1
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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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2
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Crowther M, Wipat A, Goñi-Moreno Á. GENETTA: a Network-Based Tool for the Analysis of Complex Genetic Designs. ACS Synth Biol 2023; 12:3766-3770. [PMID: 37963232 DOI: 10.1021/acssynbio.3c00333] [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: 11/16/2023]
Abstract
GENETTA is a software tool that transforms synthetic biology designs into networks using graph theory for analysis and manipulation. By representing complex data as interconnected points, GENETTA allows dynamic customization of visualizations, including interaction networks and parts hierarchies. It can also merge design data from multiple databases, providing a unified perspective. The generated interactive network can be edited by adding nodes and edges, simplifying changes to existing design files. This article presents GENETTA and its features through specific use cases, showcasing its practical applications.
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Affiliation(s)
- Matthew Crowther
- School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5TG, United Kingdom
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Madrid 28223, Spain
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, United Kingdom
| | - Ángel Goñi-Moreno
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Madrid 28223, Spain
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3
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Zong DM, Sadeghpour M, Molinari S, Alnahhas RN, Hirning AJ, Giannitsis C, Ott W, Josić K, Bennett MR. Tunable Dynamics in a Multistrain Transcriptional Pulse Generator. ACS Synth Biol 2023; 12:3531-3543. [PMID: 38016068 DOI: 10.1021/acssynbio.3c00434] [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: 11/30/2023]
Abstract
One challenge in synthetic biology is the tuning of regulatory components within gene circuits to elicit a specific behavior. This challenge becomes more difficult in synthetic microbial consortia since each strain's circuit must function at the intracellular level and their combination must operate at the population level. Here we demonstrate that circuit dynamics can be tuned in synthetic consortia through the manipulation of strain fractions within the community. To do this, we construct a microbial consortium comprised of three strains of engineered Escherichia coli that, when cocultured, use homoserine lactone-mediated intercellular signaling to create a multistrain incoherent type-1 feedforward loop (I1-FFL). Like naturally occurring I1-FFL motifs in gene networks, this engineered microbial consortium acts as a pulse generator of gene expression. We demonstrate that the amplitude of the pulse can be easily tuned by adjusting the relative population fractions of the strains. We also develop a mathematical model for the temporal dynamics of the microbial consortium. This model allows us to identify population fractions that produced desired pulse characteristics, predictions that were confirmed for all but extreme fractions. Our work demonstrates that intercellular gene circuits can be effectively tuned simply by adjusting the starting fractions of each strain in the consortium.
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Affiliation(s)
- David M Zong
- Graduate Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Mehdi Sadeghpour
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Mathematics, University of Houston, Houston, Texas 77004, United States
| | - Sara Molinari
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
| | - Razan N Alnahhas
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
| | - Andrew J Hirning
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
| | - Charilaos Giannitsis
- Graduate Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - William Ott
- Department of Mathematics, University of Houston, Houston, Texas 77004, United States
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77004, United States
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, United States
| | - Matthew R Bennett
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
- Rice Synthetic Biology Institute, Rice University, Houston, Texas 77005, United States
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4
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Marken JP, Murray RM. Addressable and adaptable intercellular communication via DNA messaging. Nat Commun 2023; 14:2358. [PMID: 37095088 PMCID: PMC10126159 DOI: 10.1038/s41467-023-37788-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 03/31/2023] [Indexed: 04/26/2023] Open
Abstract
Engineered consortia are a major research focus for synthetic biologists because they can implement sophisticated behaviors inaccessible to single-strain systems. However, this functional capacity is constrained by their constituent strains' ability to engage in complex communication. DNA messaging, by enabling information-rich channel-decoupled communication, is a promising candidate architecture for implementing complex communication. But its major advantage, its messages' dynamic mutability, is still unexplored. We develop a framework for addressable and adaptable DNA messaging that leverages all three of these advantages and implement it using plasmid conjugation in E. coli. Our system can bias the transfer of messages to targeted receiver strains by 100- to 1000-fold, and their recipient lists can be dynamically updated in situ to control the flow of information through the population. This work lays the foundation for future developments that further utilize the unique advantages of DNA messaging to engineer previously-inaccessible levels of complexity into biological systems.
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Affiliation(s)
- John P Marken
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Richard M Murray
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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5
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Martínez-García E, Fraile S, Algar E, Aparicio T, Velázquez E, Calles B, Tas H, Blázquez B, Martín B, Prieto C, Sánchez-Sampedro L, Nørholm MH, Volke D, Wirth N, Dvořák P, Alejaldre L, Grozinger L, Crowther M, Goñi-Moreno A, Nikel P, Nogales J, de Lorenzo V. SEVA 4.0: an update of the Standard European Vector Architecture database for advanced analysis and programming of bacterial phenotypes. Nucleic Acids Res 2023; 51:D1558-D1567. [PMID: 36420904 PMCID: PMC9825617 DOI: 10.1093/nar/gkac1059] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
The SEVA platform (https://seva-plasmids.com) was launched one decade ago, both as a database (DB) and as a physical repository of plasmid vectors for genetic analysis and engineering of Gram-negative bacteria with a structure and nomenclature that follows a strict, fixed architecture of functional DNA segments. While the current update keeps the basic features of earlier versions, the platform has been upgraded not only with many more ready-to-use plasmids but also with features that expand the range of target species, harmonize DNA assembly methods and enable new applications. In particular, SEVA 4.0 includes (i) a sub-collection of plasmids for easing the composition of multiple DNA segments with MoClo/Golden Gate technology, (ii) vectors for Gram-positive bacteria and yeast and [iii] off-the-shelf constructs with built-in functionalities. A growing collection of plasmids that capture part of the standard-but not its entirety-has been compiled also into the DB and repository as a separate corpus (SEVAsib) because of its value as a resource for constructing and deploying phenotypes of interest. Maintenance and curation of the DB were accompanied by dedicated diffusion and communication channels that make the SEVA platform a popular resource for genetic analyses, genome editing and bioengineering of a large number of microorganisms.
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Affiliation(s)
- Esteban Martínez-García
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Sofía Fraile
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Elena Algar
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Tomás Aparicio
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Elena Velázquez
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Belén Calles
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Huseyin Tas
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Blas Blázquez
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | | | | | | | - Morten H H Nørholm
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Nicolas T Wirth
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Pavel Dvořák
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno 62500 Czech Republic
| | - Lorea Alejaldre
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (INIA-CSIC), Pozuelo de Alarcón 28223, Spain
| | - Lewis Grozinger
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (INIA-CSIC), Pozuelo de Alarcón 28223, Spain
- School of Computing, Newcastle University, NE4 5TG, UK
| | - Matthew Crowther
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (INIA-CSIC), Pozuelo de Alarcón 28223, Spain
- School of Computing, Newcastle University, NE4 5TG, UK
| | - Angel Goñi-Moreno
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (INIA-CSIC), Pozuelo de Alarcón 28223, Spain
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Juan Nogales
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
| | - Víctor de Lorenzo
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Cantoblanco-Madrid, Spain
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6
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Abstract
As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.
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Affiliation(s)
- Matthew Crowther
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
| | - Anil Wipat
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
| | - Ángel Goñi-Moreno
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
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7
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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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8
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Greco FV, Irvine T, Grierson CS, Gorochowski TE. Design and Assembly of Multilevel Transcriptional and Translational Regulators for Stringent Control of Gene Expression. Methods Mol Biol 2022; 2518:99-110. [PMID: 35666441 DOI: 10.1007/978-1-0716-2421-0_6] [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] [Indexed: 06/15/2023]
Abstract
Precise control of gene expression is crucial when reprogramming the behavior of living cells. However, common inducible systems often lack the ability to stringently control gene expression due to the use of a single type of regulator that can be susceptible to unavoidable biomolecular fluctuations. In contrast, multilevel controllers (MLCs) employ several forms of regulation simultaneously to overcome this issue, ensuring a reduced basal expression while minimally affecting the maximum induced expression level that can be achieved. Here, we show how our publicly available genetic toolkit can be used to simplify the assembly of MLCs for the stringent control of gene expression. We demonstrate how new compatible parts can be designed and explain the rapid end-to-end assembly procedure.
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Affiliation(s)
- F Veronica Greco
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Thea Irvine
- School of Biological Sciences, University of Bristol, Bristol, UK
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9
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Abstract
Much progress has been made in developing tools to generate component-based design representations of biological systems from standard libraries of parts. Most biological designs, however, are still specified at the sequence level. Consequently, there exists a need for a tool that can be used to automatically infer component-based design representations from sequences, particularly in cases when those sequences have minimal levels of annotation. Such a tool would assist computational synthetic biologists in bridging the gap between the outputs of sequence editors and the inputs to more sophisticated design tools, and it would facilitate their development of automated workflows for design curation and quality control. Accordingly, we introduce Synthetic Biology Curation Tools (SYNBICT), a Python tool suite for automation-assisted annotation, curation, and functional inference for genetic designs. We have validated SYNBICT by applying it to genetic designs in the DARPA Synergistic Discovery & Design (SD2) program and the International Genetically Engineered Machines (iGEM) 2018 distribution. Most notably, SYNBICT is more automated and parallelizable than manual design editors, and it can be applied to interpret existing designs instead of only generating new ones.
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Affiliation(s)
- Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jeanet Mante
- Department of Biomedical 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
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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10
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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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11
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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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12
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Mante J, Hao Y, Jett J, Joshi U, Keating K, Lu X, Nakum G, Rodriguez NE, Tang J, Terry L, Wu X, Yu E, Downie JS, McInnes BT, Nguyen MH, Sepulvado B, Young EM, Myers CJ. Synthetic Biology Knowledge System. ACS Synth Biol 2021; 10:2276-2285. [PMID: 34387462 DOI: 10.1021/acssynbio.1c00188] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Yikai Hao
- University of California San Diego, La Jolla, California 92093, United States
| | - Jacob Jett
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Udayan Joshi
- University of California San Diego, La Jolla, California 92093, United States
| | - Kevin Keating
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Xiang Lu
- University of California San Diego, La Jolla, California 92093, United States
| | - Gaurav Nakum
- University of California San Diego, La Jolla, California 92093, United States
| | | | - Jiawei Tang
- University of California San Diego, La Jolla, California 92093, United States
| | - Logan Terry
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Xuanyu Wu
- University of California San Diego, La Jolla, California 92093, United States
| | - Eric Yu
- University of Utah, Salt Lake City, Utah 84112, United States
| | - J. Stephen Downie
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Bridget T. McInnes
- Virginia Commonwealth University, Richmond, Virginia 23284, United States
| | - Mai H. Nguyen
- University of California San Diego, La Jolla, California 92093, United States
| | - Brandon Sepulvado
- NORC at the University of Chicago Bethesda, Chicago, Illinois 60637, United States
| | - Eric M. Young
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Chris J. Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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13
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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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14
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Hatch B, Meng L, Mante J, McLaughlin JA, Scott-Brown J, Myers CJ. VisBOL2-Improving Web-Based Visualization for Synthetic Biology Designs. ACS Synth Biol 2021; 10:2111-2115. [PMID: 34324811 DOI: 10.1021/acssynbio.1c00147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
VisBOL is a web-based visualization tool used to depict genetic circuit designs. This tool depicts simple DNA circuits adequately, but it has become increasingly outdated as new versions of SBOL Visual were released. This paper introduces VisBOL2, a heavily redesigned version of VisBOL that makes a number of improvements to the original VisBOL, including proper functional interaction rendering, dynamic viewing, a more maintainable code base, and modularity that facilitates compatibility with other software tools. This modularity is demonstrated by incorporating VisBOL2 into a sequence visualization plugin for SynBioHub.
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Affiliation(s)
- Benjamin Hatch
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Linhao Meng
- Eindhoven University of Technology, Eindhoven, 5612 AZ, Netherlands
| | - Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | | | - Chris J. Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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15
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Moškon M, Komac R, Zimic N, Mraz M. Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05711-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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16
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McCarthy J. Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae. Integr Biol (Camb) 2021; 13:210-220. [PMID: 34270725 DOI: 10.1093/intbio/zyab013] [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: 04/27/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/14/2022]
Abstract
This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.
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Affiliation(s)
- John McCarthy
- Warwick Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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17
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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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18
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Pedone E, de Cesare I, Zamora-Chimal CG, Haener D, Postiglione L, La Regina A, Shannon B, Savery NJ, Grierson CS, di Bernardo M, Gorochowski TE, Marucci L. Cheetah: A Computational Toolkit for Cybergenetic Control. ACS Synth Biol 2021; 10:979-989. [PMID: 33904719 DOI: 10.1021/acssynbio.0c00463] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.
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Affiliation(s)
- Elisa Pedone
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD Bristol, United Kingdom
| | - Irene de Cesare
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
| | - Criseida G. Zamora-Chimal
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
| | - David Haener
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
| | - Lorena Postiglione
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
| | - Antonella La Regina
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD Bristol, United Kingdom
| | - Barbara Shannon
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD Bristol, United Kingdom
| | - Nigel J. Savery
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD Bristol, United Kingdom
| | - Claire S. Grierson
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
- School of Biological Sciences, University of Bristol, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
| | - Mario di Bernardo
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
- Department of EE and ICT, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
| | - Thomas E. Gorochowski
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
- School of Biological Sciences, University of Bristol, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD Bristol, United Kingdom
- BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ Bristol, United Kingdom
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19
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Vipin D, Ignatova Z, Gorochowski TE. Characterizing Genetic Parts and Devices Using RNA Sequencing. Methods Mol Biol 2021; 2229:175-187. [PMID: 33405222 DOI: 10.1007/978-1-0716-1032-9_8] [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] [Indexed: 06/12/2023]
Abstract
Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.
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Affiliation(s)
- Deepti Vipin
- Institute for Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, Hamburg, Germany
| | - Zoya Ignatova
- Institute for Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, Hamburg, Germany
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20
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Lee M, Woo HM. A Logic NAND Gate for Controlling Gene Expression in a Circadian Rhythm in Cyanobacteria. ACS Synth Biol 2020; 9:3210-3216. [PMID: 33263998 DOI: 10.1021/acssynbio.0c00455] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To enable circadian control of gene expression in cyanobacteria, we constructed a genetic logic gate (NAND) using orthogonal promoters via modular CRISPR interference. The NAND gates were tested in Synechococcus elongatus PCC 7942 using a fluorescent reporter. The NAND gate dynamics were characterized based on the affinity of the dCas9 complex to the output element. Upon connecting tight gene repressions with the circadian promoter (the purF gene; peak expression at dawn), inversed peak expressions were obtained as an output of the NAND gate although the retroactivities were shown in the ON and OFF states. A dark-responsive genetic element of the NAND gate was also expanded to an AND gate in S. elongatus PCC 7942. These cyanobacterial NAND and AND gates could facilitate the control of gene expressions in dynamic metabolic engineering technologies, thereby enabling the cyanobacteria to serve as biosolar cell factories.
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Affiliation(s)
- Mieun Lee
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Han Min Woo
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- BioFoundry Research Center, Institute of Biotechnology and Bioengineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
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21
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22
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McLaughlin JA, Beal J, Mısırlı G, Grünberg R, Bartley BA, Scott-Brown J, Vaidyanathan P, Fontanarrosa P, Oberortner E, Wipat A, Gorochowski TE, Myers CJ. The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering. Front Bioeng Biotechnol 2020; 8:1009. [PMID: 33015004 PMCID: PMC7516281 DOI: 10.3389/fbioe.2020.01009] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/31/2020] [Indexed: 12/17/2022] Open
Abstract
The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.
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Affiliation(s)
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, United States
| | - Göksel Mısırlı
- School of Mathematics and Computing, Keele University, Keele, United Kingdom
| | - Raik Grünberg
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | | | - James Scott-Brown
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Pedro Fontanarrosa
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Ernst Oberortner
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | | | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO, United States
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23
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Barbier I, Perez‐Carrasco R, Schaerli Y. Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch. Mol Syst Biol 2020; 16:e9361. [PMID: 32529808 PMCID: PMC7290156 DOI: 10.15252/msb.20199361] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 04/29/2020] [Accepted: 05/08/2020] [Indexed: 11/20/2022] Open
Abstract
The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit.
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Affiliation(s)
- Içvara Barbier
- Department of Fundamental MicrobiologyUniversity of LausanneLausanneSwitzerland
| | - Rubén Perez‐Carrasco
- Department of Life SciencesImperial College LondonSouth Kensington CampusLondonUK
- Department of MathematicsUniversity College LondonLondonUK
| | - Yolanda Schaerli
- Department of Fundamental MicrobiologyUniversity of LausanneLausanneSwitzerland
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24
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Abstract
Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, Staffordshire ST5 5BG, U.K
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | | | | | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, U.K
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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25
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Martínez-García E, Goñi-Moreno A, Bartley B, McLaughlin J, Sánchez-Sampedro L, Pascual del Pozo H, Prieto Hernández C, Marletta AS, De Lucrezia D, Sánchez-Fernández G, Fraile S, de Lorenzo V. SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts. Nucleic Acids Res 2020; 48:D1164-D1170. [PMID: 31740968 PMCID: PMC7018797 DOI: 10.1093/nar/gkz1024] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/17/2019] [Accepted: 10/18/2019] [Indexed: 11/29/2022] Open
Abstract
The Standard European Vector Architecture 3.0 database (SEVA-DB 3.0, http://seva.cnb.csic.es) is the update of the platform launched in 2013 both as a web-based resource and as a material repository of formatted genetic tools (mostly plasmids) for analysis, construction and deployment of complex bacterial phenotypes. The period between the first version of SEVA-DB and the present time has witnessed several technical, computational and conceptual advances in genetic/genomic engineering of prokaryotes that have enabled upgrading of the utilities of the updated database. Novelties include not only a more user-friendly web interface and many more plasmid vectors, but also new links of the plasmids to advanced bioinformatic tools. These provide an intuitive visualization of the constructs at stake and a range of virtual manipulations of DNA segments that were not possible before. Finally, the list of canonical SEVA plasmids is available in machine-readable SBOL (Synthetic Biology Open Language) format. This ensures interoperability with other platforms and affords simulations of their behaviour under different in vivo conditions. We argue that the SEVA-DB will remain a useful resource for extending Synthetic Biology approaches towards non-standard bacterial species as well as genetically programming new prokaryotic chassis for a suite of fundamental and biotechnological endeavours.
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Affiliation(s)
- Esteban Martínez-García
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de la Universidad Autónoma de Madrid, 28049 Spain
| | | | | | | | | | | | | | | | | | | | - Sofía Fraile
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de la Universidad Autónoma de Madrid, 28049 Spain
| | - Víctor de Lorenzo
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de la Universidad Autónoma de Madrid, 28049 Spain
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