1
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Holtzapple E, Zhou G, Luo H, Tang D, Arazkhani N, Hansen C, Telmer CA, Miskov-Zivanov N. The BioRECIPE Knowledge Representation Format. ACS Synth Biol 2024; 13:2621-2624. [PMID: 39051984 PMCID: PMC11334182 DOI: 10.1021/acssynbio.4c00096] [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: 02/12/2024] [Revised: 05/22/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.
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Affiliation(s)
- Emilee Holtzapple
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Gaoxiang Zhou
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Haomiao Luo
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Difei Tang
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Niloofar Arazkhani
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Casey Hansen
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Cheryl A. Telmer
- Department
of Biological Sciences, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Natasa Miskov-Zivanov
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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2
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Zelenka NR, Di Cara N, Sharma K, Sarvaharman S, Ghataora JS, Parmeggiani F, Nivala J, Abdallah ZS, Marucci L, Gorochowski TE. Data hazards in synthetic biology. Synth Biol (Oxf) 2024; 9:ysae010. [PMID: 38973982 PMCID: PMC11227101 DOI: 10.1093/synbio/ysae010] [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: 03/15/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.
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Affiliation(s)
- Natalie R Zelenka
- Jean Golding Institute, University of Bristol, Bristol, UK
- BrisEngBio, University of Bristol, Bristol, UK
| | - Nina Di Cara
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Kieren Sharma
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | | | - Jasdeep S Ghataora
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Fabio Parmeggiani
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biochemistry, University of Bristol, Bristol, UK
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, UK
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Zahraa S Abdallah
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Lucia Marucci
- BrisEngBio, University of Bristol, Bristol, UK
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
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3
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Adler A, Bader JS, Basnight B, Booth BW, Cai J, Cho E, Collins JH, Ge Y, Grothendieck J, Keating K, Marshall T, Persikov A, Scott H, Siegelmann R, Singh M, Taggart A, Toll B, Wan KH, Wyschogrod D, Yaman F, Young EM, Celniker SE, Roehner N. Ensemble Detection of DNA Engineering Signatures. ACS Synth Biol 2024; 13:1105-1115. [PMID: 38468602 DOI: 10.1021/acssynbio.3c00398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.
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Affiliation(s)
- Aaron Adler
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Brian Basnight
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Benjamin W Booth
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jitong Cai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Elizabeth Cho
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Joseph H Collins
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Yuchen Ge
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | | | - Kevin Keating
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Tyler Marshall
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Anton Persikov
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, United States
| | - Helen Scott
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Roy Siegelmann
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, United States
| | | | - Benjamin Toll
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Kenneth H Wan
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | | | - Fusun Yaman
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Eric M Young
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Susan E Celniker
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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4
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Buson F, Gao Y, Wang B. Genetic Parts and Enabling Tools for Biocircuit Design. ACS Synth Biol 2024; 13:697-713. [PMID: 38427821 DOI: 10.1021/acssynbio.3c00691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Synthetic biology aims to engineer biological systems for customized tasks through the bottom-up assembly of fundamental building blocks, which requires high-quality libraries of reliable, modular, and standardized genetic parts. To establish sets of parts that work well together, synthetic biologists created standardized part libraries in which every component is analyzed in the same metrics and context. Here we present a state-of-the-art review of the currently available part libraries for designing biocircuits and their gene expression regulation paradigms at transcriptional, translational, and post-translational levels in Escherichia coli. We discuss the necessary facets to integrate these parts into complex devices and systems along with the current efforts to catalogue and standardize measurement data. To better display the range of available parts and to facilitate part selection in synthetic biology workflows, we established biopartsDB, a curated database of well-characterized and useful genetic part and device libraries with detailed quantitative data validated by the published literature.
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Affiliation(s)
- Felipe Buson
- College of Chemical and Biological Engineering & ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, China
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, U.K
| | - Yuanli Gao
- College of Chemical and Biological Engineering & ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, China
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, U.K
| | - Baojun Wang
- College of Chemical and Biological Engineering & ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, China
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5
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Son SH, Kang J, Shin Y, Lee C, Sung BH, Lee JY, Lee W. Sustainable production of natural products using synthetic biology: Ginsenosides. J Ginseng Res 2024; 48:140-148. [PMID: 38465212 PMCID: PMC10920010 DOI: 10.1016/j.jgr.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/23/2023] [Accepted: 12/30/2023] [Indexed: 03/12/2024] Open
Abstract
Synthetic biology approaches offer potential for large-scale and sustainable production of natural products with bioactive potency, including ginsenosides, providing a means to produce novel compounds with enhanced therapeutic properties. Ginseng, known for its non-toxic and potent qualities in traditional medicine, has been used for various medical needs. Ginseng has shown promise for its antioxidant and neuroprotective properties, and it has been used as a potential agent to boost immunity against various infections when used together with other drugs and vaccines. Given the increasing demand for ginsenosides and the challenges associated with traditional extraction methods, synthetic biology holds promise in the development of therapeutics. In this review, we discuss recent developments in microorganism producer engineering and ginsenoside production in microorganisms using synthetic biology approaches.
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Affiliation(s)
- So-Hee Son
- Research Center for Bio-Based Chemistry, Korea Research Institute of Chemical Technology (KRICT), Ulsan, Republic of Korea
| | - Jin Kang
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Biosystems and Bioengineering Program, Korea National University of Science and Technology (UST), Daejeon, Republic of Korea
| | - YuJin Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - ChaeYoung Lee
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Bong Hyun Sung
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Biosystems and Bioengineering Program, Korea National University of Science and Technology (UST), Daejeon, Republic of Korea
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Ju Young Lee
- Research Center for Bio-Based Chemistry, Korea Research Institute of Chemical Technology (KRICT), Ulsan, Republic of Korea
| | - Wonsik Lee
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
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6
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Tummler K, Klipp E. Data integration strategies for whole-cell modeling. FEMS Yeast Res 2024; 24:foae011. [PMID: 38544322 PMCID: PMC11042497 DOI: 10.1093/femsyr/foae011] [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: 12/02/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.
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Affiliation(s)
- Katja Tummler
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
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7
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Vidal G, Vitalis C, Matúte T, Núñez I, Federici F, Rudge TJ. Genetic Network Design Automation with LOICA. Methods Mol Biol 2024; 2760:393-412. [PMID: 38468100 DOI: 10.1007/978-1-0716-3658-9_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs.
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Affiliation(s)
- Gonzalo Vidal
- Interdisciplinary Computing and Complex Biosystems, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Carolus Vitalis
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Tamara Matúte
- ANID-Millennium Science Initiative Program Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Santiago, Chile
- Institute for Biological and Medical Engineering Schools of Engineering, Medicine and Biological Sciences Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Isaac Núñez
- ANID-Millennium Science Initiative Program Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Santiago, Chile
- Institute for Biological and Medical Engineering Schools of Engineering, Medicine and Biological Sciences Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Fernán Federici
- ANID-Millennium Science Initiative Program Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Santiago, Chile
- Institute for Biological and Medical Engineering Schools of Engineering, Medicine and Biological Sciences Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Timothy J Rudge
- Interdisciplinary Computing and Complex Biosystems, School of Computing, Newcastle University, Newcastle upon Tyne, UK.
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8
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Beal J, Selvarajah V, Chambonnier G, Haddock T, Vignoni A, Vidal G, Roehner N. Standardized Representation of Parts and Assembly for Build Planning. ACS Synth Biol 2023; 12:3646-3655. [PMID: 37956262 DOI: 10.1021/acssynbio.3c00418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The design and construction of genetic systems, in silico, in vitro, or in vivo, often involve the handling of various pieces of DNA that exist in different forms across an assembly process: as a standalone "part" sequence, as an insert into a carrier vector, as a digested fragment, etc. Communication about these different forms of a part and their relationships is often confusing, however, because of a lack of standardized terms. Here, we present a systematic terminology and an associated set of practices for representing genetic parts at various stages of design, synthesis, and assembly. These practices are intended to represent any of the wide array of approaches based on embedding parts in carrier vectors, such as BioBricks or Type IIS methods (e.g., GoldenGate, MoClo, GoldenBraid, and PhytoBricks), and have been successfully used as a basis for cross-institutional coordination and software tooling in the iGEM Engineering Committee.
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Affiliation(s)
- Jacob Beal
- Intelligent Software & Systems, Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
| | - Vinoo Selvarajah
- iGEM Foundation, 45 Prospect Street, Cambridge, Massachusetts 02139, United States
| | - Gaël Chambonnier
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Traci Haddock
- Asimov, Inc., 201 Brookline Avenue, Suite 1201, Boston, Massachusetts 02215, United States
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | - Gonzalo Vidal
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Nicholas Roehner
- Intelligent Software & Systems, Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
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9
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Zilberzwige-Tal S, Fontanarrosa P, Bychenko D, Dorfan Y, Gazit E, Myers CJ. Investigating and Modeling the Factors That Affect Genetic Circuit Performance. ACS Synth Biol 2023; 12:3189-3204. [PMID: 37916512 PMCID: PMC10661042 DOI: 10.1021/acssynbio.3c00151] [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: 03/11/2023] [Indexed: 11/03/2023]
Abstract
Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit's performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.
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Affiliation(s)
- Shai Zilberzwige-Tal
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Pedro Fontanarrosa
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Darya Bychenko
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Yuval Dorfan
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
- Bio-engineering,
Electrical Engineering Faculty, Holon Institute
of Technology (HIT), Holon 5810201, Israel
- Alagene
Ltd., Innovation Center, Reichman University, Herzliya 7670608, Israel
| | - Ehud Gazit
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Chris J. Myers
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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10
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Lux MW, Strychalski EA, Vora GJ. Advancing reproducibility can ease the 'hard truths' of synthetic biology. Synth Biol (Oxf) 2023; 8:ysad014. [PMID: 38022744 PMCID: PMC10640854 DOI: 10.1093/synbio/ysad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/26/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023] Open
Abstract
Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.
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Affiliation(s)
- Matthew W Lux
- Research & Operations Directorate, U.S. Army Combat Capabilities Development Command Chemical Biological Center, APG, MD 21010, USA
| | - Elizabeth A Strychalski
- Cellular Engineering Group, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Gary J Vora
- Center for Bio/Molecular Science & Engineering, U.S. Naval Research Laboratory, Washington, DC 20375, USA
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11
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Mısırlı G. libSBOLj3: a graph-based library for design and data exchange in synthetic biology. Bioinformatics 2023; 39:btad525. [PMID: 37624918 PMCID: PMC10471897 DOI: 10.1093/bioinformatics/btad525] [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: 05/22/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023] Open
Abstract
SUMMARY The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBOLj3 to facilitate data exchange and provide interoperability between computer-aided design and automation tools using this standard. The library adopts a graph-based approach. Tool developers can extend these graphs with application-specific information and use detailed validation reports to identify errors and interoperability issues and apply best practice rules. AVAILABILITY AND IMPLEMENTATION The libSBOLj3 library is implemented in Java and can be downloaded or used as a Maven dependency. The open-source project, code examples and documentation about accessing and using the library are available via GitHub at https://github.com/SynBioDex/libSBOLj3.
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Affiliation(s)
- Göksel Mısırlı
- School of Computer Science and Mathematics, Keele University, Keele, Staffordshire, ST5 5BG, United Kingdom
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12
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Di Rocco G, Taunt HN, Berto M, Jackson HO, Piccinini D, Carletti A, Scurani G, Braidi N, Purton S. A PETase enzyme synthesised in the chloroplast of the microalga Chlamydomonas reinhardtii is active against post-consumer plastics. Sci Rep 2023; 13:10028. [PMID: 37340047 DOI: 10.1038/s41598-023-37227-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/18/2023] [Indexed: 06/22/2023] Open
Abstract
Polyethylene terephthalate hydrolases (PETases) are a newly discovered and industrially important class of enzymes that catalyze the enzymatic degradation of polyethylene terephatalate (PET), one of the most abundant plastics in the world. The greater enzymatic efficiencies of PETases compared to close relatives from the cutinase and lipase families have resulted in increasing research interest. Despite this, further characterization of PETases is essential, particularly regarding their possible activity against other kinds of plastic. In this study, we exploited for the first time the use of the microalgal chloroplast for more sustainable synthesis of a PETase enzyme. A photosynthetic-restoration strategy was used to generate a marker-free transformant line of the green microalga Chlamydomonas reinhardtii in which the PETase from Ideonella sakaiensis was constitutively expressed in the chloroplast. Subsequently, the activity of the PETase against both PET and post-consumer plastics was investigated via atomic force microscopy, revealing evidence of degradation of the plastics.
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Affiliation(s)
- Giulia Di Rocco
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
| | - Henry N Taunt
- Algal Research Group, Department of Structural and Molecular Biology, University College London, Gower Street, London, UK
| | - Marcello Berto
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Harry O Jackson
- Algal Research Group, Department of Structural and Molecular Biology, University College London, Gower Street, London, UK
| | - Daniele Piccinini
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Alan Carletti
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Giulia Scurani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Niccolò Braidi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Saul Purton
- Algal Research Group, Department of Structural and Molecular Biology, University College London, Gower Street, London, UK
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13
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Sents Z, Stoughton TE, Buecherl L, Thomas PJ, Fontanarrosa P, Myers CJ. SynBioSuite: A Tool for Improving the Workflow for Genetic Design and Modeling. ACS Synth Biol 2023; 12:892-897. [PMID: 36888740 DOI: 10.1021/acssynbio.2c00597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Synthetic biology research has led to the development of many software tools for designing, constructing, editing, simulating, and sharing genetic parts and circuits. Among these tools are SBOLCanvas, iBioSim, and SynBioHub, which can be used in conjunction to create a genetic circuit design following the design-build-test-learn process. However, although automation works within these tools, most of these software tools are not integrated, and the process of transferring information between them is a very manual, error-prone process. To address this problem, this work automates some of these processes and presents SynBioSuite, a cloud-based tool that eliminates many of the drawbacks of the current approach by automating the setup and reception of results for simulating a designed genetic circuit via an application programming interface.
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Affiliation(s)
- Zachary Sents
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Thomas E Stoughton
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Payton J Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Pedro Fontanarrosa
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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14
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Mante J, Abam J, Samineni SP, Pötzsch IM, Beal J, Myers CJ. Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa. ACS Synth Biol 2023; 12:340-346. [PMID: 36595709 DOI: 10.1021/acssynbio.2c00521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Julian Abam
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Sai P Samineni
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris J Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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15
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Aldulijan I, Beal J, Billerbeck S, Bouffard J, Chambonnier G, Ntelkis N, Guerreiro I, Holub M, Ross P, Selvarajah V, Sprent N, Vidal G, Vignoni A. Functional Synthetic Biology. Synth Biol (Oxf) 2023; 8:ysad006. [PMID: 37073284 PMCID: PMC10105873 DOI: 10.1093/synbio/ysad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.
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Affiliation(s)
- Ibrahim Aldulijan
- Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, NJ, USA
| | | | - Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AG, Groningen, The Netherlands
| | - Jeff Bouffard
- Centre for Applied Synthetic Biology, and Department of Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, H4B 1R6, Québec, Canada
| | - Gaël Chambonnier
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA
| | - Nikolaos Ntelkis
- Specialized Metabolism research group, Center for Plant Systems Biology, VIB-Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Isaac Guerreiro
- iGEM Foundation, 45 Prospect Street, Cambridge, 02139, MA, USA
| | - Martin Holub
- Delft University of Technology, Van der Maasweg 9, 2629 HZ, The Netherlands
| | - Paul Ross
- BioStrat Marketing, 9965 Harbour Lake Circle, Boynton Beach, FL, 33437, USA
| | | | - Noah Sprent
- Department of Chemical Engineering, Imperial College London, South Kensington Campus, Exhibition Road, SW7 2AZ, UK
| | - Gonzalo Vidal
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Devonshire Building, Devonshire Terrace, NE1 7RU, Newcastle Upon Tyne, UK
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain
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16
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Abstract
Despite an ever-growing number of data sets that catalog and characterize interactions between microbes in different environments and conditions, many of these data are neither easily accessible nor intercompatible. These limitations present a major challenge to microbiome research by hindering the streamlined drawing of inferences across studies. Here, we propose guiding principles to make microbial interaction data more findable, accessible, interoperable, and reusable (FAIR). We outline specific use cases for interaction data that span the diverse space of microbiome research, and discuss the untapped potential for new insights that can be fulfilled through broader integration of microbial interaction data. These include, among others, the design of intercompatible synthetic communities for environmental, industrial, or medical applications, and the inference of novel interactions from disparate studies. Lastly, we envision potential trajectories for the deployment of FAIR microbial interaction data based on existing resources, reporting standards, and current momentum within the community.
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Affiliation(s)
| | - Charlie Pauvert
- Functional Microbiome Research Group, Institute of Medical Microbiology, University Hospital of RWTH, Aachen, Germany
| | - Dileep Kishore
- Bioinformatics Program and Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Bioinformatics Program and Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biology, Department of Biomedical Engineering, Department of Physics, Boston University, Boston Massachusetts, USA
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17
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Vidal G, Vitalis C, Muñoz Silva M, Castillo-Passi C, Yáñez Feliú G, Federici F, Rudge TJ. Accurate characterization of dynamic microbial gene expression and growth rate profiles. Synth Biol (Oxf) 2022; 7:ysac020. [PMID: 36267953 PMCID: PMC9569155 DOI: 10.1093/synbio/ysac020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 07/16/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can, therefore, change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for the estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics and suggests a method for the dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.
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Affiliation(s)
- Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Carolus Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carlos Castillo-Passi
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile
| | - Guillermo Yáñez Feliú
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- ANID – Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio) & FONDAP Center for Genome Regulation, Santiago, Chile
| | - Timothy J Rudge
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
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18
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Gomide MDS, Leitão MDC, Coelho CM. Biocircuits in plants and eukaryotic algae. FRONTIERS IN PLANT SCIENCE 2022; 13:982959. [PMID: 36212277 PMCID: PMC9545776 DOI: 10.3389/fpls.2022.982959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/22/2022] [Indexed: 06/16/2023]
Abstract
As one of synthetic biology's foundations, biocircuits are a strategy of genetic parts assembling to recognize a signal and to produce a desirable output to interfere with a biological function. In this review, we revisited the progress in the biocircuits technology basis and its mandatory elements, such as the characterization and assembly of functional parts. Furthermore, for a successful implementation, the transcriptional control systems are a relevant point, and the computational tools help to predict the best combinations among the biological parts planned to be used to achieve the desirable phenotype. However, many challenges are involved in delivering and stabilizing the synthetic structures. Some research experiences, such as the golden crops, biosensors, and artificial photosynthetic structures, can indicate the positive and limiting aspects of the practice. Finally, we envision that the modulatory structural feature and the possibility of finer gene regulation through biocircuits can contribute to the complex design of synthetic chromosomes aiming to develop plants and algae with new or improved functions.
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Affiliation(s)
- Mayna da Silveira Gomide
- Laboratory of Synthetic Biology, Department of Genetics and Morphology, Institute of Biological Science, University of Brasília (UnB), Brasília, Distrito Federal, Brazil
- School of Medicine, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Minas Gerais, Brazil
| | - Matheus de Castro Leitão
- Laboratory of Synthetic Biology, Department of Genetics and Morphology, Institute of Biological Science, University of Brasília (UnB), Brasília, Distrito Federal, Brazil
| | - Cíntia Marques Coelho
- Laboratory of Synthetic Biology, Department of Genetics and Morphology, Institute of Biological Science, University of Brasília (UnB), Brasília, Distrito Federal, Brazil
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19
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Cryptococcus neoformans Database in Synthetic Biology Open Language. Microbiol Resour Announc 2022; 11:e0019822. [PMID: 36000855 PMCID: PMC9476951 DOI: 10.1128/mra.00198-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Cryptococcus neoformans is the etiologic agent of cryptococcosis, a lethal worldwide disease. Synthetic biology could contribute to its better understanding through engineering genetic networks. However, its major challenge is the requirement of accessible genetic parts. The database presented here provides 23 biological parts for this organism in Synthetic Biology Open Language.
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20
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Mitchell T, Beal J, Bartley B. pySBOL3: SBOL3 for Python Programmers. ACS Synth Biol 2022; 11:2523-2526. [PMID: 35767721 DOI: 10.1021/acssynbio.2c00249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Synthetic Biology Open Language version 3 (SBOL3) provides a data model for representation of synthetic biology information across multiple scales and throughout the design-build-test-learn workflow. To support practical use of this data model, we have developed pySBOL3, a Python library that allows programmers to create and edit SBOL3 documents. Here we describe this library and key engineering decisions in its design. The resulting implementation is a compact and maintainable core that provides both a familiar, pythonic interface for manipulating SBOL3 objects as well as mechanisms for building additional extensions and representations on this base.
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Affiliation(s)
- Tom Mitchell
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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21
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Vidal G, Vidal-Céspedes C, Rudge TJ. LOICA: Integrating Models with Data for Genetic Network Design Automation. ACS Synth Biol 2022; 11:1984-1990. [PMID: 35507566 PMCID: PMC9127962 DOI: 10.1021/acssynbio.1c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Indexed: 11/30/2022]
Abstract
Genetic design automation tools are necessary to expand the scale and complexity of possible synthetic genetic networks. These tools are enabled by abstraction of a hierarchy of standardized components and devices. Abstracted elements must be parametrized from data derived from relevant experiments, and these experiments must be related to the part composition of the abstract components. Here we present Logical Operators for Integrated Cell Algorithms (LOICA), a Python package for designing, modeling, and characterizing genetic networks based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. These models can be parametrized by direct connection to data contained in Flapjack so that abstracted components of designs can characterize themselves. Models can be simulated using continuous or stochastic methods and the data published and managed using Flapjack. LOICA also outputs SBOL3 descriptions and generates graph representations of genetic network designs.
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Affiliation(s)
- Gonzalo Vidal
- Institute
for Biological and Medical Engineering, Schools of Engineering, Biology,
and Medicine, Pontificia Universidad Católica
de Chile, Santiago 7820244, Chile
- Interdisciplinary
Computing and Complex BioSystems (ICOS) Research Group, School of
Computing, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K.
| | - Carlos Vidal-Céspedes
- Institute
for Biological and Medical Engineering, Schools of Engineering, Biology,
and Medicine, Pontificia Universidad Católica
de Chile, Santiago 7820244, Chile
| | - Timothy J. Rudge
- Interdisciplinary
Computing and Complex BioSystems (ICOS) Research Group, School of
Computing, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K.
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22
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Scott H, Sun D, Beal J, Kiani S. Simulation-Based Engineering of Time-Delayed Safety Switches for Safer Gene Therapies. ACS Synth Biol 2022; 11:1782-1789. [PMID: 35412812 DOI: 10.1021/acssynbio.1c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
CRISPR-based gene editing is a powerful tool with great potential for applications in the treatment of many inherited and acquired diseases. The longer that CRISPR gene therapy is maintained within a patient, however, the higher the likelihood that it will result in problematic side effects such as off-target editing or immune response. One approach to mitigating these issues is to link the operation of the therapeutic system to a safety switch that autonomously disables its operation and removes the delivered therapeutics after some amount of time. We present here a simulation-based analysis of the potential for regulating the time delay of such a safety switch using one or two transcriptional regulators and/or recombinases. Combinatorial circuit generation identifies 30 potential architectures for such circuits, which we evaluate in simulation with respect to tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation. This modeling predicts one of these circuit architectures to have the desired dynamics and robustness, which can be further tested and applied in the context of CRISPR therapeutics.
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Affiliation(s)
- Helen Scott
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Dashan Sun
- University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Samira Kiani
- Pittsburgh Liver Research Center, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- Division of Experimental Pathology, Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15219, United States
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23
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Buecherl L, Myers CJ. Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Curr Opin Microbiol 2022; 68:102155. [PMID: 35588683 DOI: 10.1016/j.mib.2022.102155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 01/23/2023]
Abstract
Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.
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Affiliation(s)
- Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, 1111 Engineering Drive, Boulder, 80309 CO, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, 425 UCB, Boulder, 80309 CO, United States.
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24
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Bartley BA. Tyto: A Python Tool Enabling Better Annotation Practices for Synthetic Biology Data-Sharing. ACS Synth Biol 2022; 11:1373-1376. [PMID: 35226470 DOI: 10.1021/acssynbio.1c00450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
As synthetic biology becomes increasingly automated and data-driven, tools that help researchers implement FAIR (findable-accessible-interoperable-reusable) data management practices are needed. Crucially, in order to support machine processing and reusability of data, it is important that data artifacts are appropriately annotated with metadata drawn from controlled vocabularies. Unfortunately, adopting standardized annotation practices is difficult for many research groups to adopt, given the set of specialized database science skills usually required to interface with ontologies. In response to this need, Take Your Terms from Ontologies (Tyto) is a lightweight Python tool that supports the use of controlled vocabularies in everyday scripting practice. While Tyto has been developed for synthetic biology applications, its utility may extend to users working in other areas of bioinformatics research as well. Tyto is available as a Python package distribution or available as source at https://github.com/SynBioDex/tyto.
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Affiliation(s)
- Bryan A. Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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25
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Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network. Biosystems 2022; 213:104608. [DOI: 10.1016/j.biosystems.2022.104608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/07/2021] [Accepted: 01/11/2022] [Indexed: 01/12/2023]
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26
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Buecherl L, Roberts R, Fontanarrosa P, Thomas PJ, Mante J, Zhang Z, Myers CJ. Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA. ACS Synth Biol 2021; 10:2532-2540. [PMID: 34606710 DOI: 10.1021/acssynbio.1c00159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit's functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.
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Affiliation(s)
- Lukas Buecherl
- Department of Biomedical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Riley Roberts
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Pedro Fontanarrosa
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Payton J. Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jeanet Mante
- Department of Biomedical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Zhen Zhang
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Chris J. Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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27
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Plahar HA, Rich TN, Lane SD, Morrell WC, Springthorpe L, Nnadi O, Aravina E, Dai T, Fero MJ, Hillson NJ, Petzold CJ. BioParts-A Biological Parts Search Portal and Updates to the ICE Parts Registry Software Platform. ACS Synth Biol 2021; 10:2649-2660. [PMID: 34449214 DOI: 10.1021/acssynbio.1c00263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database. This distributed database allows users from one registry to query and access plasmid, strain, (DNA) part, plant seed, and protein entry types in other connected registries. Users can also transfer entries from one ICE registry to another or make them publicly accessible. Bioparts, the new search portal, combines the ease and convenience of modern web search engines with the capabilities of bioinformatics search tools such as BLAST. This portal, available at bioparts.org, allows anyone to search for publicly accessible biological part information (e.g., NCBI, iGEM, SynBioHub, Addgene), including parts publicly accessible through ICE Registries. Additionally, the portal offers a REST API that enables third-party applications and tools to access the portal's functionality programmatically.
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Affiliation(s)
- Hector A. Plahar
- DOE Agile BioFoundry, Emeryville, California 94608 ,United States
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Thomas N. Rich
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- TeselaGen Biotechnology Inc., San Francisco, California 94107, United States
| | - Stephen D. Lane
- DOE Agile BioFoundry, Emeryville, California 94608 ,United States
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Sandia National Laboratories, Livermore, California 94550, United States
| | - William C. Morrell
- DOE Agile BioFoundry, Emeryville, California 94608 ,United States
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Sandia National Laboratories, Livermore, California 94550, United States
| | - Leanne Springthorpe
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Oge Nnadi
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Elena Aravina
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Tiffany Dai
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- TeselaGen Biotechnology Inc., San Francisco, California 94107, United States
| | - Michael J. Fero
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- TeselaGen Biotechnology Inc., San Francisco, California 94107, United States
| | - Nathan J. Hillson
- DOE Agile BioFoundry, Emeryville, California 94608 ,United States
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Christopher J. Petzold
- DOE Agile BioFoundry, Emeryville, California 94608 ,United States
- DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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Clark CJ, Scott-Brown J, Gorochowski TE. paraSBOLv: a foundation for standard-compliant genetic design visualization tools. Synth Biol (Oxf) 2021; 6:ysab022. [PMID: 34712845 PMCID: PMC8546602 DOI: 10.1093/synbio/ysab022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/30/2021] [Accepted: 08/09/2021] [Indexed: 11/13/2022] Open
Abstract
Diagrams constructed from standardized glyphs are central to communicating complex design information in many engineering fields. For example, circuit diagrams are commonplace in electronics and allow for a suitable abstraction of the physical system that helps support the design process. With the development of the Synthetic Biology Open Language Visual (SBOLv), bioengineers are now positioned to better describe and share their biological designs visually. However, the development of computational tools to support the creation of these diagrams is currently hampered by an excessive burden in maintenance due to the large and expanding number of glyphs present in the standard. Here, we present a Python package called paraSBOLv that enables access to the full suite of SBOLv glyphs through the use of machine-readable parametric glyph definitions. These greatly simplify the rendering process while allowing extensive customization of the resulting diagrams. We demonstrate how the adoption of paraSBOLv can accelerate the development of highly specialized biodesign visualization tools or even form the basis for more complex software by removing the burden of maintaining glyph-specific rendering code. Looking forward, we suggest that incorporation of machine-readable parametric glyph definitions into the SBOLv standard could further simplify the development of tools to produce standard-compliant diagrams and the integration of visual standards across fields.
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Affiliation(s)
- Charlie J Clark
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - James Scott-Brown
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Bristol, UK
- BrisSynBio, University of Bristol, Bristol, UK
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Terry L, Earl J, Thayer S, Bridge S, Myers CJ. SBOLCanvas: A Visual Editor for Genetic Designs. ACS Synth Biol 2021; 10:1792-1796. [PMID: 34152132 DOI: 10.1021/acssynbio.1c00096] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
SBOLCanvas is a web-based application that can create and edit genetic constructs using the SBOL data and visual standards. SBOLCanvas allows a user to create a genetic design visually and structurally from start to finish. It also allows users to incorporate existing SBOL data from a SynBioHub repository. By the nature of being a web-based application, SBOLCanvas is readily accessible and easy to use. A live version of the latest release can be found at https://sbolcanvas.org.
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Affiliation(s)
- Logan Terry
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Jared Earl
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Sam Thayer
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Samuel Bridge
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Chris J. Myers
- University of Colorado Boulder, Boulder, 80309 Colorado, United States
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30
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Dixon TA, Williams TC, Pretorius IS. Bioinformational trends in grape and wine biotechnology. Trends Biotechnol 2021; 40:124-135. [PMID: 34108075 DOI: 10.1016/j.tibtech.2021.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 02/08/2023]
Abstract
The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change.
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Affiliation(s)
- Thomas A Dixon
- Department of Modern History, Politics and International Relations, Macquarie University, Sydney, NSW 2109, Australia.
| | - Thomas C Williams
- Department of Molecular Sciences and ARC Centre of Excellence in Synthetic Biology, Centre Headquarters, Macquarie University, Sydney, NSW 2109, Australia
| | - Isak S Pretorius
- Department of Molecular Sciences and ARC Centre of Excellence in Synthetic Biology, Centre Headquarters, Macquarie University, Sydney, NSW 2109, Australia; Chancellery, Macquarie University, Sydney, NSW 2109, Australia.
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