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Watanabe L, Nguyen T, Zhang M, Zundel Z, Zhang Z, Madsen C, Roehner N, Myers C. iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synth Biol 2019; 8:1560-1563. [PMID: 29944839 DOI: 10.1021/acssynbio.8b00078] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The iBioSim tool has been developed to facilitate the design of genetic circuits via a model-based design strategy. This paper illustrates the new features incorporated into the tool for DNA circuit design, design analysis, and design synthesis, all of which can be used in a workflow for the systematic construction of new genetic circuits.
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Affiliation(s)
- Leandro Watanabe
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Tramy Nguyen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Michael Zhang
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Curtis Madsen
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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Medley JK, Goldberg AP, Karr JR. Guidelines for Reproducibly Building and Simulating Systems Biology Models. IEEE Trans Biomed Eng 2016; 63:2015-20. [PMID: 27429432 DOI: 10.1109/tbme.2016.2591960] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Reproducibility is the cornerstone of the scientific method. However, currently, many systems biology models cannot easily be reproduced. This paper presents methods that address this problem. METHODS We analyzed the recent Mycoplasma genitalium whole-cell (WC) model to determine the requirements for reproducible modeling. RESULTS We determined that reproducible modeling requires both repeatable model building and repeatable simulation. CONCLUSION New standards and simulation software tools are needed to enhance and verify the reproducibility of modeling. New standards are needed to explicitly document every data source and assumption, and new deterministic parallel simulation tools are needed to quickly simulate large, complex models. SIGNIFICANCE We anticipate that these new standards and software will enable researchers to reproducibly build and simulate more complex models, including WC models.
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Abstract
Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE).
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Nemhauser JL, Torii KU. Plant synthetic biology for molecular engineering of signalling and development. NATURE PLANTS 2016; 2:16010. [PMID: 27249346 PMCID: PMC5164986 DOI: 10.1038/nplants.2016.10] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Molecular genetic studies of model plants in the past few decades have identified many key genes and pathways controlling development, metabolism and environmental responses. Recent technological and informatics advances have led to unprecedented volumes of data that may uncover underlying principles of plants as biological systems. The newly emerged discipline of synthetic biology and related molecular engineering approaches is built on this strong foundation. Today, plant regulatory pathways can be reconstituted in heterologous organisms to identify and manipulate parameters influencing signalling outputs. Moreover, regulatory circuits that include receptors, ligands, signal transduction components, epigenetic machinery and molecular motors can be engineered and introduced into plants to create novel traits in a predictive manner. Here, we provide a brief history of plant synthetic biology and significant recent examples of this approach, focusing on how knowledge generated by the reference plant Arabidopsis thaliana has contributed to the rapid rise of this new discipline, and discuss potential future directions.
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Affiliation(s)
| | - Keiko U Torii
- Department of Biology, University of Washington, Seattle, Washington 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
- Institute of Transformative Biomolecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8601, Japan
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Shih SCC, Goyal G, Kim PW, Koutsoubelis N, Keasling JD, Adams PD, Hillson NJ, Singh AK. A Versatile Microfluidic Device for Automating Synthetic Biology. ACS Synth Biol 2015; 4:1151-64. [PMID: 26075958 DOI: 10.1021/acssynbio.5b00062] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
New microbes are being engineered that contain the genetic circuitry, metabolic pathways, and other cellular functions required for a wide range of applications such as producing biofuels, biobased chemicals, and pharmaceuticals. Although currently available tools are useful in improving the synthetic biology process, further improvements in physical automation would help to lower the barrier of entry into this field. We present an innovative microfluidic platform for assembling DNA fragments with 10× lower volumes (compared to that of current microfluidic platforms) and with integrated region-specific temperature control and on-chip transformation. Integration of these steps minimizes the loss of reagents and products compared to that with conventional methods, which require multiple pipetting steps. For assembling DNA fragments, we implemented three commonly used DNA assembly protocols on our microfluidic device: Golden Gate assembly, Gibson assembly, and yeast assembly (i.e., TAR cloning, DNA Assembler). We demonstrate the utility of these methods by assembling two combinatorial libraries of 16 plasmids each. Each DNA plasmid is transformed into Escherichia coli or Saccharomyces cerevisiae using on-chip electroporation and further sequenced to verify the assembly. We anticipate that this platform will enable new research that can integrate this automated microfluidic platform to generate large combinatorial libraries of plasmids and will help to expedite the overall synthetic biology process.
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Affiliation(s)
- Steve C. C. Shih
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
| | - Garima Goyal
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Peter W. Kim
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
| | - Nicolas Koutsoubelis
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
- Department of Chemical & Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California 94720, United States
| | - Paul D. Adams
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Nathan J. Hillson
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Anup K. Singh
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
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Abstract
The design and construction of engineered organisms is an emerging new discipline called synthetic biology and holds considerable promise as a new technological platform. The design of biologically engineered systems is however nontrivial, requiring contributions from a wide array of disciplines. One particular issue that confronts synthetic biologists is the ability to unambiguously describe novel designs such that they can be reengineered by a third-party. For this reason, the synthetic biology open language (SBOL) was developed as a community wide standard for formally representing biological designs. A design created by one engineering team can be transmitted electronically to another who can then use this design to reproduce the experimental results. The development and the community of the SBOL standard started in 2008 and has since grown in use with now over 80 participants, including international, academic, and industrial interests. SBOL has stimulated the development of repositories and software tools to help synthetic biologists in their design efforts. This chapter summarizes the latest developments and future of the SBOL standard and its supporting infrastructure.
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Karr JR, Takahashi K, Funahashi A. The principles of whole-cell modeling. Curr Opin Microbiol 2015; 27:18-24. [PMID: 26115539 DOI: 10.1016/j.mib.2015.06.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 05/25/2015] [Accepted: 06/05/2015] [Indexed: 11/17/2022]
Abstract
Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
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Affiliation(s)
- Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Koichi Takahashi
- RIKEN Quantitative Biology Center, RIKEN, Osaka 565-0874, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
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Watanabe LH, Myers CJ. Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits. Front Bioeng Biotechnol 2014; 2:55. [PMID: 25506588 PMCID: PMC4246920 DOI: 10.3389/fbioe.2014.00055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 10/29/2014] [Indexed: 11/21/2022] Open
Abstract
This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.
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Affiliation(s)
- Leandro H Watanabe
- Department of Electrical and Computer Engineering, The University of Utah , Salt Lake City, UT , USA
| | - Chris J Myers
- Department of Electrical and Computer Engineering, The University of Utah , Salt Lake City, UT , USA
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Marchisio MA. Parts & pools: a framework for modular design of synthetic gene circuits. Front Bioeng Biotechnol 2014; 2:42. [PMID: 25340051 PMCID: PMC4186347 DOI: 10.3389/fbioe.2014.00042] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 09/16/2014] [Indexed: 01/27/2023] Open
Abstract
Published in 2008, Parts & Pools represents one of the first attempts to conceptualize the modular design of bacterial synthetic gene circuits with Standard Biological Parts (DNA segments) and Pools of molecules referred to as common signal carriers (e.g., RNA polymerases and ribosomes). The original framework for modeling bacterial components and designing prokaryotic circuits evolved over the last years and brought, first, to the development of an algorithm for the automatic design of Boolean gene circuits. This is a remarkable achievement since gene digital circuits have a broad range of applications that goes from biosensors for health and environment care to computational devices. More recently, Parts & Pools was enabled to give a proper formal description of eukaryotic biological circuit components. This was possible by employing a rule-based modeling approach, a technique that permits a faithful calculation of all the species and reactions involved in complex systems such as eukaryotic cells and compartments. In this way, Parts & Pools is currently suitable for the visual and modular design of synthetic gene circuits in yeast and mammalian cells too.
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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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