1
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Radde N, Mortensen GA, Bhat D, Shah S, Clements JJ, Leonard SP, McGuffie MJ, Mishler DM, Barrick JE. Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology. Nat Commun 2024; 15:6242. [PMID: 39048554 PMCID: PMC11269670 DOI: 10.1038/s41467-024-50639-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.
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Affiliation(s)
- Noor Radde
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Genevieve A Mortensen
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Diya Bhat
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Shireen Shah
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Joseph J Clements
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Sean P Leonard
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Matthew J McGuffie
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Dennis M Mishler
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
- The Freshman Research Initiative, College of Natural Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Jeffrey E Barrick
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA.
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2
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Matuszyńska A, Ebenhöh O, Zurbriggen MD, Ducat DC, Axmann IM. A new era of synthetic biology-microbial community design. Synth Biol (Oxf) 2024; 9:ysae011. [PMID: 39086602 PMCID: PMC11290361 DOI: 10.1093/synbio/ysae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 06/21/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.
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Affiliation(s)
- Anna Matuszyńska
- Computational Life Science, Department of Biology, RWTH Aachen University, Aachen 52074, Germany
- Cluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Oliver Ebenhöh
- Cluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Matias D Zurbriggen
- Cluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute of Synthetic Biology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Daniel C Ducat
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, United States
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824, United States
- Institute for Synthetic Microbiology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Ilka M Axmann
- Cluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute for Synthetic Microbiology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
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3
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Iram A, Dong Y, Ignea C. Synthetic biology advances towards a bio-based society in the era of artificial intelligence. Curr Opin Biotechnol 2024; 87:103143. [PMID: 38781699 DOI: 10.1016/j.copbio.2024.103143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.
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Affiliation(s)
- Attia Iram
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Yueming Dong
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Codruta Ignea
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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4
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Yu W, Zhang S, Zhao S, Chen LG, Cao J, Ye H, Yan J, Zhao Q, Mo B, Wang Y, Jiao Y, Ma Y, Huang X, Qian W, Dai J. Designing a synthetic moss genome using GenoDesigner. NATURE PLANTS 2024; 10:848-856. [PMID: 38831044 DOI: 10.1038/s41477-024-01693-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/10/2024] [Indexed: 06/05/2024]
Abstract
The de novo synthesis of genomes has made unprecedented progress and achieved milestones, particularly in bacteria and yeast. However, the process of synthesizing a multicellular plant genome has not progressed at the same pace, due to the complexity of multicellular plant genomes, technical difficulties associated with large genome size and structure, and the intricacies of gene regulation and expression in plants. Here we outline the bottom-up design principles for the de novo synthesis of the Physcomitrium patens (that is, earthmoss) genome. To facilitate international collaboration and accessibility, we have developed and launched a public online design platform called GenoDesigner. This platform offers an intuitive graphical interface enabling users to efficiently manipulate extensive genome sequences, even up to the gigabase level. This tool is poised to greatly expedite the synthesis of the P. patens genome, offering an essential reference and roadmap for the synthesis of plant genomes.
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Affiliation(s)
- Wenfei Yu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shuo Zhang
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Shijun Zhao
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lian-Ge Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Jie Cao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hao Ye
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jianbin Yan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiao Zhao
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Beixin Mo
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Ying Wang
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuling Jiao
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yingxin Ma
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoluo Huang
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Wenfeng Qian
- University of Chinese Academy of Sciences, Beijing, China.
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
| | - Junbiao Dai
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.
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5
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Giannantoni L, Bardini R, Savino A, Di Carlo S. Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems. BMC Bioinformatics 2024; 25:166. [PMID: 38664639 PMCID: PMC11046772 DOI: 10.1186/s12859-024-05782-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.
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Affiliation(s)
- Leonardo Giannantoni
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Roberta Bardini
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy.
| | - Alessandro Savino
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
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6
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Radde N, Mortensen GA, Bhat D, Shah S, Clements JJ, Leonard SP, McGuffie MJ, Mishler DM, Barrick JE. Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588465. [PMID: 38645188 PMCID: PMC11030366 DOI: 10.1101/2024.04.08.588465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli. Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.
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Affiliation(s)
- Noor Radde
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Genevieve A. Mortensen
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Diya Bhat
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Shireen Shah
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Joseph J. Clements
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Sean P. Leonard
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matthew J. McGuffie
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Dennis M. Mishler
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
- The Freshman Research Initiative, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jeffrey E. Barrick
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712, USA
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7
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Benítez-Chao DF, Balderas-Cisneros FDJ, León-Buitimea A, Morones-Ramírez JR. Design and in silico analysis of a whole-cell biosensor able to kill methicillin-resistant Staphylococcus aureus. Biotechnol Appl Biochem 2021; 69:1373-1382. [PMID: 34081352 DOI: 10.1002/bab.2210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/01/2021] [Indexed: 01/03/2023]
Abstract
The rise of methicillin-resistant Staphylococcus aureus (MRSA) infections has gained concern throughout the world over the past decades. Alternative therapeutic agents to antibiotics are rapidly growing to impede the proliferation of MRSA-caused infections. Lately, synthetic biology techniques have developed whole-cell biosensors by designing gene circuitry capable of sensing quorum-sensing (QS) molecules of pathogens and triggering expression of an antimicrobial moiety that kills MRSA and therefore prevents its further proliferation. Here, an E. coli was engineered in silico to act as a whole-cell biosensor that senses QS molecules from MRSA and triggers the expression of a bacteriocin that kills MRSA. To achieve this functionality, biosensor and bacteriocin modules were constructed and assembled into a vector. Both modules were codon-optimized to increase the yield production of the recombinant proteins. We then demonstrate in silico that the construction of a dual biosensor-killer plasmid, which holds two genetical modules known as biosensor and bacteriocin modules, enables the recombinant host to sense QS molecules from MRSA. Our designed whole-cell biosensor demonstrates in silico its ability to produce and secrete the bacteriocin as a function of the external concentration of autoinducer peptide from MRSA. These in silico results unravel the possibility of designing antimicrobial smarter therapeutics against resistant pathogens.
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Affiliation(s)
- Diego Francisco Benítez-Chao
- Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Av. Universidad, S/N, Ciudad Universitaria, San Nicolas de los Garza, N.L., 66455, México.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Parque de Investigación e Innovación Tecnológica, Apodaca, Nuevo León, México
| | - Francisco de Jesús Balderas-Cisneros
- Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Av. Universidad, S/N, Ciudad Universitaria, San Nicolas de los Garza, N.L., 66455, México.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Parque de Investigación e Innovación Tecnológica, Apodaca, Nuevo León, México
| | - Angel León-Buitimea
- Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Av. Universidad, S/N, Ciudad Universitaria, San Nicolas de los Garza, N.L., 66455, México.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Parque de Investigación e Innovación Tecnológica, Apodaca, Nuevo León, México
| | - José Rubén Morones-Ramírez
- Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Av. Universidad, S/N, Ciudad Universitaria, San Nicolas de los Garza, N.L., 66455, México.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Parque de Investigación e Innovación Tecnológica, Apodaca, Nuevo León, México
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8
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Choi K, Karr JR, Sauro HM. Status and Challenges of Reproducibility in Computational Systems and Synthetic Biology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11525-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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9
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Abstract
Synthetic biology is a field of scientific research that applies engineering principles to living organisms and living systems. It is a field that is increasing in scope with respect to organisms engineered, practical outcomes, and systems integration. There is a commercial dimension as well, where living organisms are engineered as green technologies that could offer alternatives to industrial standards in the pharmaceutical and petroleum-based chemical industries. This review attempts to provide an introduction to this field as well as a consideration of important contributions that exemplify how synthetic biology may be commensurate or even disproportionate with the complexity of living systems. The engineerability of living systems remains a difficult task, yet advancements are reported at an ever-increasing pace.
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Affiliation(s)
- Martin M Hanczyc
- University of Trento, Department of Cellular, Computational, and Integrative Biology (CIBIO)
- University of New Mexico, Chemical and Biological Engineering.
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10
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Belén Paredes M, Eugenia Sulen M. An overview of synthetic biology. BIONATURA 2020. [DOI: 10.21931/rb/2020.05.01.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Synthetic Biology is the combination of basic sciences with engineering. The aim of Synthetic Biology is to create, design, and redesign biological systems and devices to understand biological processes and to achieve useful and sophisticated functionalities to improve human welfare. When the engineering community took part in the discussion for the definition of Synthetic Biology, the idea of extraction and reassembly of “biological parts” along with the principles of abstraction, modularity, and standardization was introduced. Genetic Engineering is one of the many essential tools for synthetic biology, and even though they share the DNA manipulation basis and approach to intervene in the complexity of molecular biology, they differ in many aspects, and the two terms should not be used interchangeably. Some of the applications that have already been done by Synthetic Biology include the production of 1,4-butanediol (BDO), the antimalarial drug artemisinin, and the anticancer compound taxol. The potential of Synthetic Biology to design new genomes without immediate biological ancestry has raised ontological, political, economic, and ethical concerns based on the possibility that synthetic biology may be intrinsically unethical.
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11
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Detection of inorganic ions and organic molecules with cell-free biosensing systems. J Biotechnol 2019; 300:78-86. [DOI: 10.1016/j.jbiotec.2019.05.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/23/2019] [Accepted: 05/24/2019] [Indexed: 11/17/2022]
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12
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Kang MK, Tullman-Ercek D. Engineering expression and function of membrane proteins. Methods 2018; 147:66-72. [DOI: 10.1016/j.ymeth.2018.04.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 04/03/2018] [Accepted: 04/16/2018] [Indexed: 01/18/2023] Open
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13
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Bhatia SP, Smanski MJ, Voigt CA, Densmore DM. Genetic Design via Combinatorial Constraint Specification. ACS Synth Biol 2017; 6:2130-2135. [PMID: 28874044 DOI: 10.1021/acssynbio.7b00154] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present a formal language for specifying via constraints a "design space" of DNA constructs composed of genetic parts, and an algorithm for automatically and correctly creating a novel representation of the space of satisfying designs. The language is simple, captures a large class of design spaces, and possesses algorithms for common operations on design spaces. The flexibility of this approach is demonstrated using a 16-gene nitrogen fixation pathway and genetic logic circuits.
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Affiliation(s)
- Swapnil P. Bhatia
- Biological
Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Michael J. Smanski
- Department
of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, Minnesota 55108, United States
| | - Christopher A. Voigt
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas M. Densmore
- Biological
Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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14
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Bypassing the Restriction System To Improve Transformation of Staphylococcus epidermidis. J Bacteriol 2017; 199:JB.00271-17. [PMID: 28559294 DOI: 10.1128/jb.00271-17] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 05/21/2017] [Indexed: 12/23/2022] Open
Abstract
Staphylococcus epidermidis is the leading cause of infections on indwelling medical devices worldwide. Intrinsic antibiotic resistance and vigorous biofilm production have rendered these infections difficult to treat and, in some cases, require the removal of the offending medical prosthesis. With the exception of two widely passaged isolates, RP62A and 1457, the pathogenesis of infections caused by clinical S. epidermidis strains is poorly understood due to the strong genetic barrier that precludes the efficient transformation of foreign DNA into clinical isolates. The difficulty in transforming clinical S. epidermidis isolates is primarily due to the type I and IV restriction-modification systems, which act as genetic barriers. Here, we show that efficient plasmid transformation of clinical S. epidermidis isolates from clonal complexes 2, 10, and 89 can be realized by employing a plasmid artificial modification (PAM) in Escherichia coli DC10B containing a Δdcm mutation. This transformative technique should facilitate our ability to genetically modify clinical isolates of S. epidermidis and hence improve our understanding of their pathogenesis in human infections.IMPORTANCE Staphylococcus epidermidis is a source of considerable morbidity worldwide. The underlying mechanisms contributing to the commensal and pathogenic lifestyles of S. epidermidis are poorly understood. Genetic manipulations of clinically relevant strains of S. epidermidis are largely prohibited due to the presence of a strong restriction barrier. With the introductions of the tools presented here, genetic manipulation of clinically relevant S. epidermidis isolates has now become possible, thus improving our understanding of S. epidermidis as a pathogen.
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15
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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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16
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Chao R, Mishra S, Si T, Zhao H. Engineering biological systems using automated biofoundries. Metab Eng 2017; 42:98-108. [PMID: 28602523 PMCID: PMC5544601 DOI: 10.1016/j.ymben.2017.06.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022]
Abstract
Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.
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Affiliation(s)
- Ran Chao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Shekhar Mishra
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tong Si
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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17
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Yang J, Yu S, Gong B, An N, Alterovitz G. Biobrick chain recommendations for genetic circuit design. Comput Biol Med 2017; 86:31-39. [PMID: 28499216 DOI: 10.1016/j.compbiomed.2017.04.019] [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: 12/19/2016] [Revised: 04/12/2017] [Accepted: 04/29/2017] [Indexed: 10/19/2022]
Abstract
Synthetic biology databases have collected numerous biobricks to accelerate genetic circuit design. However, selecting biobricks is a tough task. Here, we leverage the fact that these manually designed circuits can provide underlying knowledge to support biobrick selection. We propose to design a recommendation system based on the analysis of available genetic circuits, which can narrow down the biobrick selection range and provide candidate biobricks for users to choose. A recommendation strategy based on a Markov model is established to tackle this issue. Furthermore, a biobrick chain recommendation algorithm Sira is proposed that applies a dynamic programming process on a layered state transition graph to obtain the top k recommendation results. In addition, a weighted filtering strategy, WFSira, is proposed to augment the performance of Sira. The experimental results on the Registry of Standard Biological Parts show that Sira outperforms other algorithms significantly for biobrick recommendations, with approximately 30% improvement in terms of recall rate. It is also able to make biobrick chain recommendations. WFSira can further improve the recall rate of Sira by an average of 7.5% for the top 5 recommendations.
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Affiliation(s)
- Jiaoyun Yang
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Song Yu
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Bowen Gong
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Ning An
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Gil Alterovitz
- Harvard Medical School, Boston Children's Hospital, MA, USA.
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18
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Leon M, Woods ML, Fedorec AJH, Barnes CP. A computational method for the investigation of multistable systems and its application to genetic switches. BMC SYSTEMS BIOLOGY 2016; 10:130. [PMID: 27927198 PMCID: PMC5142341 DOI: 10.1186/s12918-016-0375-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/13/2016] [Indexed: 11/11/2022]
Abstract
Background Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. Results Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. Conclusions Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0375-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Miriam Leon
- Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Mae L Woods
- Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Alex J H Fedorec
- Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK. .,Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, UK.
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19
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Li J, Meng H, Wang Y. Synbiological systems for complex natural products biosynthesis. Synth Syst Biotechnol 2016; 1:221-229. [PMID: 29062947 PMCID: PMC5625725 DOI: 10.1016/j.synbio.2016.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 08/24/2016] [Accepted: 08/24/2016] [Indexed: 10/25/2022] Open
Abstract
Natural products (NPs) continue to play a pivotal role in drug discovery programs. The rapid development of synthetic biology has conferred the strategies of NPs production. Synthetic biology is a new engineering discipline that aims to produce desirable products by rationally programming the biological parts and manipulating the pathways. However, there is still a challenge for integrating a heterologous pathway in chassis cells for overproduction purpose due to the limited characterized parts, modules incompatibility, and cell tolerance towards product. Enormous endeavors have been taken for mentioned issues. Herein, in this review, the progresses in naturally discovering novel biological parts and rational design of synthetic biological parts are reviewed, combining with the advanced assembly technologies, pathway engineering, and pathway optimization in global network guidance. The future perspectives are also presented.
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Affiliation(s)
- Jianhua Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Hailin Meng
- Bioengineering Research Center, Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China
| | - Yong Wang
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
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20
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Mısırlı G, Hallinan J, Pocock M, Lord P, McLaughlin JA, Sauro H, Wipat A. Data Integration and Mining for Synthetic Biology Design. ACS Synth Biol 2016; 5:1086-1097. [PMID: 27110921 DOI: 10.1021/acssynbio.5b00295] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.
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Affiliation(s)
- Göksel Mısırlı
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | - Jennifer Hallinan
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | - Matthew Pocock
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
- Turing Ate My Hamster Ltd, NE27
0RT Newcastle upon Tyne, United Kingdom
| | - Phillip Lord
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | | | - Herbert Sauro
- Department
of Bioengineering, University of Washington, Seattle, Washington 98105, United States
| | - Anil Wipat
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
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21
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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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22
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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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23
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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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24
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Suter B, Zhang X, Pesce CG, Mendelsohn AR, Dinesh-Kumar SP, Mao JH. Next-Generation Sequencing for Binary Protein-Protein Interactions. Front Genet 2015; 6:346. [PMID: 26734059 PMCID: PMC4681833 DOI: 10.3389/fgene.2015.00346] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2015] [Accepted: 11/26/2015] [Indexed: 12/21/2022] Open
Abstract
The yeast two-hybrid (Y2H) system exploits host cell genetics in order to display binary protein-protein interactions (PPIs) via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS), and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine.
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Affiliation(s)
| | | | | | - Andrew R Mendelsohn
- Next Interactions, Inc., RichmondCA, USA; Regenerative Sciences Institute, SunnyvaleCA, USA
| | | | - Jian-Hua Mao
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, USA
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25
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Improving transformation of Staphylococcus aureus belonging to the CC1, CC5 and CC8 clonal complexes. PLoS One 2015; 10:e0119487. [PMID: 25807379 PMCID: PMC4373697 DOI: 10.1371/journal.pone.0119487] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Accepted: 01/13/2015] [Indexed: 12/24/2022] Open
Abstract
Methicillin resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen found in hospital and community environments that can cause serious infections. A major barrier to genetic manipulations of clinical isolates has been the considerable difficulty in transforming these strains with foreign plasmids, such as those from E. coli, in part due to the type I and IV Restriction Modification (R-M) barriers. Here we combine a Plasmid Artificial Modification (PAM) system with DC10B E. coli cells (dcm mutants) to bypass the barriers of both type I and IV R-M of S. aureus, thus allowing E. coli plasmid DNA to be transformed directly into clinical MRSA strains MW2, N315 and LAC, representing three of the most common clonal complexes. Successful transformation of clinical S. aureus isolates with E. coli-derived plasmids should greatly increase the ability to genetically modify relevant S. aureus strains and advance our understanding of S. aureus pathogenesis.
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26
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Abstract
ABSTRACT
The scientific and technical ambition of contemporary synthetic biology is the engineering of biological objects with a degree of predictability comparable to those made through electric and industrial manufacturing. To this end, biological parts with given specifications are sequence-edited, standardized, and combined into devices, which are assembled into complete systems. This goal, however, faces the customary context dependency of biological ingredients and their amenability to mutation. Biological orthogonality (i.e., the ability to run a function in a fashion minimally influenced by the host) is thus a desirable trait in any deeply engineered construct. Promiscuous conjugative plasmids found in environmental bacteria have evolved precisely to autonomously deploy their encoded activities in a variety of hosts, and thus they become excellent sources of basic building blocks for genetic and metabolic circuits. In this article we review a number of such reusable functions that originated in environmental plasmids and keep their properties and functional parameters in a variety of hosts. The properties encoded in the corresponding sequences include
inter alia
origins of replication, DNA transfer machineries, toxin-antitoxin systems, antibiotic selection markers, site-specific recombinases, effector-dependent transcriptional regulators (with their cognate promoters), and metabolic genes and operons. Several of these sequences have been standardized as BioBricks and/or as components of the SEVA (Standard European Vector Architecture) collection. Such formatting facilitates their physical composability, which is aimed at designing and deploying complex genetic constructs with new-to-nature properties.
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27
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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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28
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Marchisio MA. Modular design of synthetic gene circuits with biological parts and pools. Methods Mol Biol 2015; 1244:137-65. [PMID: 25487096 DOI: 10.1007/978-1-4939-1878-2_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Synthetic gene circuits can be designed in an electronic fashion by displaying their basic components-Standard Biological Parts and Pools of molecules-on the computer screen and connecting them with hypothetical wires. This procedure, achieved by our add-on for the software ProMoT, was successfully applied to bacterial circuits. Recently, we have extended this design-methodology to eukaryotic cells. Here, highly complex components such as promoters and Pools of mRNA contain hundreds of species and reactions whose calculation demands a rule-based modeling approach. We showed how to build such complex modules via the joint employment of the software BioNetGen (rule-based modeling) and ProMoT (modularization). In this chapter, we illustrate how to utilize our computational tool for synthetic biology with the in silico implementation of a simple eukaryotic gene circuit that performs the logic AND operation.
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Affiliation(s)
- Mario Andrea Marchisio
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland,
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29
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Dräger A, Palsson BØ. Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2014; 2:61. [PMID: 25538939 PMCID: PMC4259112 DOI: 10.3389/fbioe.2014.00061] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 11/14/2014] [Indexed: 11/17/2022] Open
Abstract
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.
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Affiliation(s)
- Andreas Dräger
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Cognitive Systems, Center for Bioinformatics Tübingen (ZBIT), Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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30
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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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31
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Mustard J, Levin M. Bioelectrical Mechanisms for Programming Growth and Form: Taming Physiological Networks for Soft Body Robotics. Soft Robot 2014. [DOI: 10.1089/soro.2014.0011] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Jessica Mustard
- Department of Biology and Center for Regenerative and Developmental Biology, Tufts University, Medford, Massachusetts
| | - Michael Levin
- Department of Biology and Center for Regenerative and Developmental Biology, Tufts University, Medford, Massachusetts
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Carbonell P, Parutto P, Baudier C, Junot C, Faulon JL. Retropath: automated pipeline for embedded metabolic circuits. ACS Synth Biol 2014; 3:565-77. [PMID: 24131345 DOI: 10.1021/sb4001273] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolic circuits are a promising alternative to other conventional genetic circuits as modular parts implementing functionalities required for synthetic biology applications. To date, metabolic design has been mainly focused on production circuits. Emergent applications such as smart therapeutics, however, require circuits that enable sensing and regulation. Here, we present RetroPath, an automated pipeline for embedded metabolic circuits that explores the circuit design space from a given set of specifications and selects the best circuits to implement based on desired constraints. Synthetic biology circuits embedded in a chassis organism that are capable of controlling the production, processing, sensing, and the release of specific molecules were enumerated in the metabolic space through a standard procedure. In that way, design and implementation of applications such as therapeutic circuits that autonomously diagnose and treat disease, are enabled, and their optimization is streamlined.
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The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nat Biotechnol 2014; 32:545-50. [DOI: 10.1038/nbt.2891] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2013] [Accepted: 12/20/2013] [Indexed: 02/03/2023]
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Swainston N, Currin A, Day PJ, Kell DB. GeneGenie: optimized oligomer design for directed evolution. Nucleic Acids Res 2014; 42:W395-400. [PMID: 24782527 PMCID: PMC4086129 DOI: 10.1093/nar/gku336] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
GeneGenie, a new online tool available at http://www.gene-genie.org, is introduced to support the design and self-assembly of synthetic genes and constructs. GeneGenie allows for the design of oligonucleotide cohorts encoding the gene sequence optimized for expression in any suitable host through an intuitive, easy-to-use web interface. The tool ensures consistent oligomer overlapping melting temperatures, minimizes the likelihood of misannealing, optimizes codon usage for expression in a selected host, allows for specification of forward and reverse cloning sequences (for downstream ligation) and also provides support for mutagenesis or directed evolution studies. Directed evolution studies are enabled through the construction of variant libraries via the optional specification of ‘variant codons’, containing mixtures of bases, at any position. For example, specifying the variant codon TNT (where N is any nucleotide) will generate an equimolar mixture of the codons TAT, TCT, TGT and TTT at that position, encoding a mixture of the amino acids Tyr, Ser, Cys and Phe. This facility is demonstrated through the use of GeneGenie to develop and synthesize a library of enhanced green fluorescent protein variants.
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Affiliation(s)
- Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
| | - Andrew Currin
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | - Philip J Day
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
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Fernández-Castané A, Fehér T, Carbonell P, Pauthenier C, Faulon JL. Computer-aided design for metabolic engineering. J Biotechnol 2014; 192 Pt B:302-13. [PMID: 24704607 DOI: 10.1016/j.jbiotec.2014.03.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 03/18/2014] [Accepted: 03/24/2014] [Indexed: 12/20/2022]
Abstract
The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes.
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Affiliation(s)
- Alfred Fernández-Castané
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Tamás Fehér
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Pablo Carbonell
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Cyrille Pauthenier
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Jean-Loup Faulon
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
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Marchisio MA. In silico design and in vivo implementation of yeast gene Boolean gates. J Biol Eng 2014; 8:6. [PMID: 24485181 PMCID: PMC3926364 DOI: 10.1186/1754-1611-8-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 01/25/2014] [Indexed: 12/05/2022] Open
Abstract
In our previous computational work, we showed that gene digital circuits can be automatically designed in an electronic fashion. This demands, first, a conversion of the truth table into Boolean formulas with the Karnaugh map method and, then, the translation of the Boolean formulas into circuit schemes organized into layers of Boolean gates and Pools of signal carriers. In our framework, gene digital circuits that take up to three different input signals (chemicals) arise from the composition of three kinds of basic Boolean gates, namely YES, NOT, and AND. Here we present a library of YES, NOT, and AND gates realized via plasmidic DNA integration into the yeast genome. Boolean behavior is reproduced via the transcriptional control of a synthetic bipartite promoter that contains sequences of the yeast VPH1 and minimal CYC1 promoters together with operator binding sites for bacterial (i.e. orthogonal) repressor proteins. Moreover, model-driven considerations permitted us to pinpoint a strategy for re-designing gates when a better digital performance is required. Our library of well-characterized Boolean gates is the basis for the assembly of more complex gene digital circuits. As a proof of concepts, we engineered two 2-input OR gates, designed by our software, by combining YES and NOT gates present in our library.
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Affiliation(s)
- Mario A Marchisio
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland.
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Dharmadi Y, Patel K, Shapland E, Hollis D, Slaby T, Klinkner N, Dean J, Chandran SS. High-throughput, cost-effective verification of structural DNA assembly. Nucleic Acids Res 2013; 42:e22. [PMID: 24203706 PMCID: PMC3936733 DOI: 10.1093/nar/gkt1088] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
DNA ‘assembly’ from ‘building blocks’ remains a cornerstone in synthetic biology, whether it be for gene synthesis (∼1 kb), pathway engineering (∼10 kb) or synthetic genomes (>100 kb). Despite numerous advances in the techniques used for DNA assembly, verification of the assembly is still a necessity, which becomes cost-prohibitive and a logistical challenge with increasing scale. Here we describe for the first time a comprehensive, high-throughput solution for structural DNA assembly verification by restriction digest using exhaustive in silico enzyme screening, rolling circle amplification of plasmid DNA, capillary electrophoresis and automated digest pattern recognition. This low-cost and robust methodology has been successfully used to screen over 31 000 clones of DNA constructs at <$1 per sample.
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Affiliation(s)
- Yandi Dharmadi
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, CA 94608, USA
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39
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40
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Chiang AWT, Hwang MJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinformatics 2013; 14 Suppl 16:S5. [PMID: 24564638 PMCID: PMC3853143 DOI: 10.1186/1471-2105-14-s16-s5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. RESULTS We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. CONCLUSIONS These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.
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Casini A, MacDonald JT, De Jonghe J, Christodoulou G, Freemont PS, Baldwin GS, Ellis T. One-pot DNA construction for synthetic biology: the Modular Overlap-Directed Assembly with Linkers (MODAL) strategy. Nucleic Acids Res 2013; 42:e7. [PMID: 24153110 PMCID: PMC3874208 DOI: 10.1093/nar/gkt915] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Overlap-directed DNA assembly methods allow multiple DNA parts to be assembled together in one reaction. These methods, which rely on sequence homology between the ends of DNA parts, have become widely adopted in synthetic biology, despite being incompatible with a key principle of engineering: modularity. To answer this, we present MODAL: a Modular Overlap-Directed Assembly with Linkers strategy that brings modularity to overlap-directed methods, allowing assembly of an initial set of DNA parts into a variety of arrangements in one-pot reactions. MODAL is accompanied by a custom software tool that designs overlap linkers to guide assembly, allowing parts to be assembled in any specified order and orientation. The in silico design of synthetic orthogonal overlapping junctions allows for much greater efficiency in DNA assembly for a variety of different methods compared with using non-designed sequence. In tests with three different assembly technologies, the MODAL strategy gives assembly of both yeast and bacterial plasmids, composed of up to five DNA parts in the kilobase range with efficiencies of between 75 and 100%. It also seamlessly allows mutagenesis to be performed on any specified DNA parts during the process, allowing the one-step creation of construct libraries valuable for synthetic biology applications.
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Affiliation(s)
- Arturo Casini
- Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK and Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
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Liang C, Krüger B, Dandekar T. GoSynthetic database tool to analyse natural and engineered molecular processes. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat043. [PMID: 23813641 PMCID: PMC3694605 DOI: 10.1093/database/bat043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications. Database URL:http://gosyn.bioapps.biozentrum.uni-wuerzburg.de.
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Affiliation(s)
- Chunguang Liang
- Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany and European Molecular Biology Laboratory, Meyerhofstr. 1, 69012 Heidelberg, Germany
| | - Beate Krüger
- Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany and European Molecular Biology Laboratory, Meyerhofstr. 1, 69012 Heidelberg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany and European Molecular Biology Laboratory, Meyerhofstr. 1, 69012 Heidelberg, Germany
- *Corresponding author: Tel: +49 931 318 4551; Fax: +49 931 318 4552;
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Marchisio MA, Colaiacovo M, Whitehead E, Stelling J. Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits. BMC SYSTEMS BIOLOGY 2013; 7:42. [PMID: 23705868 PMCID: PMC3680069 DOI: 10.1186/1752-0509-7-42] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 05/07/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND The modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools. RESULTS We developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part's species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits' truth tables. CONCLUSIONS For the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.
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Affiliation(s)
- Mario Andrea Marchisio
- ETH Zurich and Swiss Institute of Bioinformatics, D-BSSE, Mattenstrasse 26, Basel 4058, Switzerland.
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Linshiz G, Stawski N, Poust S, Bi C, Keasling JD, Hillson NJ. PaR-PaR laboratory automation platform. ACS Synth Biol 2013; 2:216-22. [PMID: 23654257 DOI: 10.1021/sb300075t] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Labor-intensive multistep biological tasks, such as the construction and cloning of DNA molecules, are prime candidates for laboratory automation. Flexible and biology-friendly operation of robotic equipment is key to its successful integration in biological laboratories, and the efforts required to operate a robot must be much smaller than the alternative manual lab work. To achieve these goals, a simple high-level biology-friendly robot programming language is needed. We have developed and experimentally validated such a language: Programming a Robot (PaR-PaR). The syntax and compiler for the language are based on computer science principles and a deep understanding of biological workflows. PaR-PaR allows researchers to use liquid-handling robots effectively, enabling experiments that would not have been considered previously. After minimal training, a biologist can independently write complicated protocols for a robot within an hour. Adoption of PaR-PaR as a standard cross-platform language would enable hand-written or software-generated robotic protocols to be shared across laboratories.
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Affiliation(s)
- Gregory Linshiz
- Fuels Synthesis
Division, Joint BioEnergy Institute, Emeryville,
California 94608,
United States
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road Mail Stop 978R4121, Berkeley, California 94720, United States
| | - Nina Stawski
- Fuels Synthesis
Division, Joint BioEnergy Institute, Emeryville,
California 94608,
United States
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road Mail Stop 978R4121, Berkeley, California 94720, United States
| | - Sean Poust
- Department of Chemical & Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California 94720, United States
| | - Changhao Bi
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road Mail Stop 978R4121, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Fuels Synthesis
Division, Joint BioEnergy Institute, Emeryville,
California 94608,
United States
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road Mail Stop 978R4121, Berkeley, California 94720, United States
- Department of Chemical & Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California 94720, United States
| | - Nathan J. Hillson
- Fuels Synthesis
Division, Joint BioEnergy Institute, Emeryville,
California 94608,
United States
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road Mail Stop 978R4121, Berkeley, California 94720, United States
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
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Thamamongood T, Lim NZL, Ho TY, Ayukawa S, Kiga D, Chow KL. Cultivation of Synthetic Biology with the iGEM Competition. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2013. [DOI: 10.20965/jaciii.2013.p0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main goal of synthetic biology is to create new biological modules that augment or modify the behavior of living organisms in performing different tasks. These modules are useful in a wide range of applications, such as medicine, agriculture, energy and environmental remediation. The concept is simple, but a paradigm shift needs to be in place among future life scientists and engineers to embrace this new direction. The international Genetically Engineered Machine (iGEM) competition fits this purpose well as a synthetic biology competition mainly for undergraduate students. Participants design and construct biological devices using standardized and customized biological parts that are then characterized and submitted to an existing and ever expanding library. Overall, iGEM is an eye-opening learning experience for undergraduate students. It has made a strong educational impact on participating students and cultivated a future cohort of synthetic biology practitioners and ambassadors.
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2ab assembly: a methodology for automatable, high-throughput assembly of standard biological parts. J Biol Eng 2013; 7:2. [PMID: 23305072 PMCID: PMC3563576 DOI: 10.1186/1754-1611-7-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Accepted: 01/01/2013] [Indexed: 11/10/2022] Open
Abstract
There is growing demand for robust DNA assembly strategies to quickly and accurately fabricate genetic circuits for synthetic biology. One application of this technology is reconstitution of multi-gene assemblies. Here, we integrate a new software tool chain with 2ab assembly and show that it is robust enough to generate 528 distinct composite parts with an error-free success rate of 96%. Finally, we discuss our findings in the context of its implications for biosafety and biosecurity.
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47
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Platforms for Genetic Design Automation. METHODS IN MICROBIOLOGY 2013. [DOI: 10.1016/b978-0-12-417029-2.00007-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register]
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48
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Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat Biotechnol 2012; 30:1137-42. [PMID: 23034349 DOI: 10.1038/nbt.2401] [Citation(s) in RCA: 267] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Accepted: 09/19/2012] [Indexed: 01/09/2023]
Abstract
Synthetic genetic programs are built from circuits that integrate sensors and implement temporal control of gene expression. Transcriptional circuits are layered by using promoters to carry the signal between circuits. In other words, the output promoter of one circuit serves as the input promoter to the next. Thus, connecting circuits requires physically connecting a promoter to the next circuit. We show that the sequence at the junction between the input promoter and circuit can affect the input-output response (transfer function) of the circuit. A library of putative sequences that might reduce (or buffer) such context effects, which we refer to as 'insulator parts', is screened in Escherichia coli. We find that ribozymes that cleave the 5' untranslated region (5'-UTR) of the mRNA are effective insulators. They generate quantitatively identical transfer functions, irrespective of the identity of the input promoter. When these insulators are used to join synthetic gene circuits, the behavior of layered circuits can be predicted using a mathematical model. The inclusion of insulators will be critical in reliably permuting circuits to build different programs.
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Baquero F. Metagenomic epidemiology: a public health need for the control of antimicrobial resistance. Clin Microbiol Infect 2012; 18 Suppl 4:67-73. [PMID: 22647054 DOI: 10.1111/j.1469-0691.2012.03860.x] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The intestine is an 'environment', a shared space where the interior and the exterior of the organism merge. The complexity of the intestinal microbiome modulates such interaction, and reflects the coordinated evolution of animals and intestinal microbes. The intestinal microbiome is exposed to the environmental resistome, to intestinal organisms from other hosts and also to microbiome-damaging agents, such as antibiotics. The result is a 'genetic-genomic-metagenomic reactor' where resistance genes flow among different biological units of different hierarchical levels, such as integrons, transposons, plasmids, clones, species or genetic exchange communities. Metagenomics provides the possibility to explore the presence of antibiotic resistance genes in all these biological and evolutionary units, and to identify possible 'high risk associations'. Multi-layered metagenomic epidemiology is required to understand and eventually to predict and apply interventions aiming to limit antibiotic resistance.
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Affiliation(s)
- F Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS and CIBERESP, and Joint Unit for Antimicrobial Resistance and Virulence, Ramón y Cajal Hospital-Centre for Biotechnology CSIC, Madrid, Spain
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50
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The PLOS ONE synthetic biology collection: six years and counting. PLoS One 2012; 7:e43231. [PMID: 22916228 PMCID: PMC3419720 DOI: 10.1371/journal.pone.0043231] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2012] [Accepted: 07/16/2012] [Indexed: 11/19/2022] Open
Abstract
Since it was launched in 2006, PLOS ONE has published over fifty articles illustrating the many facets of the emerging field of synthetic biology. This article reviews these publications by organizing them into broad categories focused on DNA synthesis and assembly techniques, the development of libraries of biological parts, the use of synthetic biology in protein engineering applications, and the engineering of gene regulatory networks and metabolic pathways. Finally, we review articles that describe enabling technologies such as software and modeling, along with new instrumentation. In order to increase the visibility of this body of work, the papers have been assembled into the PLOS ONE Synthetic Biology Collection (www.ploscollections.org/synbio). Many of the innovative features of the PLOS ONE web site will help make this collection a resource that will support a lively dialogue between readers and authors of PLOS ONE synthetic biology papers. The content of the collection will be updated periodically by including relevant articles as they are published by the journal. Thus, we hope that this collection will continue to meet the publishing needs of the synthetic biology community.
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