Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation
- Savas Konur*Savas Konur*Email: [email protected]Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.More by Savas Konur
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- Laurentiu MierlaLaurentiu MierlaDepartment of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.More by Laurentiu Mierla
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- Harold FellermannHarold FellermannInterdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.More by Harold Fellermann
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- Christophe LadroueChristophe LadroueDepartment of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.More by Christophe Ladroue
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- Bradley BrownBradley BrownInterdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.More by Bradley Brown
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- Anil WipatAnil WipatInterdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.More by Anil Wipat
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- Jamie TwycrossJamie TwycrossSchool of Computer Science, University of Nottingham, Nottingham, NG8 1BB, U.K.More by Jamie Twycross
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- Boyang Peter DunBoyang Peter DunDepartment of Computer Science, Stanford University, Stanford, California 94305, United StatesMore by Boyang Peter Dun
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- Sara KalvalaSara KalvalaDepartment of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.More by Sara Kalvala
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- Marian GheorgheMarian GheorgheDepartment of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K.More by Marian Gheorghe
- , and
- Natalio Krasnogor*Natalio Krasnogor*Email: [email protected]Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K.More by Natalio Krasnogor
Abstract

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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Attribution (BY): Credit must be given to the creator.
Non-Commercial (NC): Only non-commercial uses of the work are permitted.
No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited.
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License Summary*
You are free to share (copy and redistribute) this article in any medium or format within the parameters below:
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Attribution (BY): Credit must be given to the creator.
Non-Commercial (NC): Only non-commercial uses of the work are permitted.
No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited.
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You are free to share (copy and redistribute) this article in any medium or format within the parameters below:
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Attribution (BY): Credit must be given to the creator.
Non-Commercial (NC): Only non-commercial uses of the work are permitted.
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Results and Discussions
The Infobiotics Language
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.
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.





The Infobiotics Workbench
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.
Simulation
CPU-Based Simulation
GPU-Accelerated Simulation
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.
Verification

Biocompilation
Compatibility with Other Standards
Methods
Implementation
Case Studies
The Toggle Switch
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.
# | verification statement | result |
---|---|---|
1 | VERIFY [IPTG ≥ 10 μM] IS FOLLOWED BY [GFP > 7.5 μM] | T |
2 | VERIFY [IPTG ≥ 10 μM] IS FOLLOWED BY [GFP > 7.5 μM] WITH PROBABILITY ? | 0.82 |
3 | VERIFY [IPTG = 0 μM] IS FOLLOWED BY [GFP/CI > 3] WITH PROBABILITY ? | 0.99 |
4 | VERIFY [GFP] EVENTUALLY DECREASES WITH PROBABILITY ? GIVEN [aTc = 100 μM] | 1.0 |
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.
The Repressilator
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.
# | verification statement | result |
---|---|---|
1 | VERIFY [LacI ≤ 1.25 μM] HOLDS INFINITELY OFTEN WITH PROBABILITY ? | 0.48 |
2 | VERIFY [LacI > 1.25 μM] HOLDS INFINITELY OFTEN WITH PROBABILITY ? | 0.52 |
3 | VERIFY [CI ≥ 0.5 μM] AND [CI < 2 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ? | 0.92 |
Figure 7

Figure 7. Biocompiler result for the repressilator specification.
Quorum Sensing
# | verification statement | result |
---|---|---|
1 | VERIFY [LasR] EVENTUALLY INCREASES WITH PROBABILITY ? | 1.0 |
2 | VERIFY [HSL] EVENTUALLY INCREASES WITH PROBABILITY ? | 1.0 |
3 | VERIFY [LasR ≥ 0.1 μM] EVENTUALLY HOLDS WITHIN [0,1] s WITH PROBABILITY ? | 1.0 |
4 | VERIFY [HSL≥ 3 μM] EVENTUALLY HOLDS WITHIN [0,1] s WITH PROBABILITY ? | 1.0 |
5 | VERIFY [[LasR ≥ 0.005 μM] AND [LasR ≤ 0.15 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ? | 0.98 |
6 | VERIFY [[HSL ≥ 0.05 μM] AND [HSL ≤ 4.2 μM]] HOLDS IN STEADY-STATE WITH PROBABILITY ? | 0.72 |
Genetic Logic Gates
Figure 8

Figure 8. Genetic NOT, AND, and NOR gate circuits, and the corresponding truth tables.
Figure 9

Figure 9. Simulation results. (a) NOT gate. (b) AND gate. (c) NOR gate.
circuit | # | verification statement |
---|---|---|
NOT | 1a | VERIFY [TetR > Thr molecules] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ? |
1b | VERIFY [TetR < Thr molecules] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ? | |
AND | 2a | VERIFY [[TetR > Thr molecules] AND [LacI > Thr molecules]] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ? |
2b | VERIFY [[TetR < Thr molecules] OR [LacI < Thr molecules]] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ? | |
NOR | 3a | VERIFY [[TetR > Thr molecules] AND [LacI > Thr molecules]] IS FOLLOWED BY [GFP < Thr molecules] WITH PROBABILITY ? |
3b | VERIFY [[TetR < Thr molecules] OR [LacI < Thr molecules]] IS FOLLOWED BY [GFP > Thr molecules] WITH PROBABILITY ? |
Thr represents a threshold value.
NOT | |||
---|---|---|---|
Thr | Prob1a | Prob1b | score |
5 | 0.69 | 0.95 | 0.66 |
10 | 0.99 | 0.85 | 0.84 |
15 | 1.0 | 0.77 | 0.77 |
20 | 1.0 | 0.65 | 0.65 |
25 | 1.0 | 0.55 | 0.55 |
30 | 1.0 | 0.50 | 0.50 |
AND | |||
---|---|---|---|
Thr | Prob2a | Prob2b | score |
5 | 1.0 | 0.18 | 0.18 |
10 | 1.0 | 0.47 | 0.47 |
15 | 0.98 | 0.76 | 0.74 |
20 | 0.98 | 0.90 | 0.88 |
25 | 0.96 | 0.96 | 0.92 |
30 | 0.95 | 0.97 | 0.92 |
NOR | |||
---|---|---|---|
Thr | Prob3a | Prob3b | score |
5 | 0.96 | 0.82 | 0.79 |
10 | 0.99 | 0.70 | 0.69 |
15 | 1.0 | 0.59 | 0.59 |
20 | 1.0 | 0.39 | 0.39 |
25 | 1.0 | 0.19 | 0.19 |
30 | 1.0 | 0.13 | 0.13 |
Score is calculated by multiplying the probabilities obtained for two properties (e.g., 1a and 1b).
Performance Evaluation
case study | simulation (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] |
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.
Conclusion
Supporting Information
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)
Terms & Conditions
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.
Acknowledgments
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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- 11Karr, 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[Crossref], [PubMed], [CAS], Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVymsr7L&md5=13b9548cd09746c66afde9a5039358bbA Whole-Cell Computational Model Predicts Phenotype from GenotypeKarr, 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.
- 12Naylor, 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[ACS Full Text
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12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXntFejt70%253D&md5=ae9897e136461513e227469c506043faSimbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial PopulationsNaylor, Jonathan; Fellermann, Harold; Ding, Yuchun; Mohammed, Waleed K.; Jakubovics, Nicholas S.; Mukherjee, Joy; Biggs, Catherine A.; Wright, Phillip C.; Krasnogor, NatalioACS 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. - 13Sanassy, 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[ACS Full Text
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13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVSnsLbJ&md5=9834312adfad9868b6dbba4fc79b00d8Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic BiologySanassy, Daven; Widera, Pawel; Krasnogor, NatalioACS 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. - 14Gorochowski, 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[Crossref], [PubMed], [CAS], Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38jjtVylug%253D%253D&md5=c5eff7371098b5af2775859d8f2a97d2Toward Engineering Biosystems With Emergent Collective FunctionsGorochowski 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 AntoniFrontiers 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.
- 15Jiang, 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[Crossref], [PubMed], [CAS], Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisFCktLfI&md5=9dc5851f6b881ad4462871a6d14b2789NIHBA: a network interdiction approach for metabolic engineering designJiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, NatalioBioinformatics (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).
- 16Misirli, 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
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16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXpsl2nsbc%253D&md5=2ad6f7fe4326f8e9f99bfdd81b8db010A Computational Workflow for the Automated Generation of Models of Genetic DesignsMisirli, Goksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S.; Densmore, Douglas; Myers, Chris; Wipat, AnilACS 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. - 17Tellechea-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[ACS Full Text
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17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXjsVOntr0%253D&md5=282b32256dcc6a167abd1fb5b8484f29Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain EngineeringTellechea-Luzardo, Jonathan; Winterhalter, Charles; Widera, Pawel; Kozyra, Jerzy; de Lorenzo, Victor; Krasnogor, NatalioACS 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. - 18Watanabe, 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[ACS Full Text
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18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtF2ru7vJ&md5=488c35440c6d0805bda237f384b8473ciBioSim 3: A Tool for Model-Based Genetic Circuit DesignWatanabe, Leandro; Nguyen, Tramy; Zhang, Michael; Zundel, Zach; Zhang, Zhen; Madsen, Curtis; Roehner, Nicholas; Myers, ChrisACS 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. - 19Nielsen, 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
- 20Gutiérrez, 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
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20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmsVahs7k%253D&md5=f78eebffdd4b71ba31ef43e866d2d65fA New Improved and Extended Version of the Multicell Bacterial Simulator groGutierrez, Martin; Gregorio-Godoy, Paula; Perez del Pulgar, Guillermo; Munoz, Luis E.; Saez, Sandra; Rodriguez-Paton, AlfonsoACS 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. - 21Chandran, D. and Sauro, H. M. (2012) Hierarchical modeling for synthetic biology. ACS Synth. Biol. 1, 353– 364, DOI: 10.1021/sb300033q[ACS Full Text
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21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVOjsLzM&md5=c15cf99d9b31177f83d5f27d8bf1b752Hierarchical Modeling for Synthetic BiologyChandran, 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. - 22Bilitchenko, 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[Crossref], [PubMed], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXlsl2gt70%253D&md5=2bbd834f5054908816c4269a63ac9f06Eugene - a domain specific language for specifying and constraining synthetic biological parts, devices, and systemsBilitchenko, Lesia; Liu, Adam; Cheung, Sherine; Weeding, Emma; Xia, Bing; Leguia, Mariana; Anderson, J. Christopher; Densmore, DouglasPLoS 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.
- 23Beal, 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[Crossref], [PubMed], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtFajt7vK&md5=0a966eed7599c00db7575db44b84beb5Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networksBeal, Jacob; Lu, Ting; Weiss, RonPLoS 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.
- 24Villalobos, 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[Crossref], [PubMed], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD28vkvVyhug%253D%253D&md5=5e6e9a72714e8f69be3d8d351812a351Gene Designer: a synthetic biology tool for constructing artificial DNA segmentsVillalobos Alan; Ness Jon E; Gustafsson Claes; Minshull Jeremy; Govindarajan SridharBMC 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.
- 25Gaspar, 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[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFWktrrI&md5=e27f1e042cf756fe9121634b3e6b0b4cEuGene: maximizing synthetic gene design for heterologous expressionGaspar, Paulo; Oliveira, Jose Luis; Frommlet, Joerg; Santos, Manuel A. S.; Moura, GabrielaBioinformatics (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.
- 26Czar, M. J., Cai, Y., and Peccoud, J. (2009) Writing DNA with GenoCAD. Nucleic Acids Res. 37, W40– W47, DOI: 10.1093/nar/gkp361[Crossref], [PubMed], [CAS], Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXosFSkur0%253D&md5=26aa3c147e123d9e5e7bd97b0163b52aWriting DNA with GenoCADCzar, Michael J.; Cai, Yizhi; Peccoud, JeanNucleic 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.
- 27Pedersen, 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[Crossref], [PubMed], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXpslOqtLc%253D&md5=75e9d9e6a098037a38389a9ba31e36f0Towards programming languages for genetic engineering of living cellsPedersen, Michael; Phillips, AndrewJournal 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.
- 28Misirli, 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[Crossref], [PubMed], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXkt1yru7g%253D&md5=064e5ca461d2c7e4c87fe2bf09cf4ac3Model annotation for synthetic biology: automating model to nucleotide sequence conversionMisirli, Goksel; Hallinan, Jennifer S.; Yu, Tommy; Lawson, James R.; Wimalaratne, Sarala M.; Cooling, Michael T.; Wipat, AnilBioinformatics (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.
- 29Swainston, 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[Crossref], [PubMed], [CAS], Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXovFSjs7Y%253D&md5=cb41cf070468e4b02cd00793d402baf2PartsGenie: an integrated tool for optimizing and sharing synthetic biology partsSwainston, 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.
- 30Hoops, 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[Crossref], [PubMed], [CAS], Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xht1OgsrvK&md5=ff340a6c0c48f525a92a50c983aa1dddCOPASI - A COmplex PAthway SImulatorHoops, Stefan; Sahle, Sven; Gauges, Ralph; Lee, Christine; Pahle, Juergen; Simus, Natalia; Singhal, Mudita; Xu, Liang; Mendes, Pedro; Kummer, UrsulaBioinformatics (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.
- 31Moraru, 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[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjnvVensQ%253D%253D&md5=06143c5507ddf62477e3a572001f71a6Virtual Cell modelling and simulation software environmentMoraru I I; Schaff J C; Slepchenko B M; Blinov M L; Morgan F; Lakshminarayana A; Gao F; Li Y; Loew L MIET 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.
- 32Galdzicki, 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[Crossref], [PubMed], [CAS], Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXpsVGqurk%253D&md5=28f1ad342a0ce01bf297a70dbb73fe68The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biologyGaldzicki, 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.
- 33Hucka, 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[Crossref], [PubMed], [CAS], Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXit1ygu78%253D&md5=841c34faa3edec2131880858d8ce322fThe systems biology markup language (SBML): a medium for representation and exchange of biochemical network modelsHucka, 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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- 36Mısırlı, 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[ACS Full Text
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Cited By
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Abstract
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.
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.
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.
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.
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.
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.
Figure 7
Figure 7. Biocompiler result for the repressilator specification.
Figure 8
Figure 8. Genetic NOT, AND, and NOR gate circuits, and the corresponding truth tables.
Figure 9
Figure 9. Simulation results. (a) NOT gate. (b) AND gate. (c) NOR gate.
References
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9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXltVWjsg%253D%253D&md5=a15096139d13a50d7b8f04bcc9fd5bf9Whole-Cell Biosensor with Tunable Limit of Detection Enables Low-Cost Agglutination Assays for Medical Diagnostic ApplicationsKylilis, 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. - 10Sheth, 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[Crossref], [PubMed], [CAS], Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhslKjt7nM&md5=1243b531fc97cbd19f21c2df77d4ae8dDNA-based memory devices for recording cellular eventsSheth, 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.
- 11Karr, 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[Crossref], [PubMed], [CAS], Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVymsr7L&md5=13b9548cd09746c66afde9a5039358bbA Whole-Cell Computational Model Predicts Phenotype from GenotypeKarr, 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.
- 12Naylor, 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[ACS Full Text
], [CAS], Google Scholar
12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXntFejt70%253D&md5=ae9897e136461513e227469c506043faSimbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial PopulationsNaylor, Jonathan; Fellermann, Harold; Ding, Yuchun; Mohammed, Waleed K.; Jakubovics, Nicholas S.; Mukherjee, Joy; Biggs, Catherine A.; Wright, Phillip C.; Krasnogor, NatalioACS 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. - 13Sanassy, 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[ACS Full Text
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13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVSnsLbJ&md5=9834312adfad9868b6dbba4fc79b00d8Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic BiologySanassy, Daven; Widera, Pawel; Krasnogor, NatalioACS 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. - 14Gorochowski, 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[Crossref], [PubMed], [CAS], Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38jjtVylug%253D%253D&md5=c5eff7371098b5af2775859d8f2a97d2Toward Engineering Biosystems With Emergent Collective FunctionsGorochowski 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 AntoniFrontiers 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.
- 15Jiang, 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[Crossref], [PubMed], [CAS], Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisFCktLfI&md5=9dc5851f6b881ad4462871a6d14b2789NIHBA: a network interdiction approach for metabolic engineering designJiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, NatalioBioinformatics (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).
- 16Misirli, 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
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16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXpsl2nsbc%253D&md5=2ad6f7fe4326f8e9f99bfdd81b8db010A Computational Workflow for the Automated Generation of Models of Genetic DesignsMisirli, Goksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S.; Densmore, Douglas; Myers, Chris; Wipat, AnilACS 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. - 17Tellechea-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[ACS Full Text
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17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXjsVOntr0%253D&md5=282b32256dcc6a167abd1fb5b8484f29Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain EngineeringTellechea-Luzardo, Jonathan; Winterhalter, Charles; Widera, Pawel; Kozyra, Jerzy; de Lorenzo, Victor; Krasnogor, NatalioACS 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. - 18Watanabe, 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[ACS Full Text
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18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtF2ru7vJ&md5=488c35440c6d0805bda237f384b8473ciBioSim 3: A Tool for Model-Based Genetic Circuit DesignWatanabe, Leandro; Nguyen, Tramy; Zhang, Michael; Zundel, Zach; Zhang, Zhen; Madsen, Curtis; Roehner, Nicholas; Myers, ChrisACS 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. - 19Nielsen, 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
- 20Gutiérrez, 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
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20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmsVahs7k%253D&md5=f78eebffdd4b71ba31ef43e866d2d65fA New Improved and Extended Version of the Multicell Bacterial Simulator groGutierrez, Martin; Gregorio-Godoy, Paula; Perez del Pulgar, Guillermo; Munoz, Luis E.; Saez, Sandra; Rodriguez-Paton, AlfonsoACS 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. - 21Chandran, D. and Sauro, H. M. (2012) Hierarchical modeling for synthetic biology. ACS Synth. Biol. 1, 353– 364, DOI: 10.1021/sb300033q[ACS Full Text
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21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVOjsLzM&md5=c15cf99d9b31177f83d5f27d8bf1b752Hierarchical Modeling for Synthetic BiologyChandran, 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. - 22Bilitchenko, 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[Crossref], [PubMed], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXlsl2gt70%253D&md5=2bbd834f5054908816c4269a63ac9f06Eugene - a domain specific language for specifying and constraining synthetic biological parts, devices, and systemsBilitchenko, Lesia; Liu, Adam; Cheung, Sherine; Weeding, Emma; Xia, Bing; Leguia, Mariana; Anderson, J. Christopher; Densmore, DouglasPLoS 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.
- 23Beal, 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[Crossref], [PubMed], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtFajt7vK&md5=0a966eed7599c00db7575db44b84beb5Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networksBeal, Jacob; Lu, Ting; Weiss, RonPLoS 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.
- 24Villalobos, 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[Crossref], [PubMed], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD28vkvVyhug%253D%253D&md5=5e6e9a72714e8f69be3d8d351812a351Gene Designer: a synthetic biology tool for constructing artificial DNA segmentsVillalobos Alan; Ness Jon E; Gustafsson Claes; Minshull Jeremy; Govindarajan SridharBMC 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.
- 25Gaspar, 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[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFWktrrI&md5=e27f1e042cf756fe9121634b3e6b0b4cEuGene: maximizing synthetic gene design for heterologous expressionGaspar, Paulo; Oliveira, Jose Luis; Frommlet, Joerg; Santos, Manuel A. S.; Moura, GabrielaBioinformatics (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.
- 26Czar, M. J., Cai, Y., and Peccoud, J. (2009) Writing DNA with GenoCAD. Nucleic Acids Res. 37, W40– W47, DOI: 10.1093/nar/gkp361[Crossref], [PubMed], [CAS], Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXosFSkur0%253D&md5=26aa3c147e123d9e5e7bd97b0163b52aWriting DNA with GenoCADCzar, Michael J.; Cai, Yizhi; Peccoud, JeanNucleic 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.
- 27Pedersen, 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[Crossref], [PubMed], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXpslOqtLc%253D&md5=75e9d9e6a098037a38389a9ba31e36f0Towards programming languages for genetic engineering of living cellsPedersen, Michael; Phillips, AndrewJournal 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.
- 28Misirli, 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[Crossref], [PubMed], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXkt1yru7g%253D&md5=064e5ca461d2c7e4c87fe2bf09cf4ac3Model annotation for synthetic biology: automating model to nucleotide sequence conversionMisirli, Goksel; Hallinan, Jennifer S.; Yu, Tommy; Lawson, James R.; Wimalaratne, Sarala M.; Cooling, Michael T.; Wipat, AnilBioinformatics (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.
- 29Swainston, 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[Crossref], [PubMed], [CAS], Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXovFSjs7Y%253D&md5=cb41cf070468e4b02cd00793d402baf2PartsGenie: an integrated tool for optimizing and sharing synthetic biology partsSwainston, 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.
- 30Hoops, 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[Crossref], [PubMed], [CAS], Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xht1OgsrvK&md5=ff340a6c0c48f525a92a50c983aa1dddCOPASI - A COmplex PAthway SImulatorHoops, Stefan; Sahle, Sven; Gauges, Ralph; Lee, Christine; Pahle, Juergen; Simus, Natalia; Singhal, Mudita; Xu, Liang; Mendes, Pedro; Kummer, UrsulaBioinformatics (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.
- 31Moraru, 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[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjnvVensQ%253D%253D&md5=06143c5507ddf62477e3a572001f71a6Virtual Cell modelling and simulation software environmentMoraru I I; Schaff J C; Slepchenko B M; Blinov M L; Morgan F; Lakshminarayana A; Gao F; Li Y; Loew L MIET 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.
- 32Galdzicki, 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[Crossref], [PubMed], [CAS], Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXpsVGqurk%253D&md5=28f1ad342a0ce01bf297a70dbb73fe68The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biologyGaldzicki, 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.
- 33Hucka, 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[Crossref], [PubMed], [CAS], Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXit1ygu78%253D&md5=841c34faa3edec2131880858d8ce322fThe systems biology markup language (SBML): a medium for representation and exchange of biochemical network modelsHucka, 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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- 36Mısırlı, 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[ACS Full Text
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- 80Baig, H. and Madsen, J. (2017) Simulation Approach for Timing Analysis of Genetic Logic Circuits. ACS Synth. Biol. 6, 1169– 1179, DOI: 10.1021/acssynbio.6b00296[ACS Full Text
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80https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXht1WisLo%253D&md5=4b85481fe7376ca41a2f2406bb94f9e2Simulation Approach for Timing Analysis of Genetic Logic CircuitsBaig, Hasan; Madsen, JanACS Synthetic Biology (2017), 6 (7), 1169-1179CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)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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