1
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Sinzger-D'Angelo M, Hanst M, Reinhardt F, Koeppl H. Effects of mRNA conformational switching on translational noise in gene circuits. J Chem Phys 2024; 160:134108. [PMID: 38573847 DOI: 10.1063/5.0186927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.
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Affiliation(s)
| | - Maleen Hanst
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Felix Reinhardt
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Heinz Koeppl
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
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2
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. NPJ Syst Biol Appl 2024; 10:35. [PMID: 38565850 PMCID: PMC10987498 DOI: 10.1038/s41540-024-00361-5] [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: 11/21/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, Baltimore, Baltimore, MD, USA.
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3
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550573. [PMID: 37546866 PMCID: PMC10402059 DOI: 10.1101/2023.07.26.550573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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4
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Obayashi N, Junge K, Ilić S, Hughes J. Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes. Sci Rep 2023; 13:4212. [PMID: 36918733 PMCID: PMC10015042 DOI: 10.1038/s41598-023-31395-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/10/2023] [Indexed: 03/16/2023] Open
Abstract
Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance.
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Affiliation(s)
- Nana Obayashi
- CREATE Lab - Computational Robot Design & Fabrication Lab, EPFL, 1015, Lausanne, Switzerland.
| | - Kai Junge
- CREATE Lab - Computational Robot Design & Fabrication Lab, EPFL, 1015, Lausanne, Switzerland
| | - Stefan Ilić
- CREATE Lab - Computational Robot Design & Fabrication Lab, EPFL, 1015, Lausanne, Switzerland
| | - Josie Hughes
- CREATE Lab - Computational Robot Design & Fabrication Lab, EPFL, 1015, Lausanne, Switzerland
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5
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Gupta D, Sharma G, Saraswat P, Ranjan R. Synthetic Biology in Plants, a Boon for Coming Decades. Mol Biotechnol 2021; 63:1138-1154. [PMID: 34420149 DOI: 10.1007/s12033-021-00386-9] [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] [Received: 03/27/2021] [Accepted: 08/16/2021] [Indexed: 02/01/2023]
Abstract
Recently an enormous expansion of knowledge is seen in various disciplines of science. This surge of information has given rise to concept of interdisciplinary fields, which has resulted in emergence of newer research domains, one of them is 'Synthetic Biology' (SynBio). It captures basics from core biology and integrates it with concepts from the other areas of study such as chemical, electrical, and computational sciences. The essence of synthetic biology is to rewire, re-program, and re-create natural biological pathways, which are carried through genetic circuits. A genetic circuit is a functional assembly of basic biological entities (DNA, RNA, proteins), created using typical design, built, and test cycles. These circuits allow scientists to engineer nearly all biological systems for various useful purposes. The development of sophisticated molecular tools, techniques, genomic programs, and ease of nucleic acid synthesis have further fueled several innovative application of synthetic biology in areas like molecular medicines, pharmaceuticals, biofuels, drug discovery, metabolomics, developing plant biosensors, utilization of prokaryotic systems for metabolite production, and CRISPR/Cas9 in the crop improvement. These applications have largely been dominated by utilization of prokaryotic systems. However, newer researches have indicated positive growth of SynBio for the eukaryotic systems as well. This paper explores advances of synthetic biology in the plant field by elaborating on its core components and potential applications. Here, we have given a comprehensive idea of designing, development, and utilization of synthetic biology in the improvement of the present research state of plant system.
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Affiliation(s)
- Dipinte Gupta
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Gauri Sharma
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Pooja Saraswat
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Rajiv Ranjan
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India.
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6
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David F, Davis AM, Gossing M, Hayes MA, Romero E, Scott LH, Wigglesworth MJ. A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities. SLAS DISCOVERY 2021; 26:581-603. [PMID: 33834873 DOI: 10.1177/24725552211000669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.
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Affiliation(s)
- Florian David
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Andrew M Davis
- Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Cambridge, UK
| | - Michael Gossing
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Martin A Hayes
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Elvira Romero
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Louis H Scott
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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7
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Karkaria BD, Fedorec AJH, Barnes CP. Automated design of synthetic microbial communities. Nat Commun 2021; 12:672. [PMID: 33510148 PMCID: PMC7844305 DOI: 10.1038/s41467-020-20756-2] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/10/2020] [Indexed: 12/16/2022] Open
Abstract
Microbial species rarely exist in isolation. In naturally occurring microbial systems there is strong evidence for a positive relationship between species diversity and productivity of communities. The pervasiveness of these communities in nature highlights possible advantages for genetically engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially as functional complexity increases. Here, we demonstrate a methodology for designing robust synthetic communities that include competition for nutrients, and use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We computationally explore all two- and three- strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and further tuning the community composition.
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Affiliation(s)
- Behzad D Karkaria
- Department of Cell & Developmental Biology, University College London, London, WC1E 6BT, UK
| | - Alex J H Fedorec
- Department of Cell & Developmental Biology, University College London, London, WC1E 6BT, UK
| | - Chris P Barnes
- Department of Cell & Developmental Biology, University College London, London, WC1E 6BT, UK.
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK.
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8
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Van Brempt M, Clauwaert J, Mey F, Stock M, Maertens J, Waegeman W, De Mey M. Predictive design of sigma factor-specific promoters. Nat Commun 2020; 11:5822. [PMID: 33199691 PMCID: PMC7670410 DOI: 10.1038/s41467-020-19446-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023] Open
Abstract
To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.
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Affiliation(s)
- Maarten Van Brempt
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000, Ghent, Belgium
| | - Jim Clauwaert
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium
| | - Friederike Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000, Ghent, Belgium
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium
| | - Jo Maertens
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000, Ghent, Belgium
| | - Willem Waegeman
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000, Ghent, Belgium.
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9
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Gene networks that compensate for crosstalk with crosstalk. Nat Commun 2019; 10:4028. [PMID: 31492904 PMCID: PMC6731275 DOI: 10.1038/s41467-019-12021-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 08/12/2019] [Indexed: 12/18/2022] Open
Abstract
Crosstalk is a major challenge to engineering sophisticated synthetic gene networks. A common approach is to insulate signal-transduction pathways by minimizing molecular-level crosstalk between endogenous and synthetic genetic components, but this strategy can be difficult to apply in the context of complex, natural gene networks and unknown interactions. Here, we show that synthetic gene networks can be engineered to compensate for crosstalk by integrating pathway signals, rather than by pathway insulation. We demonstrate this principle using reactive oxygen species (ROS)-responsive gene circuits in Escherichia coli that exhibit concentration-dependent crosstalk with non-cognate ROS. We quantitatively map the degree of crosstalk and design gene circuits that introduce compensatory crosstalk at the gene network level. The resulting gene network exhibits reduced crosstalk in the sensing of the two different ROS. Our results suggest that simple network motifs that compensate for pathway crosstalk can be used by biological networks to accurately interpret environmental signals. Crosstalk between genetic circuits is a major challenge for engineering sophisticated networks. Here the authors design networks that compensate for crosstalk by integrating, not insulating, pathways.
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10
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Gomez‐Marquez J, Hamad‐Schifferli K. Distributed Biological Foundries for Global Health. Adv Healthc Mater 2019; 8:e1900184. [PMID: 31420954 DOI: 10.1002/adhm.201900184] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/24/2019] [Indexed: 01/25/2023]
Abstract
Historically, many industries such as manufacturing have undergone a trend away from centralized, large-scale production toward a more distributed form. Currently, this same trend is witnessed in biological manufacturing and bioprocessing, with the rise of biological foundries where one can synthesize, grow, isolate, and purify a broad range of biologics. The adoption of distributed practices for biological processing has significant implications for healthcare, diagnostics, and therapies. This essay discusses the many diverse factors that have facilitated this growth, ranging from the establishment of available biological components, or "parts," low-cost programmable hardware, and others. Currently existing examples of distributed biological foundries are also identified, separating the discussion into those that are accessible only by elite users and the more recent emerging foundries that are more accessible to the general population. Taking lessons from other fields, it is argued that this trend toward distributed biological manufacturing is inevitable, so adapting to this trend is important for the progress of creating new therapeutics, sensors, diagnostics, and reagents for biomedical applications.
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Affiliation(s)
- Jose Gomez‐Marquez
- Little Devices LabMassachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA
| | - Kimberly Hamad‐Schifferli
- Department of EngineeringSchool for the EnvironmentUniversity of Massachusetts Boston 100 Morrissey Blvd. Boston MA 02125 USA
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11
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Jeong D, Klocke M, Agarwal S, Kim J, Choi S, Franco E, Kim J. Cell-Free Synthetic Biology Platform for Engineering Synthetic Biological Circuits and Systems. Methods Protoc 2019; 2:E39. [PMID: 31164618 PMCID: PMC6632179 DOI: 10.3390/mps2020039] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/12/2019] [Accepted: 05/08/2019] [Indexed: 01/07/2023] Open
Abstract
Synthetic biology brings engineering disciplines to create novel biological systems for biomedical and technological applications. The substantial growth of the synthetic biology field in the past decade is poised to transform biotechnology and medicine. To streamline design processes and facilitate debugging of complex synthetic circuits, cell-free synthetic biology approaches has reached broad research communities both in academia and industry. By recapitulating gene expression systems in vitro, cell-free expression systems offer flexibility to explore beyond the confines of living cells and allow networking of synthetic and natural systems. Here, we review the capabilities of the current cell-free platforms, focusing on nucleic acid-based molecular programs and circuit construction. We survey the recent developments including cell-free transcription-translation platforms, DNA nanostructures and circuits, and novel classes of riboregulators. The links to mathematical models and the prospects of cell-free synthetic biology platforms will also be discussed.
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Affiliation(s)
- Dohyun Jeong
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Melissa Klocke
- Department of Mechanical Engineering, University of California at Riverside, 900 University Ave, Riverside, CA 92521, USA.
| | - Siddharth Agarwal
- Department of Mechanical Engineering, University of California at Riverside, 900 University Ave, Riverside, CA 92521, USA.
| | - Jeongwon Kim
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Seungdo Choi
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Elisa Franco
- Department of Mechanical and Aerospace Engineering, University of California at Los Angeles, 420 Westwood Plaza, Los Angeles, CA 90095, USA.
| | - Jongmin Kim
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
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12
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Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design. Processes (Basel) 2019. [DOI: 10.3390/pr7010052] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, and that the choice of growth rates, sampling schedule, and input profile each play an important role. The proposed approach to coupled component–host modelling can support gene circuit design across a range of physiological conditions.
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13
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Abstract
The semiconductor revolution that began in the 20th century has transformed society. Key to this revolution has been the integrated circuit, which enabled exponential scaling of computing devices using silicon-based transistors over many decades. Analogously, decreasing costs in DNA sequencing and synthesis, along with the development of robust genetic circuits, are enabling a "biocomputing revolution". First-generation gene circuits largely relied on assembling various transcriptional regulatory elements to execute digital and analog computing functions in living cells. Basic design rules and computational tools have since been derived so that such circuits can be scaled in order to implement complex computations. In the past five years, great strides have been made in expanding the biological programming toolkit to include recombinase- and CRISPR-based gene circuits that execute complex cellular logic and memory. Recent advances have enabled increasingly dense computing and memory circuits to function in living cells while expanding the application of these circuits from bacteria to eukaryotes, including human cells, for a wide range of uses.
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Affiliation(s)
- Kevin Yehl
- Synthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Research Laboratory of Electronics, MIT, Cambridge, MA 02139, USA
| | - Timothy Lu
- Synthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Research Laboratory of Electronics, MIT, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA
- Department of Biological Engineering, MIT, Cambridge, MA 02129, USA
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14
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Decoene T, De Paepe B, Maertens J, Coussement P, Peters G, De Maeseneire SL, De Mey M. Standardization in synthetic biology: an engineering discipline coming of age. Crit Rev Biotechnol 2017; 38:647-656. [PMID: 28954542 DOI: 10.1080/07388551.2017.1380600] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Leaping DNA read-and-write technologies, and extensive automation and miniaturization are radically transforming the field of biological experimentation by providing the tools that enable the cost-effective high-throughput required to address the enormous complexity of biological systems. However, standardization of the synthetic biology workflow has not kept abreast with dwindling technical and resource constraints, leading, for example, to the collection of multi-level and multi-omics large data sets that end up disconnected or remain under- or even unexploited. PURPOSE In this contribution, we critically evaluate the various efforts, and the (limited) success thereof, in order to introduce standards for defining, designing, assembling, characterizing, and sharing synthetic biology parts. The causes for this success or the lack thereof, as well as possible solutions to overcome these, are discussed. CONCLUSION Akin to other engineering disciplines, extensive standardization will undoubtedly speed-up and reduce the cost of bioprocess development. In this respect, further implementation of synthetic biology standards will be crucial for the field in order to redeem its promise, i.e. to enable predictable forward engineering.
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Affiliation(s)
- Thomas Decoene
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Brecht De Paepe
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Jo Maertens
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | | | - Gert Peters
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
| | - Sofie L De Maeseneire
- b InBio.be, Centre for Industrial Biotechnology and Biocatalysis, Ghent University , Ghent , Belgium
| | - Marjan De Mey
- a Centre for Synthetic Biology, Ghent University , Ghent , Belgium
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15
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Otero-Muras I, Banga JR. Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality. ACS Synth Biol 2017; 6:1180-1193. [PMID: 28350462 DOI: 10.1021/acssynbio.6b00306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
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16
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Appleton E, Madsen C, Roehner N, Densmore D. Design Automation in Synthetic Biology. Cold Spring Harb Perspect Biol 2017; 9:a023978. [PMID: 28246188 PMCID: PMC5378053 DOI: 10.1101/cshperspect.a023978] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.
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Affiliation(s)
- Evan Appleton
- Department of Genetics, Harvard Medical School, Harvard University, Boston, Massachusetts 02115
| | - Curtis Madsen
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Nicholas Roehner
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, Massachusetts 02215
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
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17
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Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat Biotechnol 2017; 35:453-462. [PMID: 28346402 PMCID: PMC5423837 DOI: 10.1038/nbt.3805] [Citation(s) in RCA: 159] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 01/27/2017] [Indexed: 01/03/2023]
Abstract
Genetic circuits engineered for mammalian cells often require extensive fine-tuning to perform their intended functions. To overcome this problem, we present a generalizable biocomputing platform that can engineer genetic circuits which function in human cells with minimal optimization. We used our Boolean Logic and Arithmetic through DNA Excision (BLADE) platform to build more than 100 multi-input-multi-output circuits. We devised a quantitative metric to evaluate the performance of the circuits in human embryonic kidney and Jurkat T cells. Of 113 circuits analysed, 109 functioned (96.5%) with the correct specified behavior without any optimization. We used our platform to build a three-input, two-output Full Adder and six-input, one-output Boolean Logic Look Up Table. We also used BLADE to design circuits with temporal small molecule-mediated inducible control and circuits that incorporate CRISPR/Cas9 to regulate endogenous mammalian genes.
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Mohammadi P, Beerenwinkel N, Benenson Y. Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy. Cell Syst 2017; 4:207-218.e14. [DOI: 10.1016/j.cels.2017.01.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 10/25/2016] [Accepted: 01/06/2017] [Indexed: 10/20/2022]
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Otero-Muras I, Henriques D, Banga JR. SYNBADm: a tool for optimization-based automated design of synthetic gene circuits. Bioinformatics 2016; 32:3360-3362. [PMID: 27402908 DOI: 10.1093/bioinformatics/btw415] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 06/23/2016] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The design of de novo circuits with predefined performance specifications is a challenging problem in Synthetic Biology. Computational models and tools have proved to be crucial for a successful wet lab implementation. Natural gene circuits are complex, subject to evolutionary tradeoffs and playing multiple roles. However, most synthetic designs implemented to date are simple and perform a single task. As the field progresses, advanced computational tools are needed in order to handle greater levels of circuit complexity in a more flexible way and considering multiple design criteria. RESULTS This works presents SYNBADm (SYNthetic Biology Automated optimal Design in Matlab), a software toolbox for the automatic optimal design of gene circuits with targeted functions from libraries of components. This tool makes use of global optimization to simultaneously search the space of structures and kinetic parameters. SYNBADm can efficiently handle high levels of circuit complexity and can consider multiple design criteria through multiobjective optimization. Further, it provides flexible design capabilities, i.e. the user can define the modeling framework, library of components and target performance function(s). AVAILABILITY AND IMPLEMENTATION SYNBADm runs under the popular MATLAB computational environment, and is available under GPLv3 license at https://sites.google.com/site/synbadm CONTACT: ireneotero@iim.csic.es or julio@iim.csic.es.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain
| | - David Henriques
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain
| | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain
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20
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Nielsen AAK, Der BS, Shin J, Vaidyanathan P, Paralanov V, Strychalski EA, Ross D, Densmore D, Voigt CA. Genetic circuit design automation. Science 2016; 352:aac7341. [PMID: 27034378 DOI: 10.1126/science.aac7341] [Citation(s) in RCA: 574] [Impact Index Per Article: 71.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 01/21/2016] [Indexed: 12/12/2022]
Abstract
Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.
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Affiliation(s)
- Alec A K Nielsen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Bryan S Der
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Biological Design Center, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Jonghyeon Shin
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Prashant Vaidyanathan
- Biological Design Center, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Vanya Paralanov
- Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20817, USA
| | - Elizabeth A Strychalski
- Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20817, USA
| | - David Ross
- Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20817, USA
| | - Douglas Densmore
- Biological Design Center, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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21
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Van Hove B, Love AM, Ajikumar PK, De Mey M. Programming Biology: Expanding the Toolset for the Engineering of Transcription. Synth Biol (Oxf) 2016. [DOI: 10.1007/978-3-319-22708-5_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Sadowski MI, Grant C, Fell TS. Harnessing QbD, Programming Languages, and Automation for Reproducible Biology. Trends Biotechnol 2015; 34:214-227. [PMID: 26708960 DOI: 10.1016/j.tibtech.2015.11.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/16/2015] [Accepted: 11/19/2015] [Indexed: 12/18/2022]
Abstract
Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.
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Affiliation(s)
- Michael I Sadowski
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Chris Grant
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Tim S Fell
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK.
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23
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Qi H, Li BZ, Zhang WQ, Liu D, Yuan YJ. Modularization of genetic elements promotes synthetic metabolic engineering. Biotechnol Adv 2015; 33:1412-9. [DOI: 10.1016/j.biotechadv.2015.04.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Revised: 01/12/2015] [Accepted: 04/05/2015] [Indexed: 01/24/2023]
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Peters G, Coussement P, Maertens J, Lammertyn J, De Mey M. Putting RNA to work: Translating RNA fundamentals into biotechnological engineering practice. Biotechnol Adv 2015; 33:1829-44. [PMID: 26514597 DOI: 10.1016/j.biotechadv.2015.10.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 10/13/2015] [Accepted: 10/22/2015] [Indexed: 11/19/2022]
Abstract
Synthetic biology, in close concert with systems biology, is revolutionizing the field of metabolic engineering by providing novel tools and technologies to rationally, in a standardized way, reroute metabolism with a view to optimally converting renewable resources into a broad range of bio-products, bio-materials and bio-energy. Increasingly, these novel synthetic biology tools are exploiting the extensive programmable nature of RNA, vis-à-vis DNA- and protein-based devices, to rationally design standardized, composable, and orthogonal parts, which can be scaled and tuned promptly and at will. This review gives an extensive overview of the recently developed parts and tools for i) modulating gene expression ii) building genetic circuits iii) detecting molecules, iv) reporting cellular processes and v) building RNA nanostructures. These parts and tools are becoming necessary armamentarium for contemporary metabolic engineering. Furthermore, the design criteria, technological challenges, and recent metabolic engineering success stories of the use of RNA devices are highlighted. Finally, the future trends in transforming metabolism through RNA engineering are critically evaluated and summarized.
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Affiliation(s)
- Gert Peters
- Centre of Expertise Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Pieter Coussement
- Centre of Expertise Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Jo Maertens
- Centre of Expertise Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Jeroen Lammertyn
- BIOSYST-MeBioS, KU Leuven, Willem de Croylaan 42, 3001 Louvain, Belgium
| | - Marjan De Mey
- Centre of Expertise Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium.
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25
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Huynh L, Tagkopoulos I. Fast and Accurate Circuit Design Automation through Hierarchical Model Switching. ACS Synth Biol 2015; 4:890-7. [PMID: 25916918 DOI: 10.1021/sb500339k] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In computer-aided biological design, the trifecta of characterized part libraries, accurate models and optimal design parameters is crucial for producing reliable designs. As the number of parts and model complexity increase, however, it becomes exponentially more difficult for any optimization method to search the solution space, hence creating a trade-off that hampers efficient design. To address this issue, we present a hierarchical computer-aided design architecture that uses a two-step approach for biological design. First, a simple model of low computational complexity is used to predict circuit behavior and assess candidate circuit branches through branch-and-bound methods. Then, a complex, nonlinear circuit model is used for a fine-grained search of the reduced solution space, thus achieving more accurate results. Evaluation with a benchmark of 11 circuits and a library of 102 experimental designs with known characterization parameters demonstrates a speed-up of 3 orders of magnitude when compared to other design methods that provide optimality guarantees.
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Affiliation(s)
- Linh Huynh
- Department of Computer Science & UC Davis Genome Center, University of California Davis, Davis, California 95616, United States
| | - Ilias Tagkopoulos
- Department of Computer Science & UC Davis Genome Center, University of California Davis, Davis, California 95616, United States
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26
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Ball P. Forging patterns and making waves from biology to geology: a commentary on Turing (1952) 'The chemical basis of morphogenesis'. Philos Trans R Soc Lond B Biol Sci 2015; 373:rsta.2014.0218. [PMID: 25750229 DOI: 10.1098/rsta.2014.0218] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/17/2015] [Indexed: 05/21/2023] Open
Abstract
Alan Turing was neither a biologist nor a chemist, and yet the paper he published in 1952, 'The chemical basis of morphogenesis', on the spontaneous formation of patterns in systems undergoing reaction and diffusion of their ingredients has had a substantial impact on both fields, as well as in other areas as disparate as geomorphology and criminology. Motivated by the question of how a spherical embryo becomes a decidedly non-spherical organism such as a human being, Turing devised a mathematical model that explained how random fluctuations can drive the emergence of pattern and structure from initial uniformity. The spontaneous appearance of pattern and form in a system far away from its equilibrium state occurs in many types of natural process, and in some artificial ones too. It is often driven by very general mechanisms, of which Turing's model supplies one of the most versatile. For that reason, these patterns show striking similarities in systems that seem superficially to share nothing in common, such as the stripes of sand ripples and of pigmentation on a zebra skin. New examples of 'Turing patterns' in biology and beyond are still being discovered today. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.
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Affiliation(s)
- Philip Ball
- 18 Hillcourt Road, East Dulwich, London SE22 0PE, UK
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27
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Eckdahl TT, Campbell AM, Heyer LJ, Poet JL, Blauch DN, Snyder NL, Atchley DT, Baker EJ, Brown M, Brunner EC, Callen SA, Campbell JS, Carr CJ, Carr DR, Chadinha SA, Chester GI, Chester J, Clarkson BR, Cochran KE, Doherty SE, Doyle C, Dwyer S, Edlin LM, Evans RA, Fluharty T, Frederick J, Galeota-Sprung J, Gammon BL, Grieshaber B, Gronniger J, Gutteridge K, Henningsen J, Isom B, Itell HL, Keffeler EC, Lantz AJ, Lim JN, McGuire EP, Moore AK, Morton J, Nakano M, Pearson SA, Perkins V, Parrish P, Pierson CE, Polpityaarachchige S, Quaney MJ, Slattery A, Smith KE, Spell J, Spencer M, Taye T, Trueblood K, Vrana CJ, Whitesides ET. Programmed evolution for optimization of orthogonal metabolic output in bacteria. PLoS One 2015; 10:e0118322. [PMID: 25714374 PMCID: PMC4340930 DOI: 10.1371/journal.pone.0118322] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 01/13/2015] [Indexed: 11/18/2022] Open
Abstract
Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields - evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy, pharmaceuticals, chemical commodities, biomining, and bioremediation.
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Affiliation(s)
- Todd T. Eckdahl
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
- * E-mail:
| | - A. Malcolm Campbell
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Laurie J. Heyer
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Jeffrey L. Poet
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - David N. Blauch
- Department of Chemistry, Davidson College, Davidson, North Carolina, United States of America
| | - Nicole L. Snyder
- Department of Chemistry, Davidson College, Davidson, North Carolina, United States of America
| | - Dustin T. Atchley
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Erich J. Baker
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Micah Brown
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Elizabeth C. Brunner
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Sean A. Callen
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Jesse S. Campbell
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Caleb J. Carr
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - David R. Carr
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Spencer A. Chadinha
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Grace I. Chester
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Josh Chester
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Ben R. Clarkson
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Kelly E. Cochran
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Shannon E. Doherty
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Catherine Doyle
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Sarah Dwyer
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Linnea M. Edlin
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Rebecca A. Evans
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Taylor Fluharty
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Janna Frederick
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Jonah Galeota-Sprung
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Betsy L. Gammon
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Brandon Grieshaber
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Jessica Gronniger
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Katelyn Gutteridge
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Joel Henningsen
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Bradley Isom
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Hannah L. Itell
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Erica C. Keffeler
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Andrew J. Lantz
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Jonathan N. Lim
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Erin P. McGuire
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Alexander K. Moore
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Jerrad Morton
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Meredith Nakano
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Sara A. Pearson
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Virginia Perkins
- Department of Computer Science, Math and Physics, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Phoebe Parrish
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Claire E. Pierson
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Sachith Polpityaarachchige
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Michael J. Quaney
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Abagael Slattery
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Kathryn E. Smith
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Jackson Spell
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Morgan Spencer
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
| | - Telavive Taye
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - Kamay Trueblood
- Department of Biology, Missouri Western State University, Saint Joseph, Missouri, United States of America
| | - Caroline J. Vrana
- Department of Biology, Davidson College, Davidson, North Carolina, United States of America
| | - E. Tucker Whitesides
- Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, United States of America
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Ding Y, Wu F, Tan C. Synthetic Biology: A Bridge between Artificial and Natural Cells. Life (Basel) 2014; 4:1092-116. [PMID: 25532531 PMCID: PMC4284483 DOI: 10.3390/life4041092] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/02/2014] [Accepted: 12/11/2014] [Indexed: 12/24/2022] Open
Abstract
Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.
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Affiliation(s)
- Yunfeng Ding
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
| | - Fan Wu
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, One Shields Ave., Davis, CA 95616-5270, USA.
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Kelwick R, MacDonald JT, Webb AJ, Freemont P. Developments in the tools and methodologies of synthetic biology. Front Bioeng Biotechnol 2014; 2:60. [PMID: 25505788 PMCID: PMC4244866 DOI: 10.3389/fbioe.2014.00060] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 11/12/2014] [Indexed: 11/27/2022] Open
Abstract
Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a “body of knowledge” from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community.
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Affiliation(s)
- Richard Kelwick
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - James T MacDonald
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - Alexander J Webb
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
| | - Paul Freemont
- Centre for Synthetic Biology and Innovation, Imperial College London , London , UK ; Department of Medicine, Imperial College London , London , UK
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30
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Marchisio MA. Parts & pools: a framework for modular design of synthetic gene circuits. Front Bioeng Biotechnol 2014; 2:42. [PMID: 25340051 PMCID: PMC4186347 DOI: 10.3389/fbioe.2014.00042] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 09/16/2014] [Indexed: 01/27/2023] Open
Abstract
Published in 2008, Parts & Pools represents one of the first attempts to conceptualize the modular design of bacterial synthetic gene circuits with Standard Biological Parts (DNA segments) and Pools of molecules referred to as common signal carriers (e.g., RNA polymerases and ribosomes). The original framework for modeling bacterial components and designing prokaryotic circuits evolved over the last years and brought, first, to the development of an algorithm for the automatic design of Boolean gene circuits. This is a remarkable achievement since gene digital circuits have a broad range of applications that goes from biosensors for health and environment care to computational devices. More recently, Parts & Pools was enabled to give a proper formal description of eukaryotic biological circuit components. This was possible by employing a rule-based modeling approach, a technique that permits a faithful calculation of all the species and reactions involved in complex systems such as eukaryotic cells and compartments. In this way, Parts & Pools is currently suitable for the visual and modular design of synthetic gene circuits in yeast and mammalian cells too.
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Brophy JAN, Voigt CA. Principles of genetic circuit design. Nat Methods 2014; 11:508-20. [PMID: 24781324 DOI: 10.1038/nmeth.2926] [Citation(s) in RCA: 568] [Impact Index Per Article: 56.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 03/18/2014] [Indexed: 12/17/2022]
Abstract
Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.
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Affiliation(s)
- Jennifer A N Brophy
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Moskon M, Mraz M. Systematic Approach to Computational Design of Gene Regulatory Networks with Information Processing Capabilities. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:431-440. [PMID: 26355789 DOI: 10.1109/tcbb.2013.2295792] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We present several measures that can be used in de novo computational design of biological systems with information processing capabilities. Their main purpose is to objectively evaluate the behavior and identify the biological information processing structures with the best dynamical properties. They can be used to define constraints that allow one to simplify the design of more complex biological systems. These measures can be applied to existent computational design approaches in synthetic biology, i.e., rational and automatic design approaches. We demonstrate their use on a) the computational models of several basic information processing structures implemented with gene regulatory networks and b) on a modular design of a synchronous toggle switch.
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