1
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Roehner N, Roberts J, Lapets A, Gould D, Akavoor V, Qin L, Gordon DB, Voigt C, Densmore D. GOLDBAR: A Framework for Combinatorial Biological Design. ACS Synth Biol 2024; 13:2899-2911. [PMID: 39162314 DOI: 10.1021/acssynbio.4c00296] [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: 08/21/2024]
Abstract
With the rise of new DNA part libraries and technologies for assembling DNA, synthetic biologists are increasingly constructing and screening combinatorial libraries to optimize their biological designs. As combinatorial libraries are used to generate data on design performance, new rules for composing biological designs will emerge. Most formal frameworks for combinatorial design, however, do not yet support formal comparison of design composition, which is needed to facilitate automated analysis and machine learning in massive biological design spaces. To address this need, we introduce a combinatorial design framework called GOLDBAR. Compared with existing frameworks, GOLDBAR enables synthetic biologists to intersect and merge the rules for entire classes of biological designs to extract common design motifs and infer new ones. Here, we demonstrate the application of GOLDBAR to refine/validate design spaces for TetR-homologue transcriptional logic circuits, verify the assembly of a partial nif gene cluster, and infer novel gene clusters for the biosynthesis of rebeccamycin. We also discuss how GOLDBAR could be used to facilitate grammar-based machine learning in synthetic biology.
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Affiliation(s)
- Nicholas Roehner
- RTX BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - James Roberts
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | | | - Dany Gould
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - Vidya Akavoor
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - Lucy Qin
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - D Benjamin Gordon
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher Voigt
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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2
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Castle SD, Stock M, Gorochowski TE. Engineering is evolution: a perspective on design processes to engineer biology. Nat Commun 2024; 15:3640. [PMID: 38684714 PMCID: PMC11059173 DOI: 10.1038/s41467-024-48000-1] [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: 09/11/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.
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Affiliation(s)
- Simeon D Castle
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK.
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3
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Moreno-Paz S, van der Hoek R, Eliana E, Zwartjens P, Gosiewska S, Martins dos Santos VAP, Schmitz J, Suarez-Diez M. Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast. ACS Synth Biol 2024; 13:1312-1322. [PMID: 38545878 PMCID: PMC11036487 DOI: 10.1021/acssynbio.4c00035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 04/20/2024]
Abstract
Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.
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Affiliation(s)
- Sara Moreno-Paz
- Laboratory
of Systems and Synthetic Biology, Wageningen
University & Research, 6708 WE Wageningen, The Netherlands
| | - Rianne van der Hoek
- Department
of Science and Research, dsm-firmenich,
Science & Research, 2600 MA Delft, The
Netherlands
| | - Elif Eliana
- Laboratory
of Systems and Synthetic Biology, Wageningen
University & Research, 6708 WE Wageningen, The Netherlands
| | - Priscilla Zwartjens
- Department
of Science and Research, dsm-firmenich,
Science & Research, 2600 MA Delft, The
Netherlands
| | - Silvia Gosiewska
- Department
of Science and Research, dsm-firmenich,
Science & Research, 2600 MA Delft, The
Netherlands
| | | | - Joep Schmitz
- Department
of Science and Research, dsm-firmenich,
Science & Research, 2600 MA Delft, The
Netherlands
| | - Maria Suarez-Diez
- Laboratory
of Systems and Synthetic Biology, Wageningen
University & Research, 6708 WE Wageningen, The Netherlands
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4
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Rondthaler S, Sarker B, Howitz N, Shah I, Andrews LB. Toolbox of Characterized Genetic Parts for Staphylococcus aureus. ACS Synth Biol 2024; 13:103-118. [PMID: 38064657 PMCID: PMC10805105 DOI: 10.1021/acssynbio.3c00325] [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: 05/24/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 01/23/2024]
Abstract
Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.
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Affiliation(s)
- Stephen
N. Rondthaler
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Nathaniel Howitz
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Ishita Shah
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
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5
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Lebovich M, Andrews LB. Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917. Front Bioeng Biotechnol 2022; 10:938056. [PMID: 36091463 PMCID: PMC9452892 DOI: 10.3389/fbioe.2022.938056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 11/25/2022] Open
Abstract
Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. A major challenge impeding the implementation of this strategy has been the difficulty to engineer the necessary whole-cell biosensors. Creation of transcription factor-based biosensors in a clinically-relevant strain often requires significant tuning of the genetic parts and gene expression to achieve the dynamic range and sensitivity required. Here, we propose an approach to efficiently engineer transcription-factor based metabolite biosensors that uses a design prototyping construct to quickly assay the gene expression design space and identify an optimal genetic design. We demonstrate this approach using the probiotic bacterium Escherichia coli Nissle 1917 (EcN) and two neuroactive gut metabolites: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. The EcN propionate sensor, utilizing the PrpR transcriptional activator from E. coli, has a large 59-fold dynamic range and >500-fold increased sensitivity that matches biologically-relevant concentrations. Our EcN GABA biosensor uses the GabR transcriptional repressor from Bacillus subtilis and a synthetic GabR-regulated promoter created in this study. This work reports the first known synthetic microbial whole-cell biosensor for GABA, which has an observed 138-fold activation in EcN at biologically-relevant concentrations. Using this rapid design prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a large dynamic range and high output promoter activity upon activation. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics.
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Affiliation(s)
- Matthew Lebovich
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, MA, United States
| | - Lauren B. Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, MA, United States
- Molecular and Cellular Biology Graduate, Program University of Massachusetts Amherst, Amherst, MA, United States
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6
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Jones TS, Oliveira SMD, Myers CJ, Voigt CA, Densmore D. Genetic circuit design automation with Cello 2.0. Nat Protoc 2022; 17:1097-1113. [PMID: 35197606 DOI: 10.1038/s41596-021-00675-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 11/29/2021] [Indexed: 11/09/2022]
Abstract
Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.
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Affiliation(s)
- Timothy S Jones
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Samuel M D Oliveira
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Chris J Myers
- Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA. .,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
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7
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James JS, Jones S, Martella A, Luo Y, Fisher DI, Cai Y. Automation and Expansion of EMMA Assembly for Fast-Tracking Mammalian System Engineering. ACS Synth Biol 2022; 11:587-595. [PMID: 35061373 DOI: 10.1021/acssynbio.1c00330] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With applications from functional genomics to the production of therapeutic biologics, libraries of mammalian expression vectors have become a cornerstone of modern biological investigation and engineering. Multiple modular vector platforms facilitate the rapid design and assembly of vectors. However, such systems approach a technical bottleneck when a library of bespoke vectors is required. Utilizing the flexibility and robustness of the Extensible Mammalian Modular Assembly (EMMA) toolkit, we present an automated workflow for the library-scale design, assembly, and verification of mammalian expression vectors. Vector design is simplified using our EMMA computer-aided design tool (EMMA-CAD), while the precision and speed of acoustic droplet ejection technology are applied in vector assembly. Our pipeline facilitates significant reductions in both reagent usage and researcher hands-on time compared with manual assembly, as shown by system Q-metrics. To demonstrate automated EMMA performance, we compiled a library of 48 distinct plasmid vectors encoding either CRISPR interference or activation modalities. Characterization of the workflow parameters shows that high assembly efficiency is maintained across vectors of various sizes and design complexities. Our system also performs strongly compared with manual assembly efficiency benchmarks. Alongside our automated pipeline, we present a straightforward strategy for integrating gRNA and Cas modules into the EMMA platform, enabling the design and manufacture of valuable genome editing resources.
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Affiliation(s)
- Joshua S James
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
| | - Sally Jones
- John Innes Centre, Norwich Research Park, Norwich, Norfolk NR4 7UH, U.K
| | - Andrea Martella
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yisha Luo
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - David I Fisher
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yizhi Cai
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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8
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Casas A, Bultelle M, Motraghi C, Kitney R. Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology. Front Bioeng Biotechnol 2022; 9:785131. [PMID: 35083201 PMCID: PMC8784771 DOI: 10.3389/fbioe.2021.785131] [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: 09/28/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022] Open
Abstract
We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).
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Affiliation(s)
- Alexis Casas
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Matthieu Bultelle
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Charles Motraghi
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Richard Kitney
- Department of Bioengineering, Imperial College London, London, United Kingdom
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9
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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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10
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Mısırlı G, Yang B, James K, Wipat A. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synth Biol 2021; 10:3304-3315. [PMID: 34762797 DOI: 10.1021/acssynbio.1c00157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, ST5 5BG, U.K
| | - Bill Yang
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, U.K
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
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11
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Abstract
Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.
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Affiliation(s)
- Mo R. Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, A809B Scaife Hall, 3550 Terrace Street, Pittsburgh, PA 15261, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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12
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Harnessing the central dogma for stringent multi-level control of gene expression. Nat Commun 2021; 12:1738. [PMID: 33741937 PMCID: PMC7979795 DOI: 10.1038/s41467-021-21995-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 02/18/2021] [Indexed: 11/17/2022] Open
Abstract
Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided, but without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple solution to this challenge. By simultaneously regulating transcription and translation, we show how basal expression of an inducible system can be reduced, with little impact on the maximum expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction and show how multi-level regulation can suppress transcriptional noise and create digital-like switches between ‘on’ and ‘off’ states. These tools will aid those working with toxic genes or requiring precise regulation and propagation of cellular signals, plus illustrate the value of more diverse regulatory designs for synthetic biology. Inducible gene expression systems should minimise leaky output and offer a large achievable range of expression. Here, the authors regulate transcription and translation together to suppress noise and create digital-like responses, while maintaining a large expression range in vivo and in vitro.
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13
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Gilman J, Walls L, Bandiera L, Menolascina F. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol 2021; 10:1-18. [PMID: 33406821 DOI: 10.1021/acssynbio.0c00385] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.
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Affiliation(s)
- James Gilman
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Laura Walls
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Lucia Bandiera
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Filippo Menolascina
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
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14
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Horns F, Quake SR. Cloning antibodies from single cells in pooled sequence libraries by selective PCR. PLoS One 2020; 15:e0236477. [PMID: 32756607 PMCID: PMC7406036 DOI: 10.1371/journal.pone.0236477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/06/2020] [Indexed: 11/19/2022] Open
Abstract
Antibodies function by binding to antigens. Antibodies must be cloned and expressed to determine their binding characteristics, but current methods for high-throughput antibody sequencing yield antibody DNA pooled from many cells and do not readily permit cloning of antibodies from single B cells. We present a strategy for retrieving and cloning antibody DNA from single cells within a pooled library of cells. Our strategy, called selective PCR for antibody retrieval (SPAR), takes advantage of the unique sequence barcodes attached to individual cDNA molecules during sample preparation to enable specific amplification by PCR of antibody heavy- and light-chain cDNA originating from a single cell. We show through computational analysis that most human antibodies sequenced using typical high-throughput methods can be retrieved using SPAR, and experimentally demonstrate retrieval of full-length antibody variable region cDNA from three cells within pools of ~5,000 cells. SPAR enables rapid low-cost cloning and expression of native human antibodies from pooled single-cell sequence libraries for functional characterization.
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Affiliation(s)
- Felix Horns
- Biophysics Graduate Program, Stanford University, Stanford, California, United States of America
| | - Stephen R. Quake
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Applied Physics, Stanford University, Stanford, California, United States of America
- Chan Zuckerberg Biohub, Stanford, California, United States of America
- * E-mail:
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15
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Tellechea-Luzardo J, Winterhalter C, Widera P, Kozyra J, de Lorenzo V, Krasnogor N. Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering. ACS Synth Biol 2020; 9:536-545. [PMID: 32078768 DOI: 10.1021/acssynbio.9b00400] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
As DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biological engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnology and digital technology, a deficit in software and laboratory techniques diminishes the ability to make biotechnology more agile, reproducible, and transparent while, at the same time, limiting the security and safety of synthetic biology constructs. To partially address some of these problems, this paper presents an approach for physically linking engineered cells to their digital footprint-we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialized version control system for collaborative strain engineering via simple barcoding protocols.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, Newcastle University, Newcastle Upon Tyne NE4 5TG, U.K
| | - Charles Winterhalter
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, Newcastle University, Newcastle Upon Tyne NE4 5TG, U.K
| | - Paweł Widera
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, Newcastle University, Newcastle Upon Tyne NE4 5TG, U.K
| | - Jerzy Kozyra
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, Newcastle University, Newcastle Upon Tyne NE4 5TG, U.K
| | - Víctor de Lorenzo
- Systems and Synthetic Biology Program, Centro Nacional de Biotecnología (CNB-CSIC), 28049 Madrid, Spain
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, Newcastle University, Newcastle Upon Tyne NE4 5TG, U.K
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16
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Gam JJ, DiAndreth B, Jones RD, Huh J, Weiss R. A 'poly-transfection' method for rapid, one-pot characterization and optimization of genetic systems. Nucleic Acids Res 2019; 47:e106. [PMID: 31372658 PMCID: PMC6765116 DOI: 10.1093/nar/gkz623] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 06/24/2019] [Accepted: 07/29/2019] [Indexed: 01/19/2023] Open
Abstract
Biological research is relying on increasingly complex genetic systems and circuits to perform sophisticated operations in living cells. Performing these operations often requires simultaneous delivery of many genes, and optimizing the stoichiometry of these genes can yield drastic improvements in performance. However, sufficiently sampling the large design space of gene expression stoichiometries in mammalian cells using current methods is cumbersome, complex, or expensive. We present a ‘poly-transfection’ method as a simple yet high-throughput alternative that enables comprehensive evaluation of genetic systems in a single, readily-prepared transfection sample. Each cell in a poly-transfection represents an independent measurement at a distinct gene expression stoichiometry, fully leveraging the single-cell nature of transfection experiments. We first benchmark poly-transfection against co-transfection, showing that titration curves for commonly-used regulators agree between the two methods. We then use poly-transfections to efficiently generate new insights, for example in CRISPRa and synthetic miRNA systems. Finally, we use poly-transfection to rapidly engineer a difficult-to-optimize miRNA-based cell classifier for discriminating cancerous cells. One-pot evaluation enabled by poly-transfection accelerates and simplifies the design of genetic systems, providing a new high-information strategy for interrogating biology.
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Affiliation(s)
- Jeremy J Gam
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.,Synthetic Biology Center, MIT, Cambridge, MA 02139, USA
| | - Breanna DiAndreth
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.,Synthetic Biology Center, MIT, Cambridge, MA 02139, USA
| | - Ross D Jones
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.,Synthetic Biology Center, MIT, Cambridge, MA 02139, USA
| | - Jin Huh
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.,Synthetic Biology Center, MIT, Cambridge, MA 02139, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.,Synthetic Biology Center, MIT, Cambridge, MA 02139, USA
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17
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Ranu N, Villani AC, Hacohen N, Blainey PC. Targeting individual cells by barcode in pooled sequence libraries. Nucleic Acids Res 2019; 47:e4. [PMID: 30256981 PMCID: PMC6326790 DOI: 10.1093/nar/gky856] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 09/12/2018] [Indexed: 01/02/2023] Open
Abstract
Transcriptional profiling of thousands of single cells in parallel by RNA-seq is now routine. However, due to reliance on pooled library preparation, targeting analysis to particular cells of interest is difficult. Here, we present a multiplexed PCR method for targeted sequencing of select cells from pooled single-cell sequence libraries. We demonstrated this molecular enrichment method on multiple cell types within pooled single-cell RNA-seq libraries produced from primary human blood cells. We show how molecular enrichment can be combined with FACS to efficiently target ultra-rare cell types, such as the recently identified AXL+SIGLEC6+ dendritic cell (AS DC) subset, in order to reduce the required sequencing effort to profile single cells by 100-fold. Our results demonstrate that DNA barcodes identifying cells within pooled sequencing libraries can be used as targets to enrich for specific molecules of interest, for example reads from a set of target cells.
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Affiliation(s)
- Navpreet Ranu
- Department of Biological Engineering, Massachusetts Institute of Technology, MA, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul C Blainey
- Department of Biological Engineering, Massachusetts Institute of Technology, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
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18
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Lin D, O'Callaghan CA. MetClo: methylase-assisted hierarchical DNA assembly using a single type IIS restriction enzyme. Nucleic Acids Res 2019; 46:e113. [PMID: 29986052 PMCID: PMC6212791 DOI: 10.1093/nar/gky596] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 06/27/2018] [Indexed: 01/05/2023] Open
Abstract
Efficient DNA assembly is of great value in biological research and biotechnology. Type IIS restriction enzyme-based assembly systems allow assembly of multiple DNA fragments in a one-pot reaction. However, large DNA fragments can only be assembled by alternating use of two or more type IIS restriction enzymes in a multi-step approach. Here, we present MetClo, a DNA assembly method that uses only a single type IIS restriction enzyme for hierarchical DNA assembly. The method is based on in vivo methylation-mediated on/off switching of type IIS restriction enzyme recognition sites that overlap with site-specific methylase recognition sequences. We have developed practical MetClo systems for the type IIS enzymes BsaI, BpiI and LguI, and demonstrated hierarchical assembly of large DNA fragments up to 218 kb. The MetClo approach substantially reduces the need to remove internal restriction sites from components to be assembled. The use of a single type IIS enzyme throughout the different stages of DNA assembly allows novel and powerful design schemes for rapid large-scale hierarchical DNA assembly. The BsaI-based MetClo system is backward-compatible with component libraries of most of the existing type IIS restriction enzyme-based assembly systems, and has potential to become a standard for modular DNA assembly.
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Affiliation(s)
- Da Lin
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Christopher A O'Callaghan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
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19
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Roehner N, Bartley B, Beal J, McLaughlin J, Pocock M, Zhang M, Zundel Z, Myers CJ. Specifying Combinatorial Designs with the Synthetic Biology Open Language (SBOL). ACS Synth Biol 2019; 8:1519-1523. [PMID: 31260271 DOI: 10.1021/acssynbio.9b00092] [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: 11/29/2022]
Abstract
As improvements in DNA synthesis technology and assembly methods make combinatorial assembly of genetic constructs increasingly accessible, methods for representing genetic constructs likewise need to improve to handle the exponential growth of combinatorial design space. To this end, we present a community accepted extension of the SBOL data standard that allows for the efficient and flexible encoding of combinatorial designs. This extension includes data structures for representing genetic designs with "variable" components that can be implemented by choosing one of many linked designs for existing genetic parts or constructs. We demonstrate the representational power of the SBOL combinatorial design extension through case studies on metabolic pathway design and genetic circuit design, and we report the expansion of the SBOLDesigner software tool to support users in creating and modifying combinatorial designs in SBOL.
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Affiliation(s)
- Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | | | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Tyne and Wear, NE27 0RT, UK
| | - Michael Zhang
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Chris J. Myers
- University of Utah, Salt Lake City, Utah 84112, United States
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20
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Lebedev MO, Yarinich LA, Ivankin AV, Pindyurin AV. Generation of barcoded plasmid libraries for massively parallel analysis of chromatin position effects. Vavilovskii Zhurnal Genet Selektsii 2019. [DOI: 10.18699/vj19.483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The discovery of the position effect variegation phenomenon and the subsequent comprehensive analysis of its molecular mechanisms led to understanding that the local chromatin composition has a dramatic effect on gene activity. To study this effect in a high-throughput mode and at the genome-wide level, the Thousands of Reporters Integrated in Parallel (TRIP) approach based on the usage of barcoded reporter gene constructs was recently developed. Here we describe the construction and quality checks of high-diversity barcoded plasmid libraries supposed to be used for high-throughput analysis of chromatin position effects in Drosophila cells. First, we highlight the critical parameters that should be considered in the generation of barcoded plasmid libraries and introduce a simple method to assess the diversity of random sequences (barcodes) of synthetic oligonucleotides using PCR amplification followed by Sanger sequencing. Second, we compare the conventional restriction-ligation method with the Gibson assembly approach for cloning barcodes into the same plasmid vector. Third, we provide optimized parameters for the construction of barcoded plasmid libraries, such as the vector : insert ratio in the Gibson assembly reaction and the voltage used for electroporation of bacterial cells with ligation products. We also compare different approaches to check the quality of barcoded plasmid libraries. Finally, we briefly describe alternative approaches that can be used for the generation of such libraries. Importantly, all improvements and modifications of the techniques described here can be applied to a wide range of experiments involving barcoded plasmid libraries.
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Affiliation(s)
- M. O. Lebedev
- Institute of Molecular and Cellular Biology, SB RAS; Novosibirsk State University
| | - L. A. Yarinich
- Institute of Molecular and Cellular Biology, SB RAS; Novosibirsk State University
| | | | - A. V. Pindyurin
- Institute of Molecular and Cellular Biology, SB RAS; Novosibirsk State University
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21
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Nora LC, Westmann CA, Martins‐Santana L, Alves LDF, Monteiro LMO, Guazzaroni M, Silva‐Rocha R. The art of vector engineering: towards the construction of next-generation genetic tools. Microb Biotechnol 2019; 12:125-147. [PMID: 30259693 PMCID: PMC6302727 DOI: 10.1111/1751-7915.13318] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 08/29/2018] [Accepted: 08/31/2018] [Indexed: 12/20/2022] Open
Abstract
When recombinant DNA technology was developed more than 40 years ago, no one could have imagined the impact it would have on both society and the scientific community. In the field of genetic engineering, the most important tool developed was the plasmid vector. This technology has been continuously expanding and undergoing adaptations. Here, we provide a detailed view following the evolution of vectors built throughout the years destined to study microorganisms and their peculiarities, including those whose genomes can only be revealed through metagenomics. We remark how synthetic biology became a turning point in designing these genetic tools to create meaningful innovations. We have placed special focus on the tools for engineering bacteria and fungi (both yeast and filamentous fungi) and those available to construct metagenomic libraries. Based on this overview, future goals would include the development of modular vectors bearing standardized parts and orthogonally designed circuits, a task not fully addressed thus far. Finally, we present some challenges that should be overcome to enable the next generation of vector design and ways to address it.
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Affiliation(s)
- Luísa Czamanski Nora
- Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
| | - Cauã Antunes Westmann
- Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
| | | | - Luana de Fátima Alves
- Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
- School of Philosophy, Science and Letters of Ribeirão PretoUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
| | | | - María‐Eugenia Guazzaroni
- School of Philosophy, Science and Letters of Ribeirão PretoUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
| | - Rafael Silva‐Rocha
- Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão Preto, São Paulo14049‐900Brazil
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22
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Andrews LB, Nielsen AAK, Voigt CA. Cellular checkpoint control using programmable sequential logic. Science 2018; 361:361/6408/eaap8987. [PMID: 30237327 DOI: 10.1126/science.aap8987] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 08/03/2018] [Indexed: 12/15/2022]
Abstract
Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.
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Affiliation(s)
- Lauren B Andrews
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alec A K Nielsen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher A Voigt
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. .,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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23
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Xiang Y, Dalchau N, Wang B. Scaling up genetic circuit design for cellular computing: advances and prospects. NATURAL COMPUTING 2018; 17:833-853. [PMID: 30524216 PMCID: PMC6244767 DOI: 10.1007/s11047-018-9715-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.
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Affiliation(s)
- Yiyu Xiang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
| | | | - Baojun Wang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
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24
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Ortiz L, Pavan M, McCarthy L, Timmons J, Densmore DM. Automated Robotic Liquid Handling Assembly of Modular DNA Devices. J Vis Exp 2017. [PMID: 29286379 PMCID: PMC5755516 DOI: 10.3791/54703] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Recent advances in modular DNA assembly techniques have enabled synthetic biologists to test significantly more of the available "design space" represented by "devices" created as combinations of individual genetic components. However, manual assembly of such large numbers of devices is time-intensive, error-prone, and costly. The increasing sophistication and scale of synthetic biology research necessitates an efficient, reproducible way to accommodate large-scale, complex, and high throughput device construction. Here, a DNA assembly protocol using the Type-IIS restriction endonuclease based Modular Cloning (MoClo) technique is automated on two liquid-handling robotic platforms. Automated liquid-handling robots require careful, often times tedious optimization of pipetting parameters for liquids of different viscosities (e.g. enzymes, DNA, water, buffers), as well as explicit programming to ensure correct aspiration and dispensing of DNA parts and reagents. This makes manual script writing for complex assemblies just as problematic as manual DNA assembly, and necessitates a software tool that can automate script generation. To this end, we have developed a web-based software tool, http://mocloassembly.com, for generating combinatorial DNA device libraries from basic DNA parts uploaded as Genbank files. We provide access to the tool, and an export file from our liquid handler software which includes optimized liquid classes, labware parameters, and deck layout. All DNA parts used are available through Addgene, and their digital maps can be accessed via the Boston University BDC ICE Registry. Together, these elements provide a foundation for other organizations to automate modular cloning experiments and similar protocols. The automated DNA assembly workflow presented here enables the repeatable, automated, high-throughput production of DNA devices, and reduces the risk of human error arising from repetitive manual pipetting. Sequencing data show the automated DNA assembly reactions generated from this workflow are ~95% correct and require as little as 4% as much hands-on time, compared to manual reaction preparation.
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Affiliation(s)
- Luis Ortiz
- Graduate Program in Molecular Biology, Cell Biology, and Biochemistry, Boston University; Biological Design Center, Boston University
| | | | | | | | - Douglas M Densmore
- Department of Electrical and Computer Engineering, Biological Design Center, Boston University;
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25
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A standard-enabled workflow for synthetic biology. Biochem Soc Trans 2017; 45:793-803. [PMID: 28620041 DOI: 10.1042/bst20160347] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/29/2017] [Accepted: 03/31/2017] [Indexed: 11/17/2022]
Abstract
A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.
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26
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Chao R, Mishra S, Si T, Zhao H. Engineering biological systems using automated biofoundries. Metab Eng 2017; 42:98-108. [PMID: 28602523 PMCID: PMC5544601 DOI: 10.1016/j.ymben.2017.06.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022]
Abstract
Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.
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Affiliation(s)
- Ran Chao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Shekhar Mishra
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tong Si
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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