1
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Lux MW, Strychalski EA, Vora GJ. Advancing reproducibility can ease the 'hard truths' of synthetic biology. Synth Biol (Oxf) 2023; 8:ysad014. [PMID: 38022744 PMCID: PMC10640854 DOI: 10.1093/synbio/ysad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/26/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023] Open
Abstract
Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.
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Affiliation(s)
- Matthew W Lux
- Research & Operations Directorate, U.S. Army Combat Capabilities Development Command Chemical Biological Center, APG, MD 21010, USA
| | - Elizabeth A Strychalski
- Cellular Engineering Group, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Gary J Vora
- Center for Bio/Molecular Science & Engineering, U.S. Naval Research Laboratory, Washington, DC 20375, USA
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2
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Abstract
As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.
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Affiliation(s)
- Matthew Crowther
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
| | - Anil Wipat
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
| | - Ángel Goñi-Moreno
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
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3
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Mısırlı G, Yang B, James K, Wipat A. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synth Biol 2021; 10:3304-3315. [PMID: 34762797 DOI: 10.1021/acssynbio.1c00157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, ST5 5BG, U.K
| | - Bill Yang
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, U.K
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
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4
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Mante J, Hao Y, Jett J, Joshi U, Keating K, Lu X, Nakum G, Rodriguez NE, Tang J, Terry L, Wu X, Yu E, Downie JS, McInnes BT, Nguyen MH, Sepulvado B, Young EM, Myers CJ. Synthetic Biology Knowledge System. ACS Synth Biol 2021; 10:2276-2285. [PMID: 34387462 DOI: 10.1021/acssynbio.1c00188] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Yikai Hao
- University of California San Diego, La Jolla, California 92093, United States
| | - Jacob Jett
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Udayan Joshi
- University of California San Diego, La Jolla, California 92093, United States
| | - Kevin Keating
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Xiang Lu
- University of California San Diego, La Jolla, California 92093, United States
| | - Gaurav Nakum
- University of California San Diego, La Jolla, California 92093, United States
| | | | - Jiawei Tang
- University of California San Diego, La Jolla, California 92093, United States
| | - Logan Terry
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Xuanyu Wu
- University of California San Diego, La Jolla, California 92093, United States
| | - Eric Yu
- University of Utah, Salt Lake City, Utah 84112, United States
| | - J. Stephen Downie
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Bridget T. McInnes
- Virginia Commonwealth University, Richmond, Virginia 23284, United States
| | - Mai H. Nguyen
- University of California San Diego, La Jolla, California 92093, United States
| | - Brandon Sepulvado
- NORC at the University of Chicago Bethesda, Chicago, Illinois 60637, United States
| | - Eric M. Young
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Chris J. Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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5
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Garagounis C, Delkis N, Papadopoulou KK. Unraveling the roles of plant specialized metabolites: using synthetic biology to design molecular biosensors. THE NEW PHYTOLOGIST 2021; 231:1338-1352. [PMID: 33997999 DOI: 10.1111/nph.17470] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/16/2021] [Indexed: 05/25/2023]
Abstract
Plants are a rich source of specialized metabolites with a broad range of bioactivities and many applications in human daily life. Over the past decades significant progress has been made in identifying many such metabolites in different plant species and in elucidating their biosynthetic pathways. However, the biological roles of plant specialized metabolites remain elusive and proposed functions lack an identified underlying molecular mechanism. Understanding the roles of specialized metabolites frequently is hampered by their dynamic production and their specific spatiotemporal accumulation within plant tissues and organs throughout a plant's life cycle. In this review, we propose the employment of strategies from the field of Synthetic Biology to construct and optimize genetically encoded biosensors that can detect individual specialized metabolites in a standardized and high-throughput manner. This will help determine the precise localization of specialized metabolites at the tissue and single-cell levels. Such information will be useful in developing complete system-level models of specialized plant metabolism, which ultimately will demonstrate how the biosynthesis of specialized metabolites is integrated with the core processes of plant growth and development.
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Affiliation(s)
- Constantine Garagounis
- Department of Biochemistry and Biotechnology, Plant and Environmental Biotechnology Laboratory, University of Thessaly, Larissa, 41500, Greece
| | - Nikolaos Delkis
- Department of Biochemistry and Biotechnology, Plant and Environmental Biotechnology Laboratory, University of Thessaly, Larissa, 41500, Greece
| | - Kalliope K Papadopoulou
- Department of Biochemistry and Biotechnology, Plant and Environmental Biotechnology Laboratory, University of Thessaly, Larissa, 41500, Greece
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6
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Fontanarrosa P, Doosthosseini H, Borujeni AE, Dorfan Y, Voigt CA, Myers C. Genetic Circuit Dynamics: Hazard and Glitch Analysis. ACS Synth Biol 2020; 9:2324-2338. [PMID: 32786351 DOI: 10.1021/acssynbio.0c00055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.
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Affiliation(s)
- Pedro Fontanarrosa
- Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Hamid Doosthosseini
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amin Espah Borujeni
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Yuval Dorfan
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Christopher A. Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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7
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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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8
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Watanabe L, Nguyen T, Zhang M, Zundel Z, Zhang Z, Madsen C, Roehner N, Myers C. iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synth Biol 2019; 8:1560-1563. [PMID: 29944839 DOI: 10.1021/acssynbio.8b00078] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
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 analysis, and design synthesis, all of which can be used in a workflow for the systematic construction of new genetic circuits.
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Affiliation(s)
- Leandro Watanabe
- 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
| | - Michael Zhang
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Curtis Madsen
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris 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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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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10
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Predicting the evolution of Escherichia coli by a data-driven approach. Nat Commun 2018; 9:3562. [PMID: 30177705 PMCID: PMC6120903 DOI: 10.1038/s41467-018-05807-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 06/12/2018] [Indexed: 12/31/2022] Open
Abstract
A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments. How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.
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11
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McLaughlin JA, Myers CJ, Zundel Z, Mısırlı G, Zhang M, Ofiteru ID, Goñi-Moreno A, Wipat A. SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology. ACS Synth Biol 2018; 7:682-688. [PMID: 29316788 DOI: 10.1021/acssynbio.7b00403] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The SynBioHub repository ( https://synbiohub.org ) is an open-source software project that facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g., the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.
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Affiliation(s)
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zach Zundel
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Göksel Mısırlı
- School
of Computing and Mathematics, Keele University, Newcastle, ST5 5BG, U.K
| | - Michael Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Irina Dana Ofiteru
- School
of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
| | - Angel Goñi-Moreno
- School
of Computing, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
| | - Anil Wipat
- School
of Computing, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
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12
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Decoene T, De Paepe B, Maertens J, Coussement P, Peters G, De Maeseneire SL, De Mey M. Standardization in synthetic biology: an engineering discipline coming of age. Crit Rev Biotechnol 2017; 38:647-656. [PMID: 28954542 DOI: 10.1080/07388551.2017.1380600] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Leaping DNA read-and-write technologies, and extensive automation and miniaturization are radically transforming the field of biological experimentation by providing the tools that enable the cost-effective high-throughput required to address the enormous complexity of biological systems. However, standardization of the synthetic biology workflow has not kept abreast with dwindling technical and resource constraints, leading, for example, to the collection of multi-level and multi-omics large data sets that end up disconnected or remain under- or even unexploited. PURPOSE In this contribution, we critically evaluate the various efforts, and the (limited) success thereof, in order to introduce standards for defining, designing, assembling, characterizing, and sharing synthetic biology parts. The causes for this success or the lack thereof, as well as possible solutions to overcome these, are discussed. CONCLUSION Akin to other engineering disciplines, extensive standardization will undoubtedly speed-up and reduce the cost of bioprocess development. In this respect, further implementation of synthetic biology standards will be crucial for the field in order to redeem its promise, i.e. to enable predictable forward engineering.
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Affiliation(s)
- Thomas Decoene
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Brecht De Paepe
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Jo Maertens
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | | | - Gert Peters
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Sofie L De Maeseneire
- b InBio.be, Centre for Industrial Biotechnology and Biocatalysis, Ghent University , Ghent , Belgium
| | - Marjan De Mey
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
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13
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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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14
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Mısırlı G, Madsen C, Murieta IS, Bultelle M, Flanagan K, Pocock M, Hallinan J, McLaughlin JA, Clark‐Casey J, Lyne M, Micklem G, Stan G, Kitney R, Wipat A. Constructing synthetic biology workflows in the cloud. ENGINEERING BIOLOGY 2017. [DOI: 10.1049/enb.2017.0001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Göksel Mısırlı
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | - Curtis Madsen
- Electrical & Computer Engineering Department Boston University Boston USA
| | | | | | - Keith Flanagan
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | | | | | | | - Justin Clark‐Casey
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Mike Lyne
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Gos Micklem
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Guy‐Bart Stan
- Department of Bioengineering Imperial College London London UK
| | - Richard Kitney
- Department of Bioengineering Imperial College London London UK
| | - Anil Wipat
- School of Computing Science Newcastle University Newcastle upon Tyne UK
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15
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Huynh L, Tagkopoulos I. A Parts Database with Consensus Parameter Estimation for Synthetic Circuit Design. ACS Synth Biol 2016; 5:1412-1420. [PMID: 27454439 DOI: 10.1021/acssynbio.5b00205] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Mathematical modeling and numerical simulation are crucial to support design decisions in synthetic biology. Accurate estimation of parameter values is key, as direct experimental measurements are difficult and time-consuming. Insufficient data, incompatible measurements, and specialized models that lack universal parameters make this task challenging. Here, we have created a database (PAMDB) that integrates data from 135 publications that contain 118 circuits and 165 genetic parts of the bacterium Escherichia coli. We used a succinct, universal model formulation to describe the part behavior in each circuit. We introduce a constrained consensus inference method that was used to infer the value of the model parameters and evaluated its performance through cross-validation in a benchmark of 23 circuits. We discuss these results and summarize the challenges in data integration and parameter inference. This work provides a resource and a methodology that can be used as a point of reference for synthetic circuit modeling.
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Affiliation(s)
- Linh Huynh
- Department of Computer Science & UC Davis Genome Center, University of California Davis, Davis, California 95616 United States
| | - Ilias Tagkopoulos
- Department of Computer Science & UC Davis Genome Center, University of California Davis, Davis, California 95616 United States
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16
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Madsen C, McLaughlin JA, Mısırlı G, Pocock M, Flanagan K, Hallinan J, Wipat A. The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs. ACS Synth Biol 2016; 5:487-97. [PMID: 27268205 DOI: 10.1021/acssynbio.5b00210] [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: 01/06/2023]
Abstract
Recently, synthetic biologists have developed the Synthetic Biology Open Language (SBOL), a data exchange standard for descriptions of genetic parts, devices, modules, and systems. The goals of this standard are to allow scientists to exchange designs of biological parts and systems, to facilitate the storage of genetic designs in repositories, and to facilitate the description of genetic designs in publications. In order to achieve these goals, the development of an infrastructure to store, retrieve, and exchange SBOL data is necessary. To address this problem, we have developed the SBOL Stack, a Resource Description Framework (RDF) database specifically designed for the storage, integration, and publication of SBOL data. This database allows users to define a library of synthetic parts and designs as a service, to share SBOL data with collaborators, and to store designs of biological systems locally. The database also allows external data sources to be integrated by mapping them to the SBOL data model. The SBOL Stack includes two Web interfaces: the SBOL Stack API and SynBioHub. While the former is designed for developers, the latter allows users to upload new SBOL biological designs, download SBOL documents, search by keyword, and visualize SBOL data. Since the SBOL Stack is based on semantic Web technology, the inherent distributed querying functionality of RDF databases can be used to allow different SBOL stack databases to be queried simultaneously, and therefore, data can be shared between different institutes, centers, or other users.
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Affiliation(s)
- Curtis Madsen
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
- Department of Electrical & Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | | | - Göksel Mısırlı
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Matthew Pocock
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
- Turing Ate My Hamster, LTD, Newcastle
upon Tyne NE27 0RT, U.K
| | - Keith Flanagan
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Jennifer Hallinan
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
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