1
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Lu Z, Shen Q, Bandari NC, Evans S, McDonnell L, Liu L, Jin W, Luna-Flores CH, Collier T, Talbo G, McCubbin T, Esquirol L, Myers C, Trau M, Dumsday G, Speight R, Howard CB, Vickers CE, Peng B. LowTempGAL: a highly responsive low temperature-inducible GAL system in Saccharomyces cerevisiae. Nucleic Acids Res 2024; 52:7367-7383. [PMID: 38808673 PMCID: PMC11229376 DOI: 10.1093/nar/gkae460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 05/12/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024] Open
Abstract
Temperature is an important control factor for biologics biomanufacturing in precision fermentation. Here, we explored a highly responsive low temperature-inducible genetic system (LowTempGAL) in the model yeast Saccharomyces cerevisiae. Two temperature biosensors, a heat-inducible degron and a heat-inducible protein aggregation domain, were used to regulate the GAL activator Gal4p, rendering the leaky LowTempGAL systems. Boolean-type induction was achieved by implementing a second-layer control through low-temperature-mediated repression on GAL repressor gene GAL80, but suffered delayed response to low-temperature triggers and a weak response at 30°C. Application potentials were validated for protein and small molecule production. Proteomics analysis suggested that residual Gal80p and Gal4p insufficiency caused suboptimal induction. 'Turbo' mechanisms were engineered through incorporating a basal Gal4p expression and a galactose-independent Gal80p-supressing Gal3p mutant (Gal3Cp). Varying Gal3Cp configurations, we deployed the LowTempGAL systems capable for a rapid stringent high-level induction upon the shift from a high temperature (37-33°C) to a low temperature (≤30°C). Overall, we present a synthetic biology procedure that leverages 'leaky' biosensors to deploy highly responsive Boolean-type genetic circuits. The key lies in optimisation of the intricate layout of the multi-factor system. The LowTempGAL systems may be applicable in non-conventional yeast platforms for precision biomanufacturing.
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Affiliation(s)
- Zeyu Lu
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Qianyi Shen
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Naga Chandra Bandari
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Samuel Evans
- ARC Centre of Excellence in Synthetic Biology, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Liam McDonnell
- ARC Centre of Excellence in Synthetic Biology, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Lian Liu
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- The Queensland Node of Metabolomics Australia and Proteomics Australia (Q-MAP), Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Wanli Jin
- Institute for Molecular Bioscience (IMB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Carlos Horacio Luna-Flores
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Thomas Collier
- ARC Centre of Excellence in Synthetic Biology, Australia
- School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Gert Talbo
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- The Queensland Node of Metabolomics Australia and Proteomics Australia (Q-MAP), Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Tim McCubbin
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Lygie Esquirol
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- Environment, Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT 2601, Australia
| | - Chris Myers
- Department of Electrical, Computer, and Energy Engineering University of Colorado, Boulder, CO 80309, USA
| | - Matt Trau
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Chemistry and Molecular Biosciences (SCMB), the University of Queensland, Brisbane, QLD 4072, Australia
| | - Geoff Dumsday
- Manufacturing, Commonwealth Scientific and Industrial Research Organisation, Clayton, VIC, 3169, Australia
| | - Robert Speight
- ARC Centre of Excellence in Synthetic Biology, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
- Advanced Engineering Biology Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain, ACT, 2601, Australia
| | - Christopher B Howard
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Claudia E Vickers
- ARC Centre of Excellence in Synthetic Biology, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Bingyin Peng
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
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2
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Zilberzwige-Tal S, Fontanarrosa P, Bychenko D, Dorfan Y, Gazit E, Myers CJ. Investigating and Modeling the Factors That Affect Genetic Circuit Performance. ACS Synth Biol 2023; 12:3189-3204. [PMID: 37916512 PMCID: PMC10661042 DOI: 10.1021/acssynbio.3c00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Indexed: 11/03/2023]
Abstract
Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit's performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.
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Affiliation(s)
- Shai Zilberzwige-Tal
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Pedro Fontanarrosa
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Darya Bychenko
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Yuval Dorfan
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
- Bio-engineering,
Electrical Engineering Faculty, Holon Institute
of Technology (HIT), Holon 5810201, Israel
- Alagene
Ltd., Innovation Center, Reichman University, Herzliya 7670608, Israel
| | - Ehud Gazit
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Chris J. Myers
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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3
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Sents Z, Stoughton TE, Buecherl L, Thomas PJ, Fontanarrosa P, Myers CJ. SynBioSuite: A Tool for Improving the Workflow for Genetic Design and Modeling. ACS Synth Biol 2023; 12:892-897. [PMID: 36888740 DOI: 10.1021/acssynbio.2c00597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Synthetic biology research has led to the development of many software tools for designing, constructing, editing, simulating, and sharing genetic parts and circuits. Among these tools are SBOLCanvas, iBioSim, and SynBioHub, which can be used in conjunction to create a genetic circuit design following the design-build-test-learn process. However, although automation works within these tools, most of these software tools are not integrated, and the process of transferring information between them is a very manual, error-prone process. To address this problem, this work automates some of these processes and presents SynBioSuite, a cloud-based tool that eliminates many of the drawbacks of the current approach by automating the setup and reception of results for simulating a designed genetic circuit via an application programming interface.
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Affiliation(s)
- Zachary Sents
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Thomas E Stoughton
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Payton J Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Pedro Fontanarrosa
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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4
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Ahmadi M, Thomas PJ, Buecherl L, Winstead C, Myers CJ, Zheng H. A Comparison of Weighted Stochastic Simulation Methods for the Analysis of Genetic Circuits. ACS Synth Biol 2023; 12:287-304. [PMID: 36583529 DOI: 10.1021/acssynbio.2c00553] [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: 12/31/2022]
Abstract
Rare events are of particular interest in synthetic biology because rare biochemical events may be catastrophic to a biological system by, for example, triggering irreversible events such as off-target drug delivery. To estimate the probability of rare events efficiently, several weighted stochastic simulation methods have been developed. Under optimal parameters and model conditions, these methods can greatly improve simulation efficiency in comparison to traditional stochastic simulation. Unfortunately, the optimal parameters and conditions cannot be deduced a priori. This paper presents a critical survey of weighted stochastic simulation methods. It shows that the methods considered here cannot consistently, efficiently, and exactly accomplish the task of rare event simulation without resorting to a computationally expensive calibration procedure, which undermines their overall efficiency. The results suggest that further development is needed before these methods can be deployed for general use in biological simulations.
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Affiliation(s)
- Mohammad Ahmadi
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida33620-9951, United States
| | - Payton J Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah84112, United States
| | - Lukas Buecherl
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado80309-0401, United States
| | - Chris Winstead
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah84322-1400, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado80309-0401, United States
| | - Hao Zheng
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida33620-9951, United States
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5
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Lee TA, Steel H. Cybergenetic control of microbial community composition. Front Bioeng Biotechnol 2022; 10:957140. [PMID: 36277404 PMCID: PMC9582452 DOI: 10.3389/fbioe.2022.957140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.
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6
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Yu W, Jin K, Wu Y, Zhang Q, Liu Y, Li J, Du G, Chen J, Lv X, Ledesma-Amaro R, Liu L. A pathway independent multi-modular ordered control system based on thermosensors and CRISPRi improves bioproduction in Bacillus subtilis. Nucleic Acids Res 2022; 50:6587-6600. [PMID: 35670665 PMCID: PMC9226513 DOI: 10.1093/nar/gkac476] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/18/2022] [Accepted: 05/21/2022] [Indexed: 11/25/2022] Open
Abstract
Dynamic regulation is an effective strategy for control of gene expression in microbial cell factories. In some pathway contexts, several metabolic modules must be controlled in a time dependent or ordered manner to maximize production, while the creation of genetic circuits with ordered regulation capacity still remains a great challenge. In this work, we develop a pathway independent and programmable system that enables multi-modular ordered control of metabolism in Bacillus subtilis. First, a series of thermosensors were created and engineered to expand their thresholds. Then we designed single-input-multi-output circuits for ordered control based on the use of thermosensors with different transition points. Meanwhile, a repression circuit was constructed by combining CRISPRi-based NOT gates. As a proof-of-concept, these genetic circuits were applied for multi-modular ordered control of 2′-fucosyllactose (2′-FL) biosynthesis, resulting in a production of 1839.7 mg/l in shake flask, which is 5.16-times that of the parental strain. In a 5-l bioreactor, the 2′-FL titer reached 28.2 g/l with down-regulation of autolysis. Taken together, this work provides programmable and versatile thermosensitive genetic toolkits for dynamic regulation in B. subtilis and a multi-modular ordered control framework that can be used to improve metabolic modules in other chassis cells and for other compounds.
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Affiliation(s)
- Wenwen Yu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Ke Jin
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Quanwei Zhang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jian Chen
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.,Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
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7
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Buecherl L, Myers CJ. Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Curr Opin Microbiol 2022; 68:102155. [PMID: 35588683 DOI: 10.1016/j.mib.2022.102155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 01/23/2023]
Abstract
Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.
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Affiliation(s)
- Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, 1111 Engineering Drive, Boulder, 80309 CO, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, 425 UCB, Boulder, 80309 CO, United States.
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8
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GAN R, Cabezas MD, Pan M, Zhang H, Hu G, Clark LG, Jewett MC, Nicol R. High-Throughput Regulatory Part Prototyping and Analysis by Cell-Free Protein Synthesis and Droplet Microfluidics. ACS Synth Biol 2022; 11:2108-2120. [PMID: 35549070 DOI: 10.1021/acssynbio.2c00050] [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
Engineering regulatory parts for improved performance in genetic programs has played a pivotal role in the development of the synthetic biology cell programming toolbox. Here, we report the development of a novel high-throughput platform for regulatory part prototyping and analysis that leverages the advantages of engineered DNA libraries, cell-free protein synthesis (CFPS), high-throughput emulsion droplet microfluidics, standard flow sorting adapted to screen droplet reactions, and next-generation sequencing (NGS). With this integrated platform, we screened the activity of millions of genetic parts within hours, followed by NGS retrieval of the improved designs. This in vitro platform is particularly valuable for engineering regulatory parts of nonmodel organisms, where in vivo high-throughput screening methods are not readily available. The platform can be extended to multipart screening of complete genetic programs to optimize yield and stability.
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Affiliation(s)
- Rui GAN
- Broad Institute of MIT and Harvard, Cambridge, 415 Main Street, Cambridge, Massachusetts 02142, United States
| | - Maria D. Cabezas
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
| | - Ming Pan
- Broad Institute of MIT and Harvard, Cambridge, 415 Main Street, Cambridge, Massachusetts 02142, United States
| | - Huaibin Zhang
- Broad Institute of MIT and Harvard, Cambridge, 415 Main Street, Cambridge, Massachusetts 02142, United States
| | - Gang Hu
- Broad Institute of MIT and Harvard, Cambridge, 415 Main Street, Cambridge, Massachusetts 02142, United States
| | - Lauren G. Clark
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
| | - Michael C. Jewett
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
- Interdisciplinary Biological Sciences Program, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
- Chemistry of Life Processes Institute, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
- Center for Synthetic Biology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3120, United States
| | - Robert Nicol
- Broad Institute of MIT and Harvard, Cambridge, 415 Main Street, Cambridge, Massachusetts 02142, United States
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9
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Schladt T, Engelmann N, Kubaczka E, Hochberger C, Koeppl H. Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty. ACS Synth Biol 2021; 10:3316-3329. [PMID: 34807573 PMCID: PMC8689692 DOI: 10.1021/acssynbio.1c00193] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
![]()
Genetic design automation
methods for combinational circuits often
rely on standard algorithms from electronic design automation in their
circuit synthesis and technology mapping. However, those algorithms
are domain-specific and are hence often not directly suitable for
the biological context. In this work we identify aspects of those
algorithms that require domain-adaptation. We first demonstrate that
enumerating structural variants for a given Boolean specification
allows us to find better performing circuits and that stochastic gate
assignment methods need to be properly adjusted in order to find the
best assignment. Second, we present a general circuit scoring scheme
that accounts for the limited accuracy of biological device models
including the variability across cells and show that circuits selected
according to this score exhibit higher robustness with respect to
parametric variations. If gate characteristics in a library are just
given in terms of intervals, we provide means to efficiently propagate
signals through such a circuit and compute corresponding scores. We
demonstrate the novel design approach using the Cello gate library
and 33 logic functions that were synthesized and implemented in vivo
recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply
considering structural variants yielding performance gains of up to
7.9-fold, whereas 22 of them can be improved with gains up to 26-fold
when selecting circuits according to the novel robustness score. We
furthermore report on the synergistic combination of the two proposed
improvements.
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Affiliation(s)
- Tobias Schladt
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Nicolai Engelmann
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Erik Kubaczka
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Christian Hochberger
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Heinz Koeppl
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
- Centre for Synthetic Biology, TU Darmstadt, Darmstadt 64283, Germany
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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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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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12
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Yeung E, Kim J, Yuan Y, Gonçalves J, Murray RM. Data-driven network models for genetic circuits from time-series data with incomplete measurements. J R Soc Interface 2021; 18:20210413. [PMID: 34493091 PMCID: PMC8424335 DOI: 10.1098/rsif.2021.0413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/12/2021] [Indexed: 12/23/2022] Open
Abstract
Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.
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Affiliation(s)
- Enoch Yeung
- Center for Biological Engineering, Biomolecular Science and Engineering Program, Department of Mechanical Engineering, Center for Control, Dynamical Systems, and Computation, University of California, Santa Barbara, CA, USA
| | - Jongmin Kim
- Department of Life Sciences, POSTECH, Pohang, South Korea
| | - Ye Yuan
- School of Artificial Intelligence and Automation, Hua Zhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Jorge Gonçalves
- Systems Biology Research Group, University of Luxembourg, Belvaux, Luxembourg
| | - Richard M. Murray
- Control and Dynamical Systems, California Institute of Technology, Pasadena, CA, USA
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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Espah Borujeni A, Zhang J, Doosthosseini H, Nielsen AAK, Voigt CA. Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage. Nat Commun 2020; 11:5001. [PMID: 33020480 PMCID: PMC7536230 DOI: 10.1038/s41467-020-18630-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/02/2020] [Indexed: 02/06/2023] Open
Abstract
To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.
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Affiliation(s)
- Amin Espah Borujeni
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jing Zhang
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hamid Doosthosseini
- 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
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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