user to constrain verification statements to system submodules, including individual cells, devices, and processes. This can be done by simply placing the verification statement in the cell, device, or process in question. Verification will then be performed only for the submodule that the statement occurs in. This provides significant performance gains compared to conventional approaches, where verification is applied to the whole system.
All these state of the art solutions make IBW one of the fastest and the most scalable tools available in the verification of large biological systems.

Biocompilation

Subsequent to successful verification and simulation, the design must be mapped to specific nucleotide sequences that can be produced and incorporated into host cells. IBW allows for automated compilation of a specified synthetic circuit into eventual genetic sequence information by interfacing with the ATGC (70) (Assistant To Genetic Compilation) component. With no user intervention, ATGC: (a) completes abstract device specifications by adding ribosome binding sites (RBS), spacers and effective terminators, (b) finds a viable arrangement of the parts by solving an equivalent integer optimization problem, and (c) finds the parts’ sequences either in online repositories (e.g., iGem) or in a built-in database, populated by parts from Biofab (71) and REbase. (72) Unless explicitly stated, RBS sequences are generated with Salis’ RBS calculator (73) and a user-specified initiation translation rate. ATGC thus automatically produces a biologically plausible sequence reflecting the original aim of the user. A number of ATGC directives allow the user to control its output. The types of constraints available at present (to increase in the future) were directly inspired by situations encountered by experimentalists. For example, one such directive controls, the automatic insertion of cloning sites. Since cloning sites are noncoding sequences, they are typically not present in high-level specifications, which primarily model the dynamics of gene expression. Instead, the user employs an ATGC directive that inserts a placeholder for a restriction site whose sequence is calculated by the biocompiler from a large curated database of restriction enzymes. The biocompiler ensures that the chosen cloning site sequence does not appear in other parts of the full sequence. In particularly constrained situations, ATGC will perform codon replacement in order to find a solution. Other directives allow existing DNA fragments, which may already contain several components, to be reused. The tool takes into consideration the bidirectionality of double-stranded DNA, which allows certain devices to be present in each of the two strands. This mimics the optimization present in natural devices.

Compatibility with Other Standards

After an IBL model is optionally biocompiled, the user has the option of exporting it as SBOL (supporting two modes of export one with compiled sequences and one without) (32) or SBML, (33) which are both compatible with a growing number of synthetic biology softwares. For example, j5 (74) can take an SBOL input and produce an assembly protocol for the construct, which is typically the next step after designing the DNA sequence. In addition to the export feature, IBW can also import from SBOL and SBML, generated by other tools, e.g., iBioSim (18) and Cello. (19)
The import IBW functionality takes as input an SBML/SBOL model and translates its different entities into their equivalent IBL representation. The translation from SBML/SBOL to IBL is implemented in Java and features the JSBML (https://github.com/sbmlteam/jsbml) and LibSBOLj (https://github.com/SynBioDex/libSBOLj) libraries for parsing SBML and SBOL models, respectively. Upon parsing, the corresponding abstract syntax tree of the input model is traversed and the corresponding semantic entities are converted into their IBL counterparts.
In a similar fashion, the IBW export functionality allows for an IBL model to be exported into an equivalent SBML/SBOL representation. The translation from IBL to SBML/SBOL is also implemented in Java and takes advantage of the JSBML and LibSBOLj. By traversing the internal IBW data structure corresponding to the IBL model, each IBL semantic entity is converted into an equivalent SBML/SBOL semantic (language-agnostic) data structure, which is constructed using Java classes defined in the above-mentioned libraries. Once a semantic SBML/SBOL data structure is obtained, the final SBML/SBOL (XML-formatted) models are generated by taking advantage of the same libraries.
SBOL conversion captures the structural information and hierarchy of the IBL model. This hierarchy (approximately region → cell → molecule/device) is translated into SBOL objects, and if the model is further biocompiled, biocompilation specific information (sequences, cloning sites, and ribosome-binding sites) are also retained. However, SBOL omits numerical information like concentrations, rate constants, compartment sizes, and units. This prevents the SBOL model from being simulated and verified. On the other hand, SBML conversion captures functional aspects of the IBL model, which includes the previously mentioned numerical information. SBML also allows us to retain the structural hierarchy, although biocompilation-specific features like DNA structures cannot be expressed.
IBW targets these shortcomings by making all computational features accessible to the SBOL and SBML communities. We note that IBL is a true programming language to be used by humans to program biology, whereas SBOL and SBML only provide data models, and are not biological programming languages. IBL can be compiled down to SBOL and/or SBML for standardized exchange but in doing so information that is currently captured by IBL will be lost, e.g., information about verification or biocompilation constraints.
We note that ShortBOL (75) has been recently introduced to generate SBOL files using a human readable language. However, it is not a comprehensive programming language like IBL. It does not capture the functional information (i.e., behavioral models at the molecular level). Also, some unique elements of IBL, e.g., verification and biocompilation statements, cannot be expressed in ShortBOL.

Methods

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Implementation

IBW has been implemented as an Eclipse rich client application using Xtext as a domain specific language framework. It is released under the MIT license and both user and developer IBW editions can be obtained from https://infobiotics.org.

Case Studies

We demonstrate the applicability of IBW and IBL using a number of case studies from the literature: Gardner et al.’s “toggle switch”, (76) Elowitz and Leibler’s “repressilator”, (77) Dockery and Keener’s “quorum sensor”, (78) and Myers’ “logic gates”. (79) For each case, we discuss the respective IBL specification, along with its verification, simulation, and biocompilation results. The parameters used in the experiments are presented in the Performance Evaluation section. The case studies can be accessed at https://infobiotics.org/casestudies/casestudies.html. The source files as well as the experimental data used in this paper are available to download at https://infobiotics.org/_static/experiments_2021.06.11.zip.

The Toggle Switch

The toggle switch is a seminal synthetic biological circuit that consists of two mutually repressing operons, (76) with lacI being regulated by a Plambda promoter and cI (a repressor of Plambda) being in turn regulated by Plac. The operon that codes for CI also codes for green fluorescent protein (GFP). Using this repressor pair allows the toggle switch to be activated by induction via IPTG and deactivated via aTc. The negative feedback cycle ensures that the switch maintain its on- or off-state even when the triggering inducer is washed out. The bistable behavior of the switch depends on the cooperativity of the repressor proteins—more specifically, on the right choice of regulators and promoters and their correct dimerization. Modeling with IBW can help develop a feasible design.
We simulate the toggle switch in four separate scenarios. We first suspended the cell in a region rich in IPTG and simulate its response in CI, LacI, and GFP production over a period of 1 h. We then use the average of the total CI, LacI, and GFP concentrations as initial values for CI, LacI, and GFP for simulation of the cell in an IPTG-depleted region. Again, the average final state after 1 h is used to initialize the cell state in a region rich in aTc, a.s.o. The averages and standard deviations of 50 simulation runs each are shown in Figure 4.

Figure 4

Figure 4. Four sequential stochastic simulations of the toggle switch. In the first simulation, the cell is suspended in IPTG, leading to the activation of the switch and production of GFP (green trace) and CI (red trace). In the second one, CI and GFP production is maintained despite the absence of IPTG. In the third simulation, the cell is suspended in aTc, leading to the deactivation of the switch, production of LacI (blue trace) and decay of GFP. In the final one, the switch resides in its off state despite the absence of aTc. Traces show the mean and standard deviations of 50 simulation runs.

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Verification is used to check the correctness of the system and to validate the system requirements using the probabilistic model checkers (see Table 1). The first property verifies that the suspension of the cell with IPTG will lead to the production of GFP. The second property verifies that once the cell is suspended with IPTG, the GFP concentration will eventually exceed 7.5 μM with a probability of 0.82. The third property verifies that GFP is produced at least three times more than CI despite the absence of IPTG with a probability 0.99. The fourth property verifies that the GFP production will decline if the cell is suspended with aTc.
Table 1. Verified Properties
#verification statementresult
1VERIFY [IPTG ≥ 10 μM] IS FOLLOWED BY [GFP > 7.5 μM]T
2VERIFY [IPTG ≥ 10 μM] IS FOLLOWED BY [GFP > 7.5 μM] WITH PROBABILITY ?0.82
3VERIFY [IPTG = 0 μM] IS FOLLOWED BY [GFP/CI > 3] WITH PROBABILITY ?0.99
4VERIFY [GFP] EVENTUALLY DECREASES WITH PROBABILITY ? GIVEN [aTc = 100 μM]1.0
To obtain a viable genetic sequence that implements the toggle switch, all that needs to be done is specify the sequences of the two promoters and three regulated proteins. We do this in IBW by pointing to the respective iGEM registry parts: BBa_R0010 for Plac, BBa_R0051 for Plambda, BBa_K105004 for cI, BBa_E0040 for gfpmut3, and BBa_C0012 for lacI. To reproduce a genetic arrangement of the published system, we introduce two compilation constraints that ensure that the second repressor operon is inverted (ATGC DIRECTION: BACKWARD in the respective device) and that the two promoters are adjacent to each other (ATGC ARRANGE Plambda,Plac).
The ATGC biocompiler automatically fetches the sequence information, adds ribosome binding sites and terminators and assembles the concatenated gene sequence in accordance with the given layout constraints. The result of the biocompilation run is shown in Figure 5.

Figure 5

Figure 5. Biocompiler result for the toggle switch specification. Sequence information of promoters, coding refions and terminators is drawn from several online repositories (here the iGem parts registry), and ribosome binding sites are automatically calculated using Salis’ ribosome binding site calculator.

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The Repressilator

An equally seminal early synthetic biological device is a synthetic genetic oscillator termed the “repressilator”. (77) Similar to the previous toggle switch, the repressilator consists of mutually repressing repressors, but this time it features three instead of two chained interacting operons. As a result, none of the promoter activation states remains stable and the operons sequentially activate and inactivate each other—resulting in continued global oscillations.
Stochastic simulation confirms continued oscillations of the regulating proteins LacI, CI, and TetR (cf. Figure 6).

Figure 6

Figure 6. Simulation traces (mean and standard deviations of 50 runs using tau-leaping) for the LacI (blue), CI (red), and TetR (green) dimers over 27 h.

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We can validate some system behavior using verification (see Table 2). Property 1 and 2 query the oscillatory behavior of LacI by checking the LacI concentration going below and above 1.25 μM repeatedly (with a probability 0.48 and 0.52, respectively). Property 3 verifies that the steady state concentration of CI is between 0.5 and 2 μM with probability 0.92.
Table 2. Verified Properties
#verification statementresult
1VERIFY [LacI ≤ 1.25 μM] HOLDS INFINITELY OFTEN WITH PROBABILITY ?0.48
2VERIFY [LacI > 1.25 μM] HOLDS INFINITELY OFTEN WITH PROBABILITY ?0.52
3VERIFY [CI ≥ 0.5 μM] AND [CI < 2 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ?0.92
The biocompiler output is shown in Figure 7.

Figure 7

Figure 7. Biocompiler result for the repressilator specification.

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Quorum Sensing

Quorum sensing is an ability expressed by certain bacterial species where a shift in population density leads to changes in gene expression. More specifically, Pseudomonas aeruginosa relies on a quorum sensing system comprised of two genes, lasR and lasI. (78)lasR codes for a transcriptional activator protein that is activated by 3-oxo-C12-HSL, an autoinducer synthesized by lasI. Their dimer promotes both lasI and lasR activity. At higher cell densities, the concentration of 3-oxo-C12-HSL rises, which leads to changes in P. aeruginosa associated with population density.
In the previous two case studies, we have shown how simulation and verification can be used together. The system dynamics can actually be analyzed using verification alone. In Table 3, we provide a list of queries that we can ask the model. For instance, one can verify the appearance of LasR later in the system, but not at the beginning, i.e., the system will eventually lead to the production of the LasR protein (Property 1). The very same property can also be verified for 3-oxo-C12-HSL (Property 2). This query can be complemented by a more grained analysis such as within a time period some threshold concentrations for LasR (Property 3) and 3-oxo-C12-HSL (Property 4) are reached. Finally, we verify it is very likely that the concentrations remain within an upper and lower bound in the steady-state (Property 5 and 6). From these results we can infer that the quorum sensing system works as described.
Table 3. Verified Properties
#verification statementresult
1VERIFY [LasR] EVENTUALLY INCREASES WITH PROBABILITY ?1.0
2VERIFY [HSL] EVENTUALLY INCREASES WITH PROBABILITY ?1.0
3VERIFY [LasR ≥ 0.1 μM] EVENTUALLY HOLDS WITHIN [0,1] s WITH PROBABILITY ?1.0
4VERIFY [HSL≥ 3 μM] EVENTUALLY HOLDS WITHIN [0,1] s WITH PROBABILITY ?1.0
5VERIFY [[LasR ≥ 0.005 μM] AND [LasR ≤ 0.15 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ?0.98
6VERIFY [[HSL ≥ 0.05 μM] AND [HSL ≤ 4.2 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ?0.72

Genetic Logic Gates

In this section, we will demonstrate the compatibility of IBW with standard data exchange formats (e.g., SBML and SBOL). IBW allows the users to upload SBML/SBOL files. Once such a model file is uploaded, the tool automatically translates the model to the IBL language. The users can then edit their model in IBL and add IBL specific annotations, e.g., verification statements, part information, etc., to utilize IBW features in their analysis.
Here, we will import some biological circuits based on logic gates implemented in SBML. (79) In our experiments, we consider the genetic NOT, AND, and NOR gates. All the models, originally in SBML, were automatically translated to IBL.
Each gate is composed of several genetic components, interacting with each other. For example, the AND gate circuit receives two proteins, LacI and TetR, as input. LacI and TetR inhibit two promoters that produce the CI protein. When the CI concentration goes below a certain threshold level, another promoter is activated, which produces green fluorescent protein (GFP) as output. The corresponding kinetic reactions are described by a number of equations captured in the SBML models. The circuit designs and their corresponding truth tables are shown in Figure 8.

Figure 8

Figure 8. Genetic NOT, AND, and NOR gate circuits, and the corresponding truth tables.

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Figure 9 shows the stochastic simulations of these gates. The simulation results show that the circuits exhibit the expected behavior. For example, for the NOT gate, the GFP concentration follows an opposite trend of the TetR concentration. For AND, the GFP concentration starts increasing as both LacI and TetR concentrations are high in the beginning; this is followed by a decrease in the GFP concentration as LacI and TetR concentrations decrease significantly. Similarly, for the NOR gate, the GFP concentration remains very low as LacI and TetR concentrations are high; GFP is then observed in high concentration as both LacI and TetR degrade in time.

Figure 9

Figure 9. Simulation results. (a) NOT gate. (b) AND gate. (c) NOR gate.

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Although simulation results are useful to observe the general behaviors of the circuits, they fall short in determining the correct Boolean logic function. The challenge here is that the simulation data do not clearly show the classification of the threshold concentration levels into the Boolean values, “low” and “high”. (80)
To address this limitation, we can utilize verification to make a more fine-grained analysis and hence can identify more accurate threshold values for these gates. The verification statements used in our experiments are presented in Table 4. Here, we assume the “high” input is achieved if the concentration of a species exceeds a threshold value (Thr), e.g., GFP > Thr; similarly, the low input is triggered if the concentration level goes below the threshold, e.g., GFP < Thr.
Table 4. Properties Verified for Each Gatea
circuit#verification statement
NOT1aVERIFY [TetR > Thr molecules] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ?
 1bVERIFY [TetR < Thr molecules] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ?
AND2aVERIFY [[TetR > Thr molecules] AND [LacI > Thr molecules]] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ?
 2bVERIFY [[TetR < Thr molecules] OR [LacI < Thr molecules]] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ?
NOR3aVERIFY [[TetR > Thr molecules] AND [LacI > Thr molecules]] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ?
 3bVERIFY [[TetR < Thr molecules] OR [LacI < Thr molecules]] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ?
a

Thr represents a threshold value.

The Boolean logic function for each gate is captured by two verification properties (Table 4). For example, for the AND gate, the first property means that if the input proteins TetR and LacI are “high”, the output protein GFP is also “high”, whereas the second property means if any of the input proteins is “low”, then the output protein is also “low”.
The verification results are presented in Table 5. Here we assume that the highest probability result represents the most desired behavior. Since the Boolean logic function of each gate is defined by two properties, we consider the product of these probabilities as the “score” that represents the accuracy of a gate. The highest score defines the best threshold value. Using this method, we can also consider/identify different threshold values for different species.
Table 5. Verification Results with Respect to the Threshold (Thr) Value for Each Gatea
NOT
ThrProb1aProb1bscore
50.690.950.66
100.990.850.84
151.00.770.77
201.00.650.65
251.00.550.55
301.00.500.50
AND
ThrProb2aProb2bscore
51.00.180.18
101.00.470.47
150.980.760.74
200.980.900.88
250.960.960.92
300.950.970.92
NOR
ThrProb3aProb3bscore
50.960.820.79
100.990.700.69
151.00.590.59
201.00.390.39
251.00.190.19
301.00.130.13
a

Score is calculated by multiplying the probabilities obtained for two properties (e.g., 1a and 1b).

We note that SBML only captures behavioral models of biological systems at the molecular level and it does not contain any genetic information on molecular species. Hence, there is not any genetic part information imported by IBW to run the biocompilation. SBOL models, on the other hand, describe structural and basic qualitative behavioral aspects with genetic part information, but the current SBOL standards do not include any quantitative information regarding molecular dynamics, hindering to run the simulation and verification.

Performance Evaluation

In this section, we provide a brief performance evaluation of the different steps. Table 6 presents the minimum and maximum times (in seconds) of each computational step for the case studies presented above.
Table 6. Simulation, Verification, and Biocompilation Times of the Case Studiesa
case studysimulation (s)verification (s)biocompilation (s)
toggle switch[0.11, 0.26][0.58, 0.91]64
repressilator[11.12, 13.56][12.9, 13.43]59
quorum sensing[12, 15][0.57, 0.68] 
logic gates[0.10, 0.17][1.45, 4.5] 
a

The experiments have been carried out using the following parameters: toggle switch: number of runs: 50, max time: 1000 s, interval: 10 s; repressilator: number of runs: 50, max time: 97 200 s (27 h), interval: 500 s; quorum sensing: number of runs: 50, max time: 10 ms, interval: 1 ms; logic gates: number of runs: 100, max time: 500 s, interval: 1 s.

The simulation experiments have been carried out using the GPU simulator based on the following VM configurations: 8 GB Memory, 6 Cores, Intel Core i7–4720HQ CPU @ 2.60 GHz. The verification experiments have been carried out using the statistical model checking which also utilizes the GPU simulator. As the results show, the simulation and verification run very fast thanks to the performance improvements provided the IBW. The biocompilation process for two case studies took around a minute. This is mainly due to the fact that all part sequences are obtained from online repositories (which requires setting up connection with remote servers) and the biocompilation process runs the RBS optimizer.

Conclusion

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In this paper, we have presented the Infobiotics Workbench, a computer-aided design suite for synthetic biology, supporting an iterative workflow that begins with specification of the desired synthetic system and is followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into a genetic construct.
IBW features a unique domain-specific language, providing a simple combined grammar for modeling, verification, and biocompilation statements. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the manual translation requirement for standalone applications.
We have shown the usability and applicability of the language and software using a number of case studies, toggle switch, repressilator, quorum sensing, and logic gates. For each case study, we have discussed the respective IBL specification, along with its verification, simulation, and biocompilation results.
IBW has quite unique features, but the current version has also some limitations. Currently, the language does not support multicellular bacterial populations. The tool can neither predict missing model parameters (e.g., kinetic rates) nor optimize them. Our SBOL translator relies on the information provided in an SBOL file. If a part “role” is missing in the SBOL file, the translator cannot predict the role type, and assigns “UNKNOWN” role in the IBL translation, leaving this to the user to change manually. Also, the biocompiler only uses RBS optimizer; it does not do any other DNA optimization.
In our future work, we aim to extend the IBL language with a “spatial” dimension, so as to be able to represent specific geometries and strains. These will allow IBL to model specific formations, e.g., biofilm coatings, and deal with large spatially distributed bacterial colonies. This extension entails investigating spatiotemporal logics and verification methods for synthetic biology. We will develop a web version of IBW to provide a more intuitive interface for experimental biologists. We will work on improving the RBS optimizer, which will reduce the biocompilation time. We will also broaden the spectrum of case studies. We will, in particular, explore more systems on synthetic drug design.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.1c00143.

  • Table S1: a comparison of the features of the most well-known computer aided synthetic biology design tools (PDF)

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
  • Authors
    • Laurentiu Mierla - Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.
    • Harold Fellermann - Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.;  Orcidhttps://orcid.org/0000-0001-5861-1945
    • Christophe Ladroue - Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
    • Bradley Brown - Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.
    • Anil Wipat - Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.;  Orcidhttps://orcid.org/0000-0001-7310-4191
    • Jamie Twycross - School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, U.K.
    • Boyang Peter Dun - Department of Computer Science, Stanford University, Stanford, California 94305, United States
    • Sara Kalvala - Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
    • Marian Gheorghe - Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.
  • Author Contributions

    S.Ko., L.M., H.F., S.Ka., M.G., and N.K. designed the research. S.Ko., L.M., H.F., and C.L. performed the research. S.Ko., L.M., H.F., C.L., B.B., J.T., and B.P.D. analyzed the data. S.Ko., H.F., and N.K. wrote the paper. L.M., C.L., B.B., A.W., J.T., B.P.D., S.Ka., and M.G. edited the paper.

  • Notes
    The authors declare no competing financial interest.

    The data and models that support the findings of this work are openly available at https://infobiotics.org/_static/experiments_2021.06.11.zip. All code associated with this manuscript is publicly available at https://github.com/Infobiotics/ibw.

Acknowledgments

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The work of S.K. is supported by EPSRC (EP/R043787/1). N.K., A.W., and B.B. acknowledge a Royal Academy of Engineering Chair in Emerging Technologies award and an EPSRC programme grant (EP/N031962/1).

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    Kylilis, Nicolas; Riangrungroj, Pinpunya; Lai, Hung-En; Salema, Valencio; Fernandez, Luis Angel; Stan, Guy-Bart V.; Freemont, Paul S.; Polizzi, Karen M.
    ACS Sensors (2019), 4 (2), 370-378CODEN: ASCEFJ; ISSN:2379-3694. (American Chemical Society)
    Whole-cell biosensors can form the basis of affordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing, but to date the detection of analytes such as proteins that cannot easily diffuse across the cell membrane has been challenging. Here we developed a novel biosensing platform based on cell agglutination using an E. coli whole-cell biosensor surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design to detect a model analyte at nanomolar concns. Moreover, we show that the design architecture is flexible by building assays optimized to detect a range of model analyte concns. using straightforward design rules and a math. model. Finally, we re-engineer our whole-cell biosensor for the detection of a medically relevant biomarker by the display of two different nanobodies against human fibrinogen and demonstrate a detection limit as low as 10 pM in dild. human plasma. Overall, we demonstrate that our agglutination technol. fulfills the requirement of POC testing by combining low-cost nanobody prodn., customizable detection range and low detection limits. This technol. has the potential to produce affordable diagnostics for field-testing in the developing world, emergency or disaster relief sites, as well as routine medical testing and personalized medicine.
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  10. 10
    Sheth, R. U. and Wang, H. H. (2018) DNA-based memory devices for recording cellular events. Nat. Rev. Genet. 19, 718– 732,  DOI: 10.1038/s41576-018-0052-8
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    10
    DNA-based memory devices for recording cellular events
    Sheth, Ravi U.; Wang, Harris H.
    Nature Reviews Genetics (2018), 19 (11), 718-732CODEN: NRGAAM; ISSN:1471-0056. (Nature Research)
    A review. Measuring biol. data across time and space is crit. for understanding complex biol. processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biol. recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biol. events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quant. characterization, modeling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.
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  11. 11
    Karr, J., Sanghvi, J., Macklin, D., Gutschow, M., Jacobs, J., Bolival, B., Assad-Garcia, N., Glass, J., and Covert, M. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150, 389– 401,  DOI: 10.1016/j.cell.2012.05.044
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    11
    A Whole-Cell Computational Model Predicts Phenotype from Genotype
    Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N.; Gutschow, Miriam V.; Jacobs, Jared M.; Bolival, Benjamin; Assad-Garcia, Nacyra; Glass, John I.; Covert, Markus W.
    Cell (Cambridge, MA, United States) (2012), 150 (2), 389-401CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
    Understanding how complex phenotypes arise from individual mols. and their interactions is a primary challenge in biol. that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its mol. components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and exptl. measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA assocn. and an inverse relationship between the durations of DNA replication initiation and replication. In addn., exptl. anal. directed by model predictions identified previously undetected kinetic parameters and biol. functions. We conclude that comprehensive whole-cell models can be used to facilitate biol. discovery.
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  12. 12
    Naylor, J., Fellermann, H., Ding, Y., Mohammed, W. K., Jakubovics, N. S., Mukherjee, J., Biggs, C. A., Wright, P. C., and Krasnogor, N. (2017) Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations. ACS Synth. Biol. 6, 1194– 1210,  DOI: 10.1021/acssynbio.6b00315
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    12
    Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations
    Naylor, Jonathan; Fellermann, Harold; Ding, Yuchun; Mohammed, Waleed K.; Jakubovics, Nicholas S.; Mukherjee, Joy; Biggs, Catherine A.; Wright, Phillip C.; Krasnogor, Natalio
    ACS Synthetic Biology (2017), 6 (7), 1194-1210CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    Simbiotics is a spatially explicit multiscale modeling platform for the design, simulation and anal. of bacterial populations. Systems ranging from planktonic cells and colonies, to biofilm formation and development may be modeled. Representation of biol. systems in Simbiotics is flexible, and user-defined processes may be in a variety of forms depending on desired model abstraction. Simbiotics provides a library of modules such as cell geometries, phys. force dynamics, genetic circuits, metabolic pathways, chem. diffusion and cell interactions. Model defined processes are integrated and scheduled for parallel multithread and multi-CPU execution. A virtual lab provides the modeler with anal. modules and some simulated lab equipment, enabling automation of sample interaction and data collection. An extendable and modular framework allows for the platform to be updated as novel models of bacteria are developed, coupled with an intuitive user interface to allow for model definitions with minimal programming experience. Simbiotics can integrate existing stds. such as SBML, and process microscopy images to initialize the 3D spatial configuration of bacteria consortia. Two case studies, used to illustrate the platform flexibility, focus on the phys. properties of the biosystems modeled. These pilot case studies demonstrate Simbiotics versatility in modeling and anal. of natural systems and as a CAD tool for synthetic biol.
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  13. 13
    Sanassy, D., Widera, P., and Krasnogor, N. (2015) Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology. ACS Synth. Biol. 4, 39– 47,  DOI: 10.1021/sb5001406
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    13
    Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology
    Sanassy, Daven; Widera, Pawel; Krasnogor, Natalio
    ACS Synthetic Biology (2015), 4 (1), 39-47CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochem. systems at low species concns. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to det. which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochem. model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochem. model with the predicted fastest algorithm as the simulation engine. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.
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  14. 14
    Gorochowski, T. E., Hauert, S., Kreft, J.-U., Marucci, L., Stillman, N. R., Tang, T.-Y. D., Bandiera, L., Bartoli, V., Dixon, D. O. R., Fedorec, A. J. H., Fellermann, H., Fletcher, A. G., Foster, T., Giuggioli, L., Matyjaszkiewicz, A., McCormick, S., Montes Olivas, S., Naylor, J., Rubio Denniss, A., and Ward, D. (2020) Toward Engineering Biosystems With Emergent Collective Functions. Front. Bioeng. Biotechnol. 8, 705,  DOI: 10.3389/fbioe.2020.00705
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    14
    Toward Engineering Biosystems With Emergent Collective Functions
    Gorochowski Thomas E; Ward Daniel; Hauert Sabine; Marucci Lucia; Stillman Namid R; Bartoli Vittorio; Giuggioli Luca; McCormick Scott; Montes Olivas Sandra; Rubio Denniss Ana; Kreft Jan-Ulrich; Foster Tim; Tang T-Y Dora; Tang T-Y Dora; Bandiera Lucia; Dixon Daniel O R; Fedorec Alex J H; Fellermann Harold; Naylor Jonathan; Fletcher Alexander G; Matyjaszkiewicz Antoni
    Frontiers in bioengineering and biotechnology (2020), 8 (), 705 ISSN:2296-4185.
    Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.
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  15. 15
    Jiang, S., Wang, Y., Kaiser, M., and Krasnogor, N. (2020) NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 36, 3482– 3492,  DOI: 10.1093/bioinformatics/btaa163
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    15
    NIHBA: a network interdiction approach for metabolic engineering design
    Jiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, Natalio
    Bioinformatics (2020), 36 (11), 3482-3492CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
    Motivation: Flux balance anal. (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochem. overprodn. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solns. in a single run. Results: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solns. that meet users' prodn. requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ~ 1 h, e.g. studied in this article).
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    Misirli, G., Nguyen, T., McLaughlin, J. A., Vaidyanathan, P., Jones, T. S., Densmore, D., Myers, C., and Wipat, A. (2019) A Computational Workflow for the Automated Generation of Models of Genetic Designs. ACS Synth. Biol. 8, 1548– 1559,  DOI: 10.1021/acssynbio.7b00459
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    16
    A Computational Workflow for the Automated Generation of Models of Genetic Designs
    Misirli, Goksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S.; Densmore, Douglas; Myers, Chris; Wipat, Anil
    ACS Synthetic Biology (2019), 8 (7), 1548-1559CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    Computational models are essential to engineer predictable biol. systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing stds. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biol. Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biol. Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biol. systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.
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  17. 17
    Tellechea-Luzardo, J., Winterhalter, C., Widera, P., Kozyra, J., de Lorenzo, V., and Krasnogor, N. (2020) Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering. ACS Synth. Biol. 9, 536– 545,  DOI: 10.1021/acssynbio.9b00400
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    17
    Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering
    Tellechea-Luzardo, Jonathan; Winterhalter, Charles; Widera, Pawel; Kozyra, Jerzy; de Lorenzo, Victor; Krasnogor, Natalio
    ACS Synthetic Biology (2020), 9 (3), 536-545CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    As DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biol. engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnol. and digital technol., a deficit in software and lab. techniques diminishes the ability to make biotechnol. more agile, reproducible and transparent while, at the same time, limiting the security and safety of synthetic biol. constructs. To partially address some of these problems, this paper presents an approach for phys. linking engineered cells to their digital footprint-we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialized version control system for collaborative strain engineering via simple barcoding protocols.
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  18. 18
    Watanabe, L., Nguyen, T., Zhang, M., Zundel, Z., Zhang, Z., Madsen, C., Roehner, N., and Myers, C. (2019) iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synth. Biol. 8, 1560,  DOI: 10.1021/acssynbio.8b00078
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    18
    iBioSim 3: A Tool for Model-Based Genetic Circuit Design
    Watanabe, Leandro; Nguyen, Tramy; Zhang, Michael; Zundel, Zach; Zhang, Zhen; Madsen, Curtis; Roehner, Nicholas; Myers, Chris
    ACS Synthetic Biology (2019), 8 (7), 1560-1563CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    The iBioSim tool has been developed to facilitate the design of genetic circuits via a model-based design strategy. This paper illustrates the new features incorporated into the tool for DNA circuit design, design anal., and design synthesis, all of which can be used in a workflow for the systematic construction of new genetic circuits.
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  19. 19
    Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D., and Voigt, C. A. (2016) Genetic circuit design automation. Science 352, aac7341,  DOI: 10.1126/science.aac7341
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    Gutiérrez, M., Gregorio-Godoy, P., Pérez del Pulgar, G., Muñoz, L. E., Sáez, S., and Rodríguez-Patón, A. (2017) A New Improved and Extended Version of the Multicell Bacterial Simulator GRO. ACS Synth. Biol. 6, 1496– 1508,  DOI: 10.1021/acssynbio.7b00003
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    20
    A New Improved and Extended Version of the Multicell Bacterial Simulator gro
    Gutierrez, Martin; Gregorio-Godoy, Paula; Perez del Pulgar, Guillermo; Munoz, Luis E.; Saez, Sandra; Rodriguez-Paton, Alfonso
    ACS Synthetic Biology (2017), 6 (8), 1496-1508CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    Gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biol. prototyping tool for simulating multicellular biocircuits and microbial consortia. The authors present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is 1-2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in <10 min. A new library, CellEngine, accelerates the resoln. of spatial phys. interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. The authors also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, the authors provide studies and novel examples ranging from synthetic biol. to evolutionary microbiol. The authors believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biol. designs.
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    Chandran, D. and Sauro, H. M. (2012) Hierarchical modeling for synthetic biology. ACS Synth. Biol. 1, 353– 364,  DOI: 10.1021/sb300033q
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    21
    Hierarchical Modeling for Synthetic Biology
    Chandran, Deepak; Sauro, Herbert M.
    ACS Synthetic Biology (2012), 1 (8), 353-364CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    One of the characteristics of synthetic biol. is that it often combines math. modeling with exptl. work. The link between modeling and expts. is carried out by human researchers who have a conceptual understanding of the underlying biol. system. At present, there is no method for representing a conceptual description that can be used to connect math. models and exptl. data, esp. sequence annotations, pertaining to the same underlying biol. system. One reason for this limitation is that there can exist different math. models of the same biol. system. In such cases, the same annotation in a DNA sequence would map differently to different models of the same system. In order to enable software support for synthetic biol., a software framework is needed such that it is able to capture a conceptual description of a biol. system, including quant. values, without confining itself to one math. model. The novel use of hierarchical modeling inside TinkerCell provides one potential software soln. for representing a "conceptual diagram" of a biol. system. The conceptual diagram does not assume any underlying model. Rather, the diagram is mapped automatically to one of several models. The diagram can then contain information relevant for both modeling and exptl. work. Computer-aided design (CAD) can be very useful to synthetic biol. CAD allows engineers to spend more effort at the design stage and less at the construction stage by automatically performing many tasks that are currently performed by human researchers. The ability to automatically link models and exptl. results will be one step in the development of practical CAD systems for synthetic biol.
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  22. 22
    Bilitchenko, L., Liu, A., Cheung, S., Weeding, E., Xia, B., Leguia, M., Anderson, J. C., and Densmore, D. (2011) EUGENE - A domain specific language for specifying and constraining synthetic biological parts, devices, and systems. PLoS One 6, e18882,  DOI: 10.1371/journal.pone.0018882
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    22
    Eugene - a domain specific language for specifying and constraining synthetic biological parts, devices, and systems
    Bilitchenko, Lesia; Liu, Adam; Cheung, Sherine; Weeding, Emma; Xia, Bing; Leguia, Mariana; Anderson, J. Christopher; Densmore, Douglas
    PLoS One (2011), 6 (4), e18882CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    Background: Synthetic biol. systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their compn. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biol. designs based on biol. parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. Results: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or phys. assembly. Eugene creates functional designs quickly and cost-effectively. Conclusions: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired ion hierarchy in synthetic biol. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and phys. device assembly.
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    Beal, J., Lu, T., and Weiss, R. (2011) Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks. PLoS One 6, e22490,  DOI: 10.1371/journal.pone.0022490
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    23
    Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks
    Beal, Jacob; Lu, Ting; Weiss, Ron
    PLoS One (2011), 6 (8), e22490CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    Background: The field of synthetic biol. promises to revolutionize our ability to engineer biol. systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biol. itself and the lag in our ability to design and optimize sophisticated biol. circuitry. Methodol./Principal Findings: To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biol.-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biol. system design with our platform, and show that our compiler optimizations can yield significant redns. in the no. of genes (∼50) and latency of the optimized engineered gene networks. Conclusions/Significance: Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biol. systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biol. relevant compiler optimizations, providing an important foundation for the development of sophisticated biol. systems.
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  24. 24
    Villalobos, A., Ness, J. E., Gustafsson, C., Minshull, J., and Govindarajan, S. (2006) Gene Designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinf. 7, 285,  DOI: 10.1186/1471-2105-7-285
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    24
    Gene Designer: a synthetic biology tool for constructing artificial DNA segments
    Villalobos Alan; Ness Jon E; Gustafsson Claes; Minshull Jeremy; Govindarajan Sridhar
    BMC bioinformatics (2006), 7 (), 285 ISSN:.
    BACKGROUND: Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes. RESULTS: Gene Designer is a stand-alone software for fast and easy design of synthetic DNA segments. Users can easily add, edit and combine genetic elements such as promoters, open reading frames and tags through an intuitive drag-and-drop graphic interface and a hierarchical DNA/Protein object map. Using advanced optimization algorithms, open reading frames within the DNA construct can readily be codon optimized for protein expression in any host organism. Gene Designer also includes features such as a real-time sliding calculator of oligonucleotide annealing temperatures, sequencing primer generator, tools for avoidance or inclusion of restriction sites, and options to maximize or minimize sequence identity to a reference. CONCLUSION: Gene Designer is an expandable Synthetic Biology workbench suitable for molecular biologists interested in the de novo creation of genetic constructs.
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    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD28vkvVyhug%253D%253D&md5=5e6e9a72714e8f69be3d8d351812a351
  25. 25
    Gaspar, P., Oliveira, J. L., Frommlet, J., Santos, M., and Moura, G. (2012) EuGene: maximizing synthetic gene design for heterologous expression. Bioinformatics 28, 2683,  DOI: 10.1093/bioinformatics/bts465
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    EuGene: maximizing synthetic gene design for heterologous expression
    Gaspar, Paulo; Oliveira, Jose Luis; Frommlet, Joerg; Santos, Manuel A. S.; Moura, Gabriela
    Bioinformatics (2012), 28 (20), 2683-2684CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Numerous software applications exist to deal with synthetic gene design, granting the field of heterologous expression a significant support. However, their dispersion requires the access to different tools and online services in order to complete one single project. Analyzing codon usage, calcg. codon adaptation index (CAI), aligning orthologs and optimizing genes are just a few examples. A software application, EuGene, was developed for the optimization of multiple gene synthetic design algorithms. In a seamless automatic form, EuGene calcs. or retrieves genome data on codon usage (relative synonymous codon usage and CAI), codon context (CPS and codon pair bias), GC content, hidden stop codons, repetitions, deleterious sites, protein primary, secondary and tertiary structures, gene orthologs, species housekeeping genes, performs alignments and identifies genes and genomes. The main function of EuGene is analyzing and redesigning gene sequences using multi-objective optimization techniques that maximize the coding features of the resulting sequence.
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    Czar, M. J., Cai, Y., and Peccoud, J. (2009) Writing DNA with GenoCAD. Nucleic Acids Res. 37, W40– W47,  DOI: 10.1093/nar/gkp361
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    Writing DNA with GenoCAD
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    Nucleic Acids Research (2009), 37 (Web Server), W40-W47CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
    Chem. synthesis of custom DNA made to order calls for software streamlining the design of synthetic DNA sequences. GenoCAD (www.genocad.org) is a free web-based application to design protein expression vectors, artificial gene networks and other genetic constructs composed of multiple functional blocks called genetic parts. By capturing design strategies in grammatical models of DNA sequences, GenoCAD guides the user through the design process. By successively clicking on icons representing structural features or actual genetic parts, complex constructs composed of dozens of functional blocks can be designed in a matter of minutes. GenoCAD automatically derives the construct sequence from its comprehensive libraries of genetic parts. Upon completion of the design process, users can download the sequence for synthesis or further anal. Users who elect to create a personal account on the system can customize their workspace by creating their own parts libraries, adding new parts to the libraries, or reusing designs to quickly generate sets of related constructs.
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    Pedersen, M. and Phillips, A. (2009) Towards programming languages for genetic engineering of living cells. J. R. Soc., Interface 6, S437– S450,  DOI: 10.1098/rsif.2008.0516.focus
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    Towards programming languages for genetic engineering of living cells
    Pedersen, Michael; Phillips, Andrew
    Journal of the Royal Society, Interface (2009), 6 (Suppl. 4), S437-S450CODEN: JRSICU; ISSN:1742-5689. (Royal Society)
    Synthetic biol. aims at producing novel biol. systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetd. proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of std. biol. parts, a process that relies on logic programming and prototype databases that contain known biol. parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging std. of biol. parts which so far has focused on biol., rather than logical, properties of parts.
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    Misirli, G., Hallinan, J. S., Yu, T., Lawson, J. R., Wimalaratne, S. M., Cooling, M. T., and Wipat, A. (2011) Model annotation for synthetic biology: automating model to nucleotide sequence conversion. Bioinformatics 27, 973– 979,  DOI: 10.1093/bioinformatics/btr048
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    Model annotation for synthetic biology: automating model to nucleotide sequence conversion
    Misirli, Goksel; Hallinan, Jennifer S.; Yu, Tommy; Lawson, James R.; Wimalaratne, Sarala M.; Cooling, Michael T.; Wipat, Anil
    Bioinformatics (2011), 27 (7), 973-979CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biol. projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behavior. The larger model then acts as a blueprint for phys. implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their phys. manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. Results: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biol. Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. Availability: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biol./downloads.html. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
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    Swainston, N., Dunstan, M., Jervis, A. J., Robinson, C. J., Carbonell, P., Williams, A. R., Faulon, J.-L., Scrutton, N. S., and Kell, D. B. (2018) PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics 34, 2327– 2329,  DOI: 10.1093/bioinformatics/bty105
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    PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts
    Swainston, Neil; Dunstan, Mark; Jervis, Adrian J.; Robinson, Christopher J.; Carbonell, Pablo; Williams, Alan R.; Faulon, Jean-Loup; Scrutton, Nigel S.; Kell, Douglas B.
    Bioinformatics (2018), 34 (13), 2327-2329CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
    Motivation: Synthetic biol. is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. PartsGenie is introduced to facilitate the computational design of such synthetic biol. parts, bridging the gap between optimization tools for the design of novel parts, the representation of such parts in community-developed data stds. such as Synthetic Biol. Open Language, and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a no. of DNA optimization algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimization and dissemination of reusable synthetic biol. parts through an integrated application.
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    Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., and Kummer, U. (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22, 3067,  DOI: 10.1093/bioinformatics/btl485
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    COPASI - A COmplex PAthway SImulator
    Hoops, Stefan; Sahle, Sven; Gauges, Ralph; Lee, Christine; Pahle, Juergen; Simus, Natalia; Singhal, Mudita; Xu, Liang; Mendes, Pedro; Kummer, Ursula
    Bioinformatics (2006), 22 (24), 3067-3074CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: Simulation and modeling is becoming a std. approach to understand complex biochem. processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Results: Here, we present COPASI, a platform-independent and user-friendly biochem. simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random no. generator numerical resoln. in stochastic simulation.
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    Moraru, I., Schaff, J., and Slepchenko, B. (2008) Virtual cell modelling and simulation software environment. IET Syst. Biol. 2, 352,  DOI: 10.1049/iet-syb:20080102
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    Virtual Cell modelling and simulation software environment
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    IET systems biology (2008), 2 (5), 352-62 ISSN:1751-8849.
    The Virtual Cell (VCell; http://vcell.org/) is a problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client-server system, with more than a thousand world-wide users. VCell provides a separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This separation clarifies the impact of modelling decisions, assumptions and approximations. The result is a physically consistent, mathematically rigorous, spatial modelling and simulation framework. Users create biological models and VCell will automatically (i) generate the appropriate mathematical encoding for running a simulation and (ii) generate and compile the appropriate computer code. Both deterministic and stochastic algorithms are supported for describing and running non-spatial simulations; a full partial differential equation solver using the finite volume numerical algorithm is available for reaction-diffusion-advection simulations in complex cell geometries including 3D geometries derived from microscope images. Using the VCell database, models and model components can be reused and updated, as well as privately shared among collaborating groups, or published. Exchange of models with other tools is possible via import/export of SBML, CellML and MatLab formats. Furthermore, curation of models is facilitated by external database binding mechanisms for unique identification of components and by standardised annotations compliant with the MIRIAM standard. VCell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform.
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    Galdzicki, M., Clancy, K. P., Oberortner, E., Pocock, M., Quinn, J. Y., Rodriguez, C. A., Nicholas, R., Wilson, M. L., Adam, L., Anderson, J. C. (2014) The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nat. Biotechnol. 32, 545– 550,  DOI: 10.1038/nbt.2891
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    The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology
    Galdzicki, Michal; Clancy, Kevin P.; Oberortner, Ernst; Pocock, Matthew; Quinn, Jacqueline Y.; Rodriguez, Cesar A.; Roehner, Nicholas; Wilson, Mandy L.; Adam, Laura; Anderson, J. Christopher; Bartley, Bryan A.; Beal, Jacob; Chandran, Deepak; Chen, Joanna; Densmore, Douglas; Endy, Drew; Grunberg, Raik; Hallinan, Jennifer; Hillson, Nathan J.; Johnson, Jeffrey D.; Kuchinsky, Allan; Lux, Matthew; Misirli, Goksel; Peccoud, Jean; Plahar, Hector A.; Sirin, Evren; Stan, Guy-Bart; Villalobos, Alan; Wipat, Anil; Gennari, John H.; Myers, Chris J.; Sauro, Herbert M.
    Nature Biotechnology (2014), 32 (6), 545-550CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
    The re-use of previously validated designs is crit. to the evolution of synthetic biol. from a research discipline to an engineering practice. Here we describe the Synthetic Biol. Open Language (SBOL), a proposed data std. for exchanging designs within the synthetic biol. community. SBOL represents synthetic biol. designs in a community-driven, formalized format for exchange between software tools, research groups and com. service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven std., SBOL will be updated as synthetic biol. evolves to provide specific capabilities for different aspects of the synthetic biol. workflow.
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    Hucka, M. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524,  DOI: 10.1093/bioinformatics/btg015
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    The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models
    Hucka, M.; Finney, A.; Sauro, H. M.; Bolouri, H.; Doyle, J. C.; Kitano, H.; Arkin, A. P.; Bornstein, B. J.; Bray, D.; Cornish-Bowden, A.; Cuellar, A. A.; Dronov, S.; Gilles, E. D.; Ginkel, M.; Gor, V.; Goryanin, I. I.; Hedley, W. J.; Hodgman, T. C.; Hofmeyr, J.-H.; Hunter, P. J.; Juty, N. S.; Kasberger, J. L.; Kremling, A.; Kummer, U.; Le Novere, N.; Loew, L. M.; Lucio, D.; Mendes, P.; Minch, E.; Mjolsness, E. D.; Nakayama, Y.; Nelson, M. R.; Nielsen, P. F.; Sakurada, T.; Schaff, J. C.; Shapiro, B. E.; Shimizu, T. S.; Spence, H. D.; Stelling, J.; Takahashi, K.; Tomita, M.; Wagner, J.; Wang, J.
    Bioinformatics (2003), 19 (4), 524-531CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    The Systems Biol. Markup Language (SBML) Level 1 is a free, open, XML-based format for representing biochem. reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biol., including cell signaling pathways, metabolic pathways, gene regulation, and others.
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    The Infobiotics Workbench: an integrated in silico modelling platform for Systems and Synthetic Biology
    Blakes, Jonathan; Twycross, Jamie; Romero-Campero, Francisco Jose; Krasnogor, Natalio
    Bioinformatics (2011), 27 (23), 3323-3324CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    The Infobiotics Workbench is an integrated software suite incorporating model specification, simulation, parameter optimization and model checking for Systems and Synthetic Biol. A modular model specification allows for straightforward creation of large-scale models contg. many compartments and reactions. Models are simulated either using stochastic simulation or numerical integration, and visualized in time and space. Model parameters and structure can be optimized with evolutionary algorithms, and model properties calcd. using probabilistic model checking.
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    Mısırlı, G., Taylor, R., Goñi-Moreno, A., McLaughlin, J. A., Myers, C., Gennari, J. H., Lord, P., and Wipat, A. (2019) SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information. ACS Synth. Biol. 8, 1498– 1514,  DOI: 10.1021/acssynbio.8b00532
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    SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information
    Misirli, Goksel; Taylor, Renee; Goni-Moreno, Angel; McLaughlin, James Alastair; Myers, Chris; Gennari, John H.; Lord, Phillip; Wipat, Anil
    ACS Synthetic Biology (2019), 8 (7), 1498-1514CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
    Std. representation of data is key for the reproducibility of designs in synthetic biol. The Synthetic Biol. Open Language (SBOL) has already emerged as a data std. to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontol. for a machine understandable definition of SBOL. This ontol. acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addn. to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biol. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontol. representation of the SBOL std. provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biol. ontologies.
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This article is cited by 5 publications.

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  • Abstract

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    Figure 1

    Figure 1. Hierarchical representation of biological entities. A RULE represents a chemical reaction; a PROCESS is a group of rules and provides a more abstract representation (and decomposition) of complex processes; a DEVICE refers to an assembly of genetic parts and biological building blocks; a CELL represents a bacterial cell.

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    Figure 2

    Figure 2. A screenshot of IBW in its biocompilation perspective. The integrated development environment features a project navigator (left), source code editor (top center), biocompilation controller (right) and compilation result window (bottom center). The toolbar and menu provides access to typical development features including refactoring and collaborative versioning tools such as git.

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    Figure 3

    Figure 3. Performance benchmark comparison of IBW’s CPU and GPU simulators using the quorum sensing model (see Quorum Sensing section). The GPU simulator provides a much faster and efficient alternative compared to the CPU simulator (implementing serial algorithms) as it uses parallel algorithms, run in high performance environments.

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    Figure 4

    Figure 4. Four sequential stochastic simulations of the toggle switch. In the first simulation, the cell is suspended in IPTG, leading to the activation of the switch and production of GFP (green trace) and CI (red trace). In the second one, CI and GFP production is maintained despite the absence of IPTG. In the third simulation, the cell is suspended in aTc, leading to the deactivation of the switch, production of LacI (blue trace) and decay of GFP. In the final one, the switch resides in its off state despite the absence of aTc. Traces show the mean and standard deviations of 50 simulation runs.

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    Figure 5

    Figure 5. Biocompiler result for the toggle switch specification. Sequence information of promoters, coding refions and terminators is drawn from several online repositories (here the iGem parts registry), and ribosome binding sites are automatically calculated using Salis’ ribosome binding site calculator.

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    Figure 6

    Figure 6. Simulation traces (mean and standard deviations of 50 runs using tau-leaping) for the LacI (blue), CI (red), and TetR (green) dimers over 27 h.

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    Figure 7

    Figure 7. Biocompiler result for the repressilator specification.

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    Figure 8

    Figure 8. Genetic NOT, AND, and NOR gate circuits, and the corresponding truth tables.

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    Figure 9

    Figure 9. Simulation results. (a) NOT gate. (b) AND gate. (c) NOR gate.

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  • References

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    8. 8
      Gleizer, S., Ben-Nissan, R., Bar-On, Y. M., Antonovsky, N., Noor, E., Zohar, Y., Jona, G., Krieger, E., Shamshoum, M., Bar-Even, A., and Milo, R. (2019) Conversion of Escherichia coli to Generate All Biomass Carbon from CO2. Cell 179, 1255– 1263,  DOI: 10.1016/j.cell.2019.11.009
      [Crossref], [PubMed], [CAS], Google Scholar
      8
      Conversion of Escherichia coli to generate all biomass carbon from CO2
      Gleizer, Shmuel; Ben-Nissan, Roee; Bar-On, Yinon M.; Antonovsky, Niv; Noor, Elad; Zohar, Yehudit; Jona, Ghil; Krieger, Eyal; Shamshoum, Melina; Bar-Even, Arren; Milo, Ron
      Cell (Cambridge, MA, United States) (2019), 179 (6), 1255-1263.e12CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
      The living world is largely divided into autotrophs that convert CO2 into biomass and heterotrophs that consume org. compds. In spite of widespread interest in renewable energy storage and more sustainable food prodn., the engineering of industrially relevant heterotrophic model organisms to use CO2 as their sole carbon source has so far remained an outstanding challenge. Here, we report the achievement of this transformation on lab. timescales. We constructed and evolved Escherichia coli to produce all its biomass carbon from CO2. Reducing power and energy, but not carbon, are supplied via the one-carbon mol. formate, which can be produced electrochem. Rubisco and phosphoribulokinase were co-expressed with formate dehydrogenase to enable CO2 fixation and redn. via the Calvin-Benson-Bassham cycle. Autotrophic growth was achieved following several months of continuous lab. evolution in a chemostat under intensifying org. carbon limitation and confirmed via isotopic labeling.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXit12ksbfM&md5=dc626ce70b48868ed207b6ea48067653
    9. 9
      Kylilis, N., Riangrungroj, P., Lai, H.-E., Salema, V., Fernández, L. A., Stan, G.-B. V., Freemont, P. S., and Polizzi, K. M. (2019) Whole-Cell Biosensor with Tunable Limit of Detection Enables Low-Cost Agglutination Assays for Medical Diagnostic Applications. ACS Sensors 4, 370– 378,  DOI: 10.1021/acssensors.8b01163
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      9
      Whole-Cell Biosensor with Tunable Limit of Detection Enables Low-Cost Agglutination Assays for Medical Diagnostic Applications
      Kylilis, Nicolas; Riangrungroj, Pinpunya; Lai, Hung-En; Salema, Valencio; Fernandez, Luis Angel; Stan, Guy-Bart V.; Freemont, Paul S.; Polizzi, Karen M.
      ACS Sensors (2019), 4 (2), 370-378CODEN: ASCEFJ; ISSN:2379-3694. (American Chemical Society)
      Whole-cell biosensors can form the basis of affordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing, but to date the detection of analytes such as proteins that cannot easily diffuse across the cell membrane has been challenging. Here we developed a novel biosensing platform based on cell agglutination using an E. coli whole-cell biosensor surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design to detect a model analyte at nanomolar concns. Moreover, we show that the design architecture is flexible by building assays optimized to detect a range of model analyte concns. using straightforward design rules and a math. model. Finally, we re-engineer our whole-cell biosensor for the detection of a medically relevant biomarker by the display of two different nanobodies against human fibrinogen and demonstrate a detection limit as low as 10 pM in dild. human plasma. Overall, we demonstrate that our agglutination technol. fulfills the requirement of POC testing by combining low-cost nanobody prodn., customizable detection range and low detection limits. This technol. has the potential to produce affordable diagnostics for field-testing in the developing world, emergency or disaster relief sites, as well as routine medical testing and personalized medicine.
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    10. 10
      Sheth, R. U. and Wang, H. H. (2018) DNA-based memory devices for recording cellular events. Nat. Rev. Genet. 19, 718– 732,  DOI: 10.1038/s41576-018-0052-8
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      10
      DNA-based memory devices for recording cellular events
      Sheth, Ravi U.; Wang, Harris H.
      Nature Reviews Genetics (2018), 19 (11), 718-732CODEN: NRGAAM; ISSN:1471-0056. (Nature Research)
      A review. Measuring biol. data across time and space is crit. for understanding complex biol. processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biol. recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biol. events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quant. characterization, modeling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.
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    11. 11
      Karr, J., Sanghvi, J., Macklin, D., Gutschow, M., Jacobs, J., Bolival, B., Assad-Garcia, N., Glass, J., and Covert, M. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150, 389– 401,  DOI: 10.1016/j.cell.2012.05.044
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      11
      A Whole-Cell Computational Model Predicts Phenotype from Genotype
      Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N.; Gutschow, Miriam V.; Jacobs, Jared M.; Bolival, Benjamin; Assad-Garcia, Nacyra; Glass, John I.; Covert, Markus W.
      Cell (Cambridge, MA, United States) (2012), 150 (2), 389-401CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
      Understanding how complex phenotypes arise from individual mols. and their interactions is a primary challenge in biol. that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its mol. components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and exptl. measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA assocn. and an inverse relationship between the durations of DNA replication initiation and replication. In addn., exptl. anal. directed by model predictions identified previously undetected kinetic parameters and biol. functions. We conclude that comprehensive whole-cell models can be used to facilitate biol. discovery.
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    12. 12
      Naylor, J., Fellermann, H., Ding, Y., Mohammed, W. K., Jakubovics, N. S., Mukherjee, J., Biggs, C. A., Wright, P. C., and Krasnogor, N. (2017) Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations. ACS Synth. Biol. 6, 1194– 1210,  DOI: 10.1021/acssynbio.6b00315
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      12
      Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations
      Naylor, Jonathan; Fellermann, Harold; Ding, Yuchun; Mohammed, Waleed K.; Jakubovics, Nicholas S.; Mukherjee, Joy; Biggs, Catherine A.; Wright, Phillip C.; Krasnogor, Natalio
      ACS Synthetic Biology (2017), 6 (7), 1194-1210CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      Simbiotics is a spatially explicit multiscale modeling platform for the design, simulation and anal. of bacterial populations. Systems ranging from planktonic cells and colonies, to biofilm formation and development may be modeled. Representation of biol. systems in Simbiotics is flexible, and user-defined processes may be in a variety of forms depending on desired model abstraction. Simbiotics provides a library of modules such as cell geometries, phys. force dynamics, genetic circuits, metabolic pathways, chem. diffusion and cell interactions. Model defined processes are integrated and scheduled for parallel multithread and multi-CPU execution. A virtual lab provides the modeler with anal. modules and some simulated lab equipment, enabling automation of sample interaction and data collection. An extendable and modular framework allows for the platform to be updated as novel models of bacteria are developed, coupled with an intuitive user interface to allow for model definitions with minimal programming experience. Simbiotics can integrate existing stds. such as SBML, and process microscopy images to initialize the 3D spatial configuration of bacteria consortia. Two case studies, used to illustrate the platform flexibility, focus on the phys. properties of the biosystems modeled. These pilot case studies demonstrate Simbiotics versatility in modeling and anal. of natural systems and as a CAD tool for synthetic biol.
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    13. 13
      Sanassy, D., Widera, P., and Krasnogor, N. (2015) Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology. ACS Synth. Biol. 4, 39– 47,  DOI: 10.1021/sb5001406
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      13
      Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology
      Sanassy, Daven; Widera, Pawel; Krasnogor, Natalio
      ACS Synthetic Biology (2015), 4 (1), 39-47CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochem. systems at low species concns. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to det. which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochem. model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochem. model with the predicted fastest algorithm as the simulation engine. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.
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    14. 14
      Gorochowski, T. E., Hauert, S., Kreft, J.-U., Marucci, L., Stillman, N. R., Tang, T.-Y. D., Bandiera, L., Bartoli, V., Dixon, D. O. R., Fedorec, A. J. H., Fellermann, H., Fletcher, A. G., Foster, T., Giuggioli, L., Matyjaszkiewicz, A., McCormick, S., Montes Olivas, S., Naylor, J., Rubio Denniss, A., and Ward, D. (2020) Toward Engineering Biosystems With Emergent Collective Functions. Front. Bioeng. Biotechnol. 8, 705,  DOI: 10.3389/fbioe.2020.00705
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      14
      Toward Engineering Biosystems With Emergent Collective Functions
      Gorochowski Thomas E; Ward Daniel; Hauert Sabine; Marucci Lucia; Stillman Namid R; Bartoli Vittorio; Giuggioli Luca; McCormick Scott; Montes Olivas Sandra; Rubio Denniss Ana; Kreft Jan-Ulrich; Foster Tim; Tang T-Y Dora; Tang T-Y Dora; Bandiera Lucia; Dixon Daniel O R; Fedorec Alex J H; Fellermann Harold; Naylor Jonathan; Fletcher Alexander G; Matyjaszkiewicz Antoni
      Frontiers in bioengineering and biotechnology (2020), 8 (), 705 ISSN:2296-4185.
      Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.
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    15. 15
      Jiang, S., Wang, Y., Kaiser, M., and Krasnogor, N. (2020) NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 36, 3482– 3492,  DOI: 10.1093/bioinformatics/btaa163
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      15
      NIHBA: a network interdiction approach for metabolic engineering design
      Jiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, Natalio
      Bioinformatics (2020), 36 (11), 3482-3492CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
      Motivation: Flux balance anal. (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochem. overprodn. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solns. in a single run. Results: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solns. that meet users' prodn. requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ~ 1 h, e.g. studied in this article).
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    16. 16
      Misirli, G., Nguyen, T., McLaughlin, J. A., Vaidyanathan, P., Jones, T. S., Densmore, D., Myers, C., and Wipat, A. (2019) A Computational Workflow for the Automated Generation of Models of Genetic Designs. ACS Synth. Biol. 8, 1548– 1559,  DOI: 10.1021/acssynbio.7b00459
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      16
      A Computational Workflow for the Automated Generation of Models of Genetic Designs
      Misirli, Goksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S.; Densmore, Douglas; Myers, Chris; Wipat, Anil
      ACS Synthetic Biology (2019), 8 (7), 1548-1559CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      Computational models are essential to engineer predictable biol. systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing stds. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biol. Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biol. Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biol. systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.
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    17. 17
      Tellechea-Luzardo, J., Winterhalter, C., Widera, P., Kozyra, J., de Lorenzo, V., and Krasnogor, N. (2020) Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering. ACS Synth. Biol. 9, 536– 545,  DOI: 10.1021/acssynbio.9b00400
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      17
      Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering
      Tellechea-Luzardo, Jonathan; Winterhalter, Charles; Widera, Pawel; Kozyra, Jerzy; de Lorenzo, Victor; Krasnogor, Natalio
      ACS Synthetic Biology (2020), 9 (3), 536-545CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      As DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biol. engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnol. and digital technol., a deficit in software and lab. techniques diminishes the ability to make biotechnol. more agile, reproducible and transparent while, at the same time, limiting the security and safety of synthetic biol. constructs. To partially address some of these problems, this paper presents an approach for phys. linking engineered cells to their digital footprint-we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialized version control system for collaborative strain engineering via simple barcoding protocols.
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    18. 18
      Watanabe, L., Nguyen, T., Zhang, M., Zundel, Z., Zhang, Z., Madsen, C., Roehner, N., and Myers, C. (2019) iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synth. Biol. 8, 1560,  DOI: 10.1021/acssynbio.8b00078
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      18
      iBioSim 3: A Tool for Model-Based Genetic Circuit Design
      Watanabe, Leandro; Nguyen, Tramy; Zhang, Michael; Zundel, Zach; Zhang, Zhen; Madsen, Curtis; Roehner, Nicholas; Myers, Chris
      ACS Synthetic Biology (2019), 8 (7), 1560-1563CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      The iBioSim tool has been developed to facilitate the design of genetic circuits via a model-based design strategy. This paper illustrates the new features incorporated into the tool for DNA circuit design, design anal., and design synthesis, all of which can be used in a workflow for the systematic construction of new genetic circuits.
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    19. 19
      Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D., and Voigt, C. A. (2016) Genetic circuit design automation. Science 352, aac7341,  DOI: 10.1126/science.aac7341
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    20. 20
      Gutiérrez, M., Gregorio-Godoy, P., Pérez del Pulgar, G., Muñoz, L. E., Sáez, S., and Rodríguez-Patón, A. (2017) A New Improved and Extended Version of the Multicell Bacterial Simulator GRO. ACS Synth. Biol. 6, 1496– 1508,  DOI: 10.1021/acssynbio.7b00003
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      20
      A New Improved and Extended Version of the Multicell Bacterial Simulator gro
      Gutierrez, Martin; Gregorio-Godoy, Paula; Perez del Pulgar, Guillermo; Munoz, Luis E.; Saez, Sandra; Rodriguez-Paton, Alfonso
      ACS Synthetic Biology (2017), 6 (8), 1496-1508CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      Gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biol. prototyping tool for simulating multicellular biocircuits and microbial consortia. The authors present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is 1-2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in <10 min. A new library, CellEngine, accelerates the resoln. of spatial phys. interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. The authors also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, the authors provide studies and novel examples ranging from synthetic biol. to evolutionary microbiol. The authors believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biol. designs.
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    21. 21
      Chandran, D. and Sauro, H. M. (2012) Hierarchical modeling for synthetic biology. ACS Synth. Biol. 1, 353– 364,  DOI: 10.1021/sb300033q
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      21
      Hierarchical Modeling for Synthetic Biology
      Chandran, Deepak; Sauro, Herbert M.
      ACS Synthetic Biology (2012), 1 (8), 353-364CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      One of the characteristics of synthetic biol. is that it often combines math. modeling with exptl. work. The link between modeling and expts. is carried out by human researchers who have a conceptual understanding of the underlying biol. system. At present, there is no method for representing a conceptual description that can be used to connect math. models and exptl. data, esp. sequence annotations, pertaining to the same underlying biol. system. One reason for this limitation is that there can exist different math. models of the same biol. system. In such cases, the same annotation in a DNA sequence would map differently to different models of the same system. In order to enable software support for synthetic biol., a software framework is needed such that it is able to capture a conceptual description of a biol. system, including quant. values, without confining itself to one math. model. The novel use of hierarchical modeling inside TinkerCell provides one potential software soln. for representing a "conceptual diagram" of a biol. system. The conceptual diagram does not assume any underlying model. Rather, the diagram is mapped automatically to one of several models. The diagram can then contain information relevant for both modeling and exptl. work. Computer-aided design (CAD) can be very useful to synthetic biol. CAD allows engineers to spend more effort at the design stage and less at the construction stage by automatically performing many tasks that are currently performed by human researchers. The ability to automatically link models and exptl. results will be one step in the development of practical CAD systems for synthetic biol.
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    22. 22
      Bilitchenko, L., Liu, A., Cheung, S., Weeding, E., Xia, B., Leguia, M., Anderson, J. C., and Densmore, D. (2011) EUGENE - A domain specific language for specifying and constraining synthetic biological parts, devices, and systems. PLoS One 6, e18882,  DOI: 10.1371/journal.pone.0018882
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      22
      Eugene - a domain specific language for specifying and constraining synthetic biological parts, devices, and systems
      Bilitchenko, Lesia; Liu, Adam; Cheung, Sherine; Weeding, Emma; Xia, Bing; Leguia, Mariana; Anderson, J. Christopher; Densmore, Douglas
      PLoS One (2011), 6 (4), e18882CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Background: Synthetic biol. systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their compn. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biol. designs based on biol. parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. Results: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or phys. assembly. Eugene creates functional designs quickly and cost-effectively. Conclusions: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired ion hierarchy in synthetic biol. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and phys. device assembly.
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    23. 23
      Beal, J., Lu, T., and Weiss, R. (2011) Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks. PLoS One 6, e22490,  DOI: 10.1371/journal.pone.0022490
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      23
      Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks
      Beal, Jacob; Lu, Ting; Weiss, Ron
      PLoS One (2011), 6 (8), e22490CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Background: The field of synthetic biol. promises to revolutionize our ability to engineer biol. systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biol. itself and the lag in our ability to design and optimize sophisticated biol. circuitry. Methodol./Principal Findings: To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biol.-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biol. system design with our platform, and show that our compiler optimizations can yield significant redns. in the no. of genes (∼50) and latency of the optimized engineered gene networks. Conclusions/Significance: Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biol. systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biol. relevant compiler optimizations, providing an important foundation for the development of sophisticated biol. systems.
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    24. 24
      Villalobos, A., Ness, J. E., Gustafsson, C., Minshull, J., and Govindarajan, S. (2006) Gene Designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinf. 7, 285,  DOI: 10.1186/1471-2105-7-285
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      Gene Designer: a synthetic biology tool for constructing artificial DNA segments
      Villalobos Alan; Ness Jon E; Gustafsson Claes; Minshull Jeremy; Govindarajan Sridhar
      BMC bioinformatics (2006), 7 (), 285 ISSN:.
      BACKGROUND: Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes. RESULTS: Gene Designer is a stand-alone software for fast and easy design of synthetic DNA segments. Users can easily add, edit and combine genetic elements such as promoters, open reading frames and tags through an intuitive drag-and-drop graphic interface and a hierarchical DNA/Protein object map. Using advanced optimization algorithms, open reading frames within the DNA construct can readily be codon optimized for protein expression in any host organism. Gene Designer also includes features such as a real-time sliding calculator of oligonucleotide annealing temperatures, sequencing primer generator, tools for avoidance or inclusion of restriction sites, and options to maximize or minimize sequence identity to a reference. CONCLUSION: Gene Designer is an expandable Synthetic Biology workbench suitable for molecular biologists interested in the de novo creation of genetic constructs.
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      Gaspar, P., Oliveira, J. L., Frommlet, J., Santos, M., and Moura, G. (2012) EuGene: maximizing synthetic gene design for heterologous expression. Bioinformatics 28, 2683,  DOI: 10.1093/bioinformatics/bts465
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      EuGene: maximizing synthetic gene design for heterologous expression
      Gaspar, Paulo; Oliveira, Jose Luis; Frommlet, Joerg; Santos, Manuel A. S.; Moura, Gabriela
      Bioinformatics (2012), 28 (20), 2683-2684CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Numerous software applications exist to deal with synthetic gene design, granting the field of heterologous expression a significant support. However, their dispersion requires the access to different tools and online services in order to complete one single project. Analyzing codon usage, calcg. codon adaptation index (CAI), aligning orthologs and optimizing genes are just a few examples. A software application, EuGene, was developed for the optimization of multiple gene synthetic design algorithms. In a seamless automatic form, EuGene calcs. or retrieves genome data on codon usage (relative synonymous codon usage and CAI), codon context (CPS and codon pair bias), GC content, hidden stop codons, repetitions, deleterious sites, protein primary, secondary and tertiary structures, gene orthologs, species housekeeping genes, performs alignments and identifies genes and genomes. The main function of EuGene is analyzing and redesigning gene sequences using multi-objective optimization techniques that maximize the coding features of the resulting sequence.
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      Czar, M. J., Cai, Y., and Peccoud, J. (2009) Writing DNA with GenoCAD. Nucleic Acids Res. 37, W40– W47,  DOI: 10.1093/nar/gkp361
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      Writing DNA with GenoCAD
      Czar, Michael J.; Cai, Yizhi; Peccoud, Jean
      Nucleic Acids Research (2009), 37 (Web Server), W40-W47CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      Chem. synthesis of custom DNA made to order calls for software streamlining the design of synthetic DNA sequences. GenoCAD (www.genocad.org) is a free web-based application to design protein expression vectors, artificial gene networks and other genetic constructs composed of multiple functional blocks called genetic parts. By capturing design strategies in grammatical models of DNA sequences, GenoCAD guides the user through the design process. By successively clicking on icons representing structural features or actual genetic parts, complex constructs composed of dozens of functional blocks can be designed in a matter of minutes. GenoCAD automatically derives the construct sequence from its comprehensive libraries of genetic parts. Upon completion of the design process, users can download the sequence for synthesis or further anal. Users who elect to create a personal account on the system can customize their workspace by creating their own parts libraries, adding new parts to the libraries, or reusing designs to quickly generate sets of related constructs.
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      Pedersen, M. and Phillips, A. (2009) Towards programming languages for genetic engineering of living cells. J. R. Soc., Interface 6, S437– S450,  DOI: 10.1098/rsif.2008.0516.focus
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      Towards programming languages for genetic engineering of living cells
      Pedersen, Michael; Phillips, Andrew
      Journal of the Royal Society, Interface (2009), 6 (Suppl. 4), S437-S450CODEN: JRSICU; ISSN:1742-5689. (Royal Society)
      Synthetic biol. aims at producing novel biol. systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetd. proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of std. biol. parts, a process that relies on logic programming and prototype databases that contain known biol. parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging std. of biol. parts which so far has focused on biol., rather than logical, properties of parts.
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      Misirli, G., Hallinan, J. S., Yu, T., Lawson, J. R., Wimalaratne, S. M., Cooling, M. T., and Wipat, A. (2011) Model annotation for synthetic biology: automating model to nucleotide sequence conversion. Bioinformatics 27, 973– 979,  DOI: 10.1093/bioinformatics/btr048
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      Model annotation for synthetic biology: automating model to nucleotide sequence conversion
      Misirli, Goksel; Hallinan, Jennifer S.; Yu, Tommy; Lawson, James R.; Wimalaratne, Sarala M.; Cooling, Michael T.; Wipat, Anil
      Bioinformatics (2011), 27 (7), 973-979CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biol. projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behavior. The larger model then acts as a blueprint for phys. implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their phys. manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. Results: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biol. Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. Availability: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biol./downloads.html. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
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      Swainston, N., Dunstan, M., Jervis, A. J., Robinson, C. J., Carbonell, P., Williams, A. R., Faulon, J.-L., Scrutton, N. S., and Kell, D. B. (2018) PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics 34, 2327– 2329,  DOI: 10.1093/bioinformatics/bty105
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      PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts
      Swainston, Neil; Dunstan, Mark; Jervis, Adrian J.; Robinson, Christopher J.; Carbonell, Pablo; Williams, Alan R.; Faulon, Jean-Loup; Scrutton, Nigel S.; Kell, Douglas B.
      Bioinformatics (2018), 34 (13), 2327-2329CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
      Motivation: Synthetic biol. is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. PartsGenie is introduced to facilitate the computational design of such synthetic biol. parts, bridging the gap between optimization tools for the design of novel parts, the representation of such parts in community-developed data stds. such as Synthetic Biol. Open Language, and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a no. of DNA optimization algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimization and dissemination of reusable synthetic biol. parts through an integrated application.
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      Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., and Kummer, U. (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22, 3067,  DOI: 10.1093/bioinformatics/btl485
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      COPASI - A COmplex PAthway SImulator
      Hoops, Stefan; Sahle, Sven; Gauges, Ralph; Lee, Christine; Pahle, Juergen; Simus, Natalia; Singhal, Mudita; Xu, Liang; Mendes, Pedro; Kummer, Ursula
      Bioinformatics (2006), 22 (24), 3067-3074CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: Simulation and modeling is becoming a std. approach to understand complex biochem. processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Results: Here, we present COPASI, a platform-independent and user-friendly biochem. simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random no. generator numerical resoln. in stochastic simulation.
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      Moraru, I., Schaff, J., and Slepchenko, B. (2008) Virtual cell modelling and simulation software environment. IET Syst. Biol. 2, 352,  DOI: 10.1049/iet-syb:20080102
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      Virtual Cell modelling and simulation software environment
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      IET systems biology (2008), 2 (5), 352-62 ISSN:1751-8849.
      The Virtual Cell (VCell; http://vcell.org/) is a problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client-server system, with more than a thousand world-wide users. VCell provides a separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This separation clarifies the impact of modelling decisions, assumptions and approximations. The result is a physically consistent, mathematically rigorous, spatial modelling and simulation framework. Users create biological models and VCell will automatically (i) generate the appropriate mathematical encoding for running a simulation and (ii) generate and compile the appropriate computer code. Both deterministic and stochastic algorithms are supported for describing and running non-spatial simulations; a full partial differential equation solver using the finite volume numerical algorithm is available for reaction-diffusion-advection simulations in complex cell geometries including 3D geometries derived from microscope images. Using the VCell database, models and model components can be reused and updated, as well as privately shared among collaborating groups, or published. Exchange of models with other tools is possible via import/export of SBML, CellML and MatLab formats. Furthermore, curation of models is facilitated by external database binding mechanisms for unique identification of components and by standardised annotations compliant with the MIRIAM standard. VCell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform.
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      Galdzicki, M., Clancy, K. P., Oberortner, E., Pocock, M., Quinn, J. Y., Rodriguez, C. A., Nicholas, R., Wilson, M. L., Adam, L., Anderson, J. C. (2014) The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nat. Biotechnol. 32, 545– 550,  DOI: 10.1038/nbt.2891
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      The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology
      Galdzicki, Michal; Clancy, Kevin P.; Oberortner, Ernst; Pocock, Matthew; Quinn, Jacqueline Y.; Rodriguez, Cesar A.; Roehner, Nicholas; Wilson, Mandy L.; Adam, Laura; Anderson, J. Christopher; Bartley, Bryan A.; Beal, Jacob; Chandran, Deepak; Chen, Joanna; Densmore, Douglas; Endy, Drew; Grunberg, Raik; Hallinan, Jennifer; Hillson, Nathan J.; Johnson, Jeffrey D.; Kuchinsky, Allan; Lux, Matthew; Misirli, Goksel; Peccoud, Jean; Plahar, Hector A.; Sirin, Evren; Stan, Guy-Bart; Villalobos, Alan; Wipat, Anil; Gennari, John H.; Myers, Chris J.; Sauro, Herbert M.
      Nature Biotechnology (2014), 32 (6), 545-550CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
      The re-use of previously validated designs is crit. to the evolution of synthetic biol. from a research discipline to an engineering practice. Here we describe the Synthetic Biol. Open Language (SBOL), a proposed data std. for exchanging designs within the synthetic biol. community. SBOL represents synthetic biol. designs in a community-driven, formalized format for exchange between software tools, research groups and com. service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven std., SBOL will be updated as synthetic biol. evolves to provide specific capabilities for different aspects of the synthetic biol. workflow.
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      Hucka, M. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524,  DOI: 10.1093/bioinformatics/btg015
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      The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models
      Hucka, M.; Finney, A.; Sauro, H. M.; Bolouri, H.; Doyle, J. C.; Kitano, H.; Arkin, A. P.; Bornstein, B. J.; Bray, D.; Cornish-Bowden, A.; Cuellar, A. A.; Dronov, S.; Gilles, E. D.; Ginkel, M.; Gor, V.; Goryanin, I. I.; Hedley, W. J.; Hodgman, T. C.; Hofmeyr, J.-H.; Hunter, P. J.; Juty, N. S.; Kasberger, J. L.; Kremling, A.; Kummer, U.; Le Novere, N.; Loew, L. M.; Lucio, D.; Mendes, P.; Minch, E.; Mjolsness, E. D.; Nakayama, Y.; Nelson, M. R.; Nielsen, P. F.; Sakurada, T.; Schaff, J. C.; Shapiro, B. E.; Shimizu, T. S.; Spence, H. D.; Stelling, J.; Takahashi, K.; Tomita, M.; Wagner, J.; Wang, J.
      Bioinformatics (2003), 19 (4), 524-531CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      The Systems Biol. Markup Language (SBML) Level 1 is a free, open, XML-based format for representing biochem. reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biol., including cell signaling pathways, metabolic pathways, gene regulation, and others.
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      The Infobiotics Workbench: an integrated in silico modelling platform for Systems and Synthetic Biology
      Blakes, Jonathan; Twycross, Jamie; Romero-Campero, Francisco Jose; Krasnogor, Natalio
      Bioinformatics (2011), 27 (23), 3323-3324CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      The Infobiotics Workbench is an integrated software suite incorporating model specification, simulation, parameter optimization and model checking for Systems and Synthetic Biol. A modular model specification allows for straightforward creation of large-scale models contg. many compartments and reactions. Models are simulated either using stochastic simulation or numerical integration, and visualized in time and space. Model parameters and structure can be optimized with evolutionary algorithms, and model properties calcd. using probabilistic model checking.
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      Mısırlı, G., Taylor, R., Goñi-Moreno, A., McLaughlin, J. A., Myers, C., Gennari, J. H., Lord, P., and Wipat, A. (2019) SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information. ACS Synth. Biol. 8, 1498– 1514,  DOI: 10.1021/acssynbio.8b00532
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      SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information
      Misirli, Goksel; Taylor, Renee; Goni-Moreno, Angel; McLaughlin, James Alastair; Myers, Chris; Gennari, John H.; Lord, Phillip; Wipat, Anil
      ACS Synthetic Biology (2019), 8 (7), 1498-1514CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      Std. representation of data is key for the reproducibility of designs in synthetic biol. The Synthetic Biol. Open Language (SBOL) has already emerged as a data std. to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontol. for a machine understandable definition of SBOL. This ontol. acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addn. to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biol. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontol. representation of the SBOL std. provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biol. ontologies.
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      Constructing genetic logic circuits is an application of synthetic biol. in which parts of the DNA of a living cell are engineered to perform a dedicated Boolean function triggered by an appropriate concn. of certain proteins or by different genetic components. These logic circuits work in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degrdn. rates and concns. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits, a capability that we believe will be important for design automation in synthetic biol.
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    • Table S1: a comparison of the features of the most well-known computer aided synthetic biology design tools (PDF)


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