1
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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2
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Misirli G, Nguyen T, McLaughlin JA, Vaidyanathan P, Jones TS, Densmore D, Myers C, Wipat A. A Computational Workflow for the Automated Generation of Models of Genetic Designs. ACS Synth Biol 2019; 8:1548-1559. [PMID: 29782151 DOI: 10.1021/acssynbio.7b00459] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Computational models are essential to engineer predictable biological 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 standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology 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 Biology 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 biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.
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Affiliation(s)
- Göksel Misirli
- School of Computing and Mathematics, Keele University, Staffordshire, U.K
| | - Tramy Nguyen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | | | - Prashant Vaidyanathan
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Timothy S. Jones
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Douglas Densmore
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Anil Wipat
- ICOS, School of Computing, Newcastle University, Newcastle upon Tyne, U.K
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3
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Mısırlı G, Taylor R, Goñi-Moreno A, McLaughlin JA, Myers C, Gennari JH, Lord P, Wipat A. SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information. ACS Synth Biol 2019; 8:1498-1514. [PMID: 31059645 DOI: 10.1021/acssynbio.8b00532] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, Staffordshire ST5 5BG, UK
| | - Renee Taylor
- School of Computing and Mathematics, Keele University, Keele, Staffordshire ST5 5BG, UK
| | - Angel Goñi-Moreno
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | | | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - John H. Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington 98195, United States
| | - Phillip Lord
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
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4
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Affiliation(s)
| | - Matthieu Bultelle
- Department of Bioengineering Imperial College London London SW7 2AZ UK
| | - Richard I. Kitney
- Department of Bioengineering Imperial College London London SW7 2AZ UK
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5
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A standard-enabled workflow for synthetic biology. Biochem Soc Trans 2017; 45:793-803. [PMID: 28620041 DOI: 10.1042/bst20160347] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/29/2017] [Accepted: 03/31/2017] [Indexed: 11/17/2022]
Abstract
A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.
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6
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Zundel Z, Samineni M, Zhang Z, Myers CJ. A Validator and Converter for the Synthetic Biology Open Language. ACS Synth Biol 2017; 6:1161-1168. [PMID: 28033703 DOI: 10.1021/acssynbio.6b00277] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
This paper presents a new validation and conversion utility for the Synthetic Biology Open Language (SBOL). This utility can be accessed directly in software using the libSBOLj library, through a web interface, or using a web service via RESTful API calls. The validator checks all required and best practice rules set forth in the SBOL specification document, and it reports back to the user the location within the document of any errors found. The converter is capable of translating from/to SBOL 1, GenBank, and FASTA formats to/from SBOL 2. The SBOL Validator/Converter utility is released freely and open source under the Apache 2.0 license. The online version of the validator/converter utility can be found here: http://www.async.ece.utah.edu/sbol-validator/ . The source code for the validator/converter can be found here: http://github.com/SynBioDex/SBOL-Validator/ .
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Affiliation(s)
- Zach Zundel
- Department of Bioengineering, University of Utah, 36 S. Wasatch Drive, SMBB Room 3100, Salt
Lake City, Utah 84112, United States
| | - Meher Samineni
- Department of Electrical and Computer Engineering, University of Utah, 50 S. Central Campus Drive, MEB Room 2110, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Avenue, ENB 118, Tampa, Florida 33620, United States
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, 50 S. Central Campus Drive, MEB Room 2110, Salt Lake City, Utah 84112, United States
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7
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Otero-Muras I, Banga JR. Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality. ACS Synth Biol 2017; 6:1180-1193. [PMID: 28350462 DOI: 10.1021/acssynbio.6b00306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
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8
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Zhang M, McLaughlin JA, Wipat A, Myers CJ. SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design. ACS Synth Biol 2017; 6:1150-1160. [PMID: 28441476 DOI: 10.1021/acssynbio.6b00275] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
As the Synthetic Biology Open Language (SBOL) data and visual standards gain acceptance for describing genetic designs in a detailed and reproducible way, there is an increasing need for an intuitive sequence editor tool that biologists can use that supports these standards. This paper describes SBOLDesigner 2, a genetic design automation (GDA) tool that natively supports both the SBOL data model (Version 2) and SBOL Visual (Version 1). This software is enabled to fetch and store parts and designs from SBOL repositories, such as SynBioHub. It can also import and export data about parts and designs in FASTA, GenBank, and SBOL 1 data format. Finally, it possesses a simple and intuitive user interface. This paper describes the design process using SBOLDesigner 2, highlighting new features over the earlier prototype versions. SBOLDesigner 2 is released freely and open source under the Apache 2.0 license.
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Affiliation(s)
- Michael Zhang
- Department of Electrical
and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | | | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Chris J. Myers
- Department of Electrical
and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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9
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Appleton E, Madsen C, Roehner N, Densmore D. Design Automation in Synthetic Biology. Cold Spring Harb Perspect Biol 2017; 9:a023978. [PMID: 28246188 PMCID: PMC5378053 DOI: 10.1101/cshperspect.a023978] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.
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Affiliation(s)
- Evan Appleton
- Department of Genetics, Harvard Medical School, Harvard University, Boston, Massachusetts 02115
| | - Curtis Madsen
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Nicholas Roehner
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
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10
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Goñi-Moreno A, Carcajona M, Kim J, Martínez-García E, Amos M, de Lorenzo V. An Implementation-Focused Bio/Algorithmic Workflow for Synthetic Biology. ACS Synth Biol 2016; 5:1127-1135. [PMID: 27454551 DOI: 10.1021/acssynbio.6b00029] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e., characterization and measurement of synthetic genetic constructs) has received relatively little attention to date, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented, which is supported by information standards and computational methods and focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features are discussed. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as in the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device's kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onward, the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescence units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.
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Affiliation(s)
- Angel Goñi-Moreno
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | - Marta Carcajona
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | - Juhyun Kim
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | | | - Martyn Amos
- Informatics
Research Centre, Manchester Metropolitan University, Manchester M1 5GD, United Kingdom
| | - Víctor de Lorenzo
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
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11
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MacDonald IC, Deans TL. Tools and applications in synthetic biology. Adv Drug Deliv Rev 2016; 105:20-34. [PMID: 27568463 DOI: 10.1016/j.addr.2016.08.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 08/15/2016] [Accepted: 08/17/2016] [Indexed: 12/25/2022]
Abstract
Advances in synthetic biology have enabled the engineering of cells with genetic circuits in order to program cells with new biological behavior, dynamic gene expression, and logic control. This cellular engineering progression offers an array of living sensors that can discriminate between cell states, produce a regulated dose of therapeutic biomolecules, and function in various delivery platforms. In this review, we highlight and summarize the tools and applications in bacterial and mammalian synthetic biology. The examples detailed in this review provide insight to further understand genetic circuits, how they are used to program cells with novel functions, and current methods to reliably interface this technology in vivo; thus paving the way for the design of promising novel therapeutic applications.
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Affiliation(s)
- I Cody MacDonald
- Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, United States
| | - Tara L Deans
- Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, United States.
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12
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Sainz de Murieta I, Bultelle M, Kitney RI. Toward the First Data Acquisition Standard in Synthetic Biology. ACS Synth Biol 2016; 5:817-26. [PMID: 26854090 DOI: 10.1021/acssynbio.5b00222] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.
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Affiliation(s)
- Iñaki Sainz de Murieta
- Centre
for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom
- Department
of BioEngineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Matthieu Bultelle
- Centre
for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom
- Department
of BioEngineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Richard I Kitney
- Centre
for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom
- Department
of BioEngineering, Imperial College London, London, SW7 2AZ, United Kingdom
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13
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Nguyen T, Roehner N, Zundel Z, Myers CJ. A Converter from the Systems Biology Markup Language to the Synthetic Biology Open Language. ACS Synth Biol 2016; 5:479-86. [PMID: 26696234 DOI: 10.1021/acssynbio.5b00212] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Standards are important to synthetic biology because they enable exchange and reproducibility of genetic designs. This paper describes a procedure for converting between two standards: the Systems Biology Markup Language (SBML) and the Synthetic Biology Open Language (SBOL). SBML is a standard for behavioral models of biological systems at the molecular level. SBOL describes structural and basic qualitative behavioral aspects of a biological design. Converting SBML to SBOL enables a consistent connection between behavioral and structural information for a biological design. The conversion process described in this paper leverages Systems Biology Ontology (SBO) annotations to enable inference of a designs qualitative function.
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Affiliation(s)
| | - Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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14
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Roehner N, Beal J, Clancy K, Bartley B, Misirli G, Grünberg R, Oberortner E, Pocock M, Bissell M, Madsen C, Nguyen T, Zhang M, Zhang Z, Zundel Z, Densmore D, Gennari JH, Wipat A, Sauro HM, Myers CJ. Sharing Structure and Function in Biological Design with SBOL 2.0. ACS Synth Biol 2016; 5:498-506. [PMID: 27111421 DOI: 10.1021/acssynbio.5b00215] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The Synthetic Biology Open Language (SBOL) is a standard that enables collaborative engineering of biological systems across different institutions and tools. SBOL is developed through careful consideration of recent synthetic biology trends, real use cases, and consensus among leading researchers in the field and members of commercial biotechnology enterprises. We demonstrate and discuss how a set of SBOL-enabled software tools can form an integrated, cross-organizational workflow to recapitulate the design of one of the largest published genetic circuits to date, a 4-input AND sensor. This design encompasses the structural components of the system, such as its DNA, RNA, small molecules, and proteins, as well as the interactions between these components that determine the system's behavior/function. The demonstrated workflow and resulting circuit design illustrate the utility of SBOL 2.0 in automating the exchange of structural and functional specifications for genetic parts, devices, and the biological systems in which they operate.
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Affiliation(s)
- Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California 92008, United States
| | - Bryan Bartley
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Goksel Misirli
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Raik Grünberg
- Institute
for Research in Immunology and Cancer, University of Montreal, Montreal, Quebec H3T 1J4, Canada
| | - Ernst Oberortner
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Newcastle
upon Tyne NE27 0RT, U.K
| | | | - Curtis Madsen
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Michael Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zach Zundel
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Douglas Densmore
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - John H. Gennari
- Department
of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington 98195, United States
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Herbert M. Sauro
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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15
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Misirli G, Cavaliere M, Waites W, Pocock M, Madsen C, Gilfellon O, Honorato-Zimmer R, Zuliani P, Danos V, Wipat A. Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization. Bioinformatics 2016; 32:908-17. [PMID: 26559508 PMCID: PMC4803388 DOI: 10.1093/bioinformatics/btv660] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 10/08/2015] [Accepted: 11/03/2015] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. RESULTS We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. AVAILABILITY AND IMPLEMENTATION The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf CONTACT anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk.
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Affiliation(s)
- Goksel Misirli
- Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK
| | - Matteo Cavaliere
- School of Informatics, University of Edinburgh, Edinburgh, UK and
| | - William Waites
- School of Informatics, University of Edinburgh, Edinburgh, UK and
| | | | - Curtis Madsen
- Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK
| | - Owen Gilfellon
- Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK
| | | | - Paolo Zuliani
- Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK
| | - Vincent Danos
- School of Informatics, University of Edinburgh, Edinburgh, UK and
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK
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16
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Roehner N, Zhang Z, Nguyen T, Myers CJ. Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language. ACS Synth Biol 2015; 4:873-9. [PMID: 25822671 DOI: 10.1021/sb5003289] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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Abstract
The design and construction of engineered organisms is an emerging new discipline called synthetic biology and holds considerable promise as a new technological platform. The design of biologically engineered systems is however nontrivial, requiring contributions from a wide array of disciplines. One particular issue that confronts synthetic biologists is the ability to unambiguously describe novel designs such that they can be reengineered by a third-party. For this reason, the synthetic biology open language (SBOL) was developed as a community wide standard for formally representing biological designs. A design created by one engineering team can be transmitted electronically to another who can then use this design to reproduce the experimental results. The development and the community of the SBOL standard started in 2008 and has since grown in use with now over 80 participants, including international, academic, and industrial interests. SBOL has stimulated the development of repositories and software tools to help synthetic biologists in their design efforts. This chapter summarizes the latest developments and future of the SBOL standard and its supporting infrastructure.
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18
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Roehner N, Oberortner E, Pocock M, Beal J, Clancy K, Madsen C, Misirli G, Wipat A, Sauro H, Myers CJ. Proposed data model for the next version of the synthetic biology open language. ACS Synth Biol 2015; 4:57-71. [PMID: 24896221 DOI: 10.1021/sb500176h] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.
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Affiliation(s)
- Nicholas Roehner
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States
| | - Ernst Oberortner
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States
| | - Matthew Pocock
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts, United States
| | - Kevin Clancy
- Life Technologies, Carlsbad, California, United States
| | - Curtis Madsen
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Goksel Misirli
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Herbert Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States
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19
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Kelwick R, MacDonald JT, Webb AJ, Freemont P. Developments in the tools and methodologies of synthetic biology. Front Bioeng Biotechnol 2014; 2:60. [PMID: 25505788 PMCID: PMC4244866 DOI: 10.3389/fbioe.2014.00060] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 11/12/2014] [Indexed: 11/27/2022] Open
Abstract
Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a “body of knowledge” from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community.
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Affiliation(s)
- Richard Kelwick
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - James T MacDonald
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - Alexander J Webb
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - Paul Freemont
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
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20
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Martínez-García E, Aparicio T, Goñi-Moreno A, Fraile S, de Lorenzo V. SEVA 2.0: an update of the Standard European Vector Architecture for de-/re-construction of bacterial functionalities. Nucleic Acids Res 2014; 43:D1183-9. [PMID: 25392407 PMCID: PMC4383931 DOI: 10.1093/nar/gku1114] [Citation(s) in RCA: 148] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The Standard European Vector Architecture 2.0 database (SEVA-DB 2.0, http://seva.cnb.csic.es) is an improved and expanded version of the platform released in 2013 (doi: 10.1093/nar/gks1119) aimed at assisting the choice of optimal genetic tools for de-constructing and re-constructing complex prokaryotic phenotypes. By adopting simple compositional rules, the SEVA standard facilitates combinations of functional DNA segments that ease both the analysis and the engineering of diverse Gram-negative bacteria for fundamental or biotechnological purposes. The large number of users of the SEVA-DB during its first two years of existence has resulted in a valuable feedback that we have exploited for fixing DNA sequence errors, improving the nomenclature of the SEVA plasmids, expanding the vector collection, adding new features to the web interface and encouraging contributions of materials from the community of users. The SEVA platform is also adopting the Synthetic Biology Open Language (SBOL) for electronic-like description of the constructs available in the collection and their interfacing with genetic devices developed by other Synthetic Biology communities. We advocate the SEVA format as one interim asset for the ongoing transition of genetic design of microorganisms from being a trial-and-error endeavor to become an authentic engineering discipline.
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Affiliation(s)
- Esteban Martínez-García
- Systems Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Tomás Aparicio
- Systems Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Angel Goñi-Moreno
- Systems Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Sofía Fraile
- Systems Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Víctor de Lorenzo
- Systems Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
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21
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Roehner N, Myers CJ. Directed acyclic graph-based technology mapping of genetic circuit models. ACS Synth Biol 2014; 3:543-55. [PMID: 24650240 DOI: 10.1021/sb400135t] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
As engineering foundations such as standards and abstraction begin to mature within synthetic biology, it is vital that genetic design automation (GDA) tools be developed to enable synthetic biologists to automatically select standardized DNA components from a library to meet the behavioral specification for a genetic circuit. To this end, we have developed a genetic technology mapping algorithm that builds on the directed acyclic graph (DAG) based mapping techniques originally used to select parts for digital electronic circuit designs and implemented it in our GDA tool, iBioSim. It is among the first genetic technology mapping algorithms to adapt techniques from electronic circuit design, in particular the use of a cost function to guide the search for an optimal solution, and perhaps that which makes the greatest use of standards for describing genetic function and structure to represent design specifications and component libraries. This paper demonstrates the use of our algorithm to map the specifications for three different genetic circuits against four randomly generated libraries of increasing size to evaluate its performance against both exhaustive search and greedy variants for finding optimal and near-optimal solutions.
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City 84112, United States
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22
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Brophy JAN, Voigt CA. Principles of genetic circuit design. Nat Methods 2014; 11:508-20. [PMID: 24781324 DOI: 10.1038/nmeth.2926] [Citation(s) in RCA: 568] [Impact Index Per Article: 56.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 03/18/2014] [Indexed: 12/17/2022]
Abstract
Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.
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Affiliation(s)
- Jennifer A N Brophy
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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23
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Carbonell P, Parutto P, Herisson J, Pandit SB, Faulon JL. XTMS: pathway design in an eXTended metabolic space. Nucleic Acids Res 2014; 42:W389-94. [PMID: 24792156 PMCID: PMC4086079 DOI: 10.1093/nar/gku362] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.
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Affiliation(s)
- Pablo Carbonell
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Pierre Parutto
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Joan Herisson
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | | | - Jean-Loup Faulon
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
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