1
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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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2
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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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3
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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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4
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Youn J, Rai N, Tagkopoulos I. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nat Commun 2022; 13:2360. [PMID: 35487919 PMCID: PMC9055065 DOI: 10.1038/s41467-022-29993-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/04/2022] [Indexed: 11/09/2022] Open
Abstract
We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.
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Affiliation(s)
- Jason Youn
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Navneet Rai
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, CA, 95616, USA.
- Genome Center, University of California, Davis, CA, 95616, USA.
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA.
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5
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Jones TS, Oliveira SMD, Myers CJ, Voigt CA, Densmore D. Genetic circuit design automation with Cello 2.0. Nat Protoc 2022; 17:1097-1113. [PMID: 35197606 DOI: 10.1038/s41596-021-00675-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 11/29/2021] [Indexed: 11/09/2022]
Abstract
Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.
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Affiliation(s)
- Timothy S Jones
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Samuel M D Oliveira
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Chris J Myers
- Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA. .,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
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6
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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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7
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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8
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Bhatia SP, Smanski MJ, Voigt CA, Densmore DM. Genetic Design via Combinatorial Constraint Specification. ACS Synth Biol 2017; 6:2130-2135. [PMID: 28874044 DOI: 10.1021/acssynbio.7b00154] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present a formal language for specifying via constraints a "design space" of DNA constructs composed of genetic parts, and an algorithm for automatically and correctly creating a novel representation of the space of satisfying designs. The language is simple, captures a large class of design spaces, and possesses algorithms for common operations on design spaces. The flexibility of this approach is demonstrated using a 16-gene nitrogen fixation pathway and genetic logic circuits.
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Affiliation(s)
- Swapnil P. Bhatia
- Biological
Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Michael J. Smanski
- Department
of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, Minnesota 55108, United States
| | - Christopher A. Voigt
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas M. Densmore
- Biological
Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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9
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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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10
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Yang J, Yu S, Gong B, An N, Alterovitz G. Biobrick chain recommendations for genetic circuit design. Comput Biol Med 2017; 86:31-39. [PMID: 28499216 DOI: 10.1016/j.compbiomed.2017.04.019] [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: 12/19/2016] [Revised: 04/12/2017] [Accepted: 04/29/2017] [Indexed: 10/19/2022]
Abstract
Synthetic biology databases have collected numerous biobricks to accelerate genetic circuit design. However, selecting biobricks is a tough task. Here, we leverage the fact that these manually designed circuits can provide underlying knowledge to support biobrick selection. We propose to design a recommendation system based on the analysis of available genetic circuits, which can narrow down the biobrick selection range and provide candidate biobricks for users to choose. A recommendation strategy based on a Markov model is established to tackle this issue. Furthermore, a biobrick chain recommendation algorithm Sira is proposed that applies a dynamic programming process on a layered state transition graph to obtain the top k recommendation results. In addition, a weighted filtering strategy, WFSira, is proposed to augment the performance of Sira. The experimental results on the Registry of Standard Biological Parts show that Sira outperforms other algorithms significantly for biobrick recommendations, with approximately 30% improvement in terms of recall rate. It is also able to make biobrick chain recommendations. WFSira can further improve the recall rate of Sira by an average of 7.5% for the top 5 recommendations.
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Affiliation(s)
- Jiaoyun Yang
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Song Yu
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Bowen Gong
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Ning An
- Grenotechnology Lab, Hefei University of Technology, Hefei, China.
| | - Gil Alterovitz
- Harvard Medical School, Boston Children's Hospital, MA, USA.
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11
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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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12
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Huynh L, Tagkopoulos I. A Parts Database with Consensus Parameter Estimation for Synthetic Circuit Design. ACS Synth Biol 2016; 5:1412-1420. [PMID: 27454439 DOI: 10.1021/acssynbio.5b00205] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Mathematical modeling and numerical simulation are crucial to support design decisions in synthetic biology. Accurate estimation of parameter values is key, as direct experimental measurements are difficult and time-consuming. Insufficient data, incompatible measurements, and specialized models that lack universal parameters make this task challenging. Here, we have created a database (PAMDB) that integrates data from 135 publications that contain 118 circuits and 165 genetic parts of the bacterium Escherichia coli. We used a succinct, universal model formulation to describe the part behavior in each circuit. We introduce a constrained consensus inference method that was used to infer the value of the model parameters and evaluated its performance through cross-validation in a benchmark of 23 circuits. We discuss these results and summarize the challenges in data integration and parameter inference. This work provides a resource and a methodology that can be used as a point of reference for synthetic circuit modeling.
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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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13
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Goñi-Moreno A, Carcajona M, Kim J, Martínez-García E, Amos M, de Lorenzo V. An Implementation-Focused Bio/Algorithmic Workflow for Synthetic Biology. ACS Synth Biol 2016; 5:1127-1135. [PMID: 27454551 DOI: 10.1021/acssynbio.6b00029] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e., characterization and measurement of synthetic genetic constructs) has received relatively little attention to date, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented, which is supported by information standards and computational methods and focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features are discussed. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as in the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device's kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onward, the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescence units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.
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Affiliation(s)
- Angel Goñi-Moreno
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | - Marta Carcajona
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | - Juhyun Kim
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
| | | | - Martyn Amos
- Informatics
Research Centre, Manchester Metropolitan University, Manchester M1 5GD, United Kingdom
| | - Víctor de Lorenzo
- Systems
Biology Program, Centro Nacional de Biotecnología, Cantoblanco, 28049 Madrid, Spain
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14
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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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15
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Wilson EH, Sagawa S, Weis JW, Schubert MG, Bissell M, Hawthorne B, Reeves CD, Dean J, Platt D. Genotype Specification Language. ACS Synth Biol 2016; 5:471-8. [PMID: 26886161 DOI: 10.1021/acssynbio.5b00194] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We describe here the Genotype Specification Language (GSL), a language that facilitates the rapid design of large and complex DNA constructs used to engineer genomes. The GSL compiler implements a high-level language based on traditional genetic notation, as well as a set of low-level DNA manipulation primitives. The language allows facile incorporation of parts from a library of cloned DNA constructs and from the "natural" library of parts in fully sequenced and annotated genomes. GSL was designed to engage genetic engineers in their native language while providing a framework for higher level abstract tooling. To this end we define four language levels, Level 0 (literal DNA sequence) through Level 3, with increasing abstraction of part selection and construction paths. GSL targets an intermediate language based on DNA slices that translates efficiently into a wide range of final output formats, such as FASTA and GenBank, and includes formats that specify instructions and materials such as oligonucleotide primers to allow the physical construction of the GSL designs by individual strain engineers or an automated DNA assembly core facility.
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Affiliation(s)
- Erin H. Wilson
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Shiori Sagawa
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - James W. Weis
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Max G. Schubert
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Michael Bissell
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Brian Hawthorne
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Christopher D Reeves
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Jed Dean
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
| | - Darren Platt
- Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, California 94608, United States
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16
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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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17
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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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18
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Roehner N, Zhang Z, Nguyen T, Myers CJ. Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language. ACS Synth Biol 2015; 4:873-9. [PMID: 25822671 DOI: 10.1021/sb5003289] [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] [Indexed: 11/30/2022]
Abstract
In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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19
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Beal J. Signal-to-Noise Ratio Measures Efficacy of Biological Computing Devices and Circuits. Front Bioeng Biotechnol 2015; 3:93. [PMID: 26177070 PMCID: PMC4485182 DOI: 10.3389/fbioe.2015.00093] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 06/15/2015] [Indexed: 11/13/2022] Open
Abstract
Engineering biological cells to perform computations has a broad range of important potential applications, including precision medical therapies, biosynthesis process control, and environmental sensing. Implementing predictable and effective computation, however, has been extremely difficult to date, due to a combination of poor composability of available parts and of insufficient characterization of parts and their interactions with the complex environment in which they operate. In this paper, the author argues that this situation can be improved by quantitative signal-to-noise analysis of the relationship between computational abstractions and the variation and uncertainty endemic in biological organisms. This analysis takes the form of a ΔSNRdB function for each computational device, which can be computed from measurements of a device's input/output curve and expression noise. These functions can then be combined to predict how well a circuit will implement an intended computation, as well as evaluating the general suitability of biological devices for engineering computational circuits. Applying signal-to-noise analysis to current repressor libraries shows that no library is currently sufficient for general circuit engineering, but also indicates key targets to remedy this situation and vastly improve the range of computations that can be used effectively in the implementation of biological applications.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
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20
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RiboTALE: A modular, inducible system for accurate gene expression control. Sci Rep 2015; 5:10658. [PMID: 26023068 PMCID: PMC4650599 DOI: 10.1038/srep10658] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2014] [Accepted: 04/20/2015] [Indexed: 01/02/2023] Open
Abstract
A limiting factor in synthetic gene circuit design is the number of independent control elements that can be combined together in a single system. Here, we present RiboTALEs, a new class of inducible repressors that combine the specificity of TALEs with the ability of riboswitches to recognize exogenous signals and differentially control protein abundance. We demonstrate the capacity of RiboTALEs, constructed through different combinations of TALE proteins and riboswitches, to rapidly and reproducibly control the expression of downstream targets with a dynamic range of 243.7 ± 17.6-fold, which is adequate for many biotechnological applications.
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21
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Hsu CY, Pan ZM, Hu RH, Chang CC, Cheng HC, Lin C, Chen BS. Systematic Biological Filter Design with a Desired I/O Filtering Response Based on Promoter-RBS Libraries. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:712-725. [PMID: 26357282 DOI: 10.1109/tcbb.2014.2372790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this study, robust biological filters with an external control to match a desired input/output (I/O) filtering response are engineered based on the well-characterized promoter-RBS libraries and a cascade gene circuit topology. In the field of synthetic biology, the biological filter system serves as a powerful detector or sensor to sense different molecular signals and produces a specific output response only if the concentration of the input molecular signal is higher or lower than a specified threshold. The proposed systematic design method of robust biological filters is summarized into three steps. Firstly, several well-characterized promoter-RBS libraries are established for biological filter design by identifying and collecting the quantitative and qualitative characteristics of their promoter-RBS components via nonlinear parameter estimation method. Then, the topology of synthetic biological filter is decomposed into three cascade gene regulatory modules, and an appropriate promoter-RBS library is selected for each module to achieve the desired I/O specification of a biological filter. Finally, based on the proposed systematic method, a robust externally tunable biological filter is engineered by searching the promoter-RBS component libraries and a control inducer concentration library to achieve the optimal reference match for the specified I/O filtering response.
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22
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Beal J. Bridging the gap: a roadmap to breaking the biological design barrier. Front Bioeng Biotechnol 2015; 2:87. [PMID: 25654077 PMCID: PMC4299508 DOI: 10.3389/fbioe.2014.00087] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 12/21/2014] [Indexed: 12/11/2022] Open
Abstract
This paper presents an analysis of an emerging bottleneck in organism engineering, and paths by which it may be overcome. Recent years have seen the development of a profusion of synthetic biology tools, largely falling into two categories: high-level “design” tools aimed at mapping from organism specifications to nucleic acid sequences implementing those specifications, and low-level “build and test” tools aimed at faster, cheaper, and more reliable fabrication of those sequences and assays of their behavior in engineered biological organisms. Between the two families, however, there is a major gap: we still largely lack the predictive models and component characterization data required to effectively determine which of the many possible candidate sequences considered in the design phase are the most likely to produce useful results when built and tested. As low-level tools continue to mature, the bottleneck in biological systems engineering is shifting to be dominated by design, making this gap a critical barrier to progress. Considering how to address this gap, we find that widespread adoption of readily available analytic and assay methods is likely to lead to rapid improvement in available predictive models and component characterization models, as evidenced by a number of recent results. Such an enabling development is, in turn, likely to allow high-level tools to break the design barrier and support rapid development of transformative biological applications.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies , Cambridge, MA , USA
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23
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Roehner N, Oberortner E, Pocock M, Beal J, Clancy K, Madsen C, Misirli G, Wipat A, Sauro H, Myers CJ. Proposed data model for the next version of the synthetic biology open language. ACS Synth Biol 2015; 4:57-71. [PMID: 24896221 DOI: 10.1021/sb500176h] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.
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Affiliation(s)
- Nicholas Roehner
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States
| | - Ernst Oberortner
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States
| | - Matthew Pocock
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts, United States
| | - Kevin Clancy
- Life Technologies, Carlsbad, California, United States
| | - Curtis Madsen
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Goksel Misirli
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Herbert Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States
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24
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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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25
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Lewis DD, Villarreal FD, Wu F, Tan C. Synthetic biology outside the cell: linking computational tools to cell-free systems. Front Bioeng Biotechnol 2014; 2:66. [PMID: 25538941 PMCID: PMC4260521 DOI: 10.3389/fbioe.2014.00066] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 11/23/2014] [Indexed: 12/22/2022] Open
Abstract
As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.
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Affiliation(s)
- Daniel D. Lewis
- Integrative Genetics and Genomics, University of California Davis, Davis, CA, USA
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
| | | | - Fan Wu
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA
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26
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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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27
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Otero-Muras I, Banga JR. Multicriteria global optimization for biocircuit design. BMC SYSTEMS BIOLOGY 2014; 8:113. [PMID: 25248337 PMCID: PMC4180256 DOI: 10.1186/s12918-014-0113-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 09/16/2014] [Indexed: 11/10/2022]
Abstract
Background One of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. While circuits in Biology are complex and subject to natural tradeoffs, most synthetic circuits are simple in terms of the number of regulatory regions, and have been designed to meet a single design criterion. Results In this contribution we introduce a multiobjective formulation for the design of biocircuits. We set up the basis for an advanced optimization tool for the modular and systematic design of biocircuits capable of handling high levels of complexity and multiple design criteria. Our methodology combines the efficiency of global Mixed Integer Nonlinear Programming solvers with multiobjective optimization techniques. Through a number of examples we show the capability of the method to generate non intuitive designs with a desired functionality setting up a priori the desired level of complexity. Conclusions The methodology presented here can be used for biocircuit design and also to explore and identify different design principles for synthetic gene circuits. The presence of more than one competing objective provides a realistic design setting where every solution represents an optimal trade-off between different criteria. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0113-3) contains supplementary material, which is available to authorized users.
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28
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Huynh L, Tagkopoulos I. Optimal part and module selection for synthetic gene circuit design automation. ACS Synth Biol 2014; 3:556-64. [PMID: 24933033 DOI: 10.1021/sb400139h] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
An integral challenge in synthetic circuit design is the selection of optimal parts to populate a given circuit topology, so that the resulting circuit behavior best approximates the desired one. In some cases, it is also possible to reuse multipart constructs or modules that have been already built and experimentally characterized. Efficient part and module selection algorithms are essential to systematically search the solution space, and their significance will only increase in the following years due to the projected explosion in part libraries and circuit complexity. Here, we address this problem by introducing a structured abstraction methodology and a dynamic programming-based algorithm that guaranties optimal part selection. In addition, we provide three extensions that are based on symmetry check, information look-ahead and branch-and-bound techniques, to reduce the running time and space requirements. We have evaluated the proposed methodology with a benchmark of 11 circuits, a database of 73 parts and 304 experimentally constructed modules with encouraging results. This work represents a fundamental departure from traditional heuristic-based methods for part and module selection and is a step toward maximizing efficiency in synthetic circuit design and construction.
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Affiliation(s)
- Linh Huynh
- Department of Computer Science
and UC Davis Genome Center University of California Davis, California 95616 United States
| | - Ilias Tagkopoulos
- Department of Computer Science
and UC Davis Genome Center University of California Davis, California 95616 United States
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29
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Roehner N, Myers CJ. Directed acyclic graph-based technology mapping of genetic circuit models. ACS Synth Biol 2014; 3:543-55. [PMID: 24650240 DOI: 10.1021/sb400135t] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
As engineering foundations such as standards and abstraction begin to mature within synthetic biology, it is vital that genetic design automation (GDA) tools be developed to enable synthetic biologists to automatically select standardized DNA components from a library to meet the behavioral specification for a genetic circuit. To this end, we have developed a genetic technology mapping algorithm that builds on the directed acyclic graph (DAG) based mapping techniques originally used to select parts for digital electronic circuit designs and implemented it in our GDA tool, iBioSim. It is among the first genetic technology mapping algorithms to adapt techniques from electronic circuit design, in particular the use of a cost function to guide the search for an optimal solution, and perhaps that which makes the greatest use of standards for describing genetic function and structure to represent design specifications and component libraries. This paper demonstrates the use of our algorithm to map the specifications for three different genetic circuits against four randomly generated libraries of increasing size to evaluate its performance against both exhaustive search and greedy variants for finding optimal and near-optimal solutions.
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City 84112, United States
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30
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Carrera J, Estrela R, Luo J, Rai N, Tsoukalas A, Tagkopoulos I. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol 2014; 10:735. [PMID: 24987114 PMCID: PMC4299492 DOI: 10.15252/msb.20145108] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Given the vast behavioral repertoire and biological complexity of even the simplest organisms,
accurately predicting phenotypes in novel environments and unveiling their biological organization
is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a
common framework the various biological processes and their interactions across multiple layers. We
trained this methodology on an extensive normalized compendium for the gram-negative bacterium
Escherichia coli, which incorporates gene expression data for genetic and
environmental perturbations, transcriptional regulation, signal transduction, and metabolic
pathways, as well as growth measurements. Comparison with measured growth and high-throughput data
demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various
environmental and genetic conditions, even in cases where their underlying functions are
under-represented in the training set. This work paves the way toward integrative techniques that
extract knowledge from a variety of biological data to achieve more than the sum of their parts in
the context of prediction, analysis, and redesign of biological systems.
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Affiliation(s)
- Javier Carrera
- UC Davis Genome Center, University of California, Davis, CA, USA
| | - Raissa Estrela
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Jing Luo
- UC Davis Genome Center, University of California, Davis, CA, USA
| | - Navneet Rai
- UC Davis Genome Center, University of California, Davis, CA, USA
| | - Athanasios Tsoukalas
- UC Davis Genome Center, University of California, Davis, CA, USA Department of Computer Science, University of California, Davis, CA, USA
| | - Ilias Tagkopoulos
- UC Davis Genome Center, University of California, Davis, CA, USA Department of Computer Science, University of California, Davis, CA, USA
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31
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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: 576] [Impact Index Per Article: 57.6] [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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32
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Platforms for Genetic Design Automation. METHODS IN MICROBIOLOGY 2013. [DOI: 10.1016/b978-0-12-417029-2.00007-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register]
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