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Kardynska M, Kogut D, Pacholczyk M, Smieja J. Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods. Comput Struct Biotechnol J 2023; 21:1523-1532. [PMID: 36851915 PMCID: PMC9958294 DOI: 10.1016/j.csbj.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.
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Affiliation(s)
- Malgorzata Kardynska
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland
| | - Daria Kogut
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Marcin Pacholczyk
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Jaroslaw Smieja
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
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2
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Oliveira SMD, Densmore D. Hardware, Software, and Wetware Codesign Environment for Synthetic Biology. BIODESIGN RESEARCH 2022; 2022:9794510. [PMID: 37850136 PMCID: PMC10521664 DOI: 10.34133/2022/9794510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/10/2022] [Indexed: 10/19/2023] Open
Abstract
Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.
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Affiliation(s)
- Samuel M. D. Oliveira
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
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3
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Wang Q, Guo Y, Huang T, Zhang X, Zhang P, Xie H, Liu J, Li L, Kong Z, Qin P. Transcriptome and Metabolome Analyses Revealed the Response Mechanism of Quinoa Seedlings to Different Phosphorus Stresses. Int J Mol Sci 2022; 23:ijms23094704. [PMID: 35563095 PMCID: PMC9105174 DOI: 10.3390/ijms23094704] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/23/2022] Open
Abstract
Quinoa (Chenopodium quinoa Willd.) is a dicotyledonous annual herb of Family Amaranthaceae and Subfamily Chenopodiaceae. It has high nutritional and economic value. Phosphorus (P) is an essential plant macronutrient, a component of many biomolecules, and vital to growth, development, and metabolism. We analyzed the transcriptomes and metabolomes of Dianli–1299 and Dianli–71 quinoa seedlings, compared their phenotypes, and elucidated the mechanisms of their responses to the phosphorus treatments. Phenotypes significantly varied with phosphorus level. The plants responded to changes in available phosphorus by modulating metabolites and genes implicated in glycerophospholipid, glycerolipid and glycolysis, and glyconeogenesis metabolism. We detected 1057 metabolites, of which 149 were differentially expressed (DEMs) and common to the control (CK) vs. the low-phosphorus (LP) treatment samples, while two DEMs were common to CK vs. the high-phosphorus (HP) treatment samples. The Kyoto Encyclopedia of genes and genomes (KEGG) annotated 29,232 genes, of which 231 were differentially expressed (DEGs) and common to CK vs. LP, while one was common to CK vs. HP. A total of 15 DEMs and 11 DEGs might account for the observed differences in the responses of the quinoa seedlings to the various phosphorus levels. The foregoing results may provide a theoretical basis for improving the phosphorus utilization efficiency in quinoa.
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Affiliation(s)
- Qianchao Wang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Yirui Guo
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Tingzhi Huang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Xuesong Zhang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Ping Zhang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Heng Xie
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Junna Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Li Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
| | - Zhiyou Kong
- College of Natural Resources and Environment, Baoshan University, Baoshan 678000, China
- Correspondence: (Z.K.); (P.Q.); Tel.: +86-130-9967-6866 (Z.K.); +86-135-0880-6942 (P.Q.)
| | - Peng Qin
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (Q.W.); (Y.G.); (T.H.); (X.Z.); (P.Z.); (H.X.); (J.L.); (L.L.)
- Correspondence: (Z.K.); (P.Q.); Tel.: +86-130-9967-6866 (Z.K.); +86-135-0880-6942 (P.Q.)
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4
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Shallom D, Naiger D, Weiss S, Tuller T. Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware. ACS Synth Biol 2021; 10:3489-3506. [PMID: 34813269 PMCID: PMC8689694 DOI: 10.1021/acssynbio.1c00415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In recent years, intracellular biophysical simulations have been used with increasing frequency not only for answering basic scientific questions but also in the field of synthetic biology. However, since these models include networks of interaction between millions of components, they are extremely time-consuming and cannot run easily on parallel computers. In this study, we demonstrate for the first time a novel approach addressing this challenge by using a dedicated hardware designed specifically to simulate such processes. As a proof of concept, we specifically focus on mRNA translation, which is the process consuming most of the energy in the cell. We design a hardware that simulates translation in Escherichia coli and Saccharomyces cerevisiae for thousands of mRNAs and ribosomes, which is in orders of magnitude faster than a similar software solution. With the sharp increase in the amount of genomic data available today and the complexity of the corresponding models inferred from them, we believe that the strategy suggested here will become common and can be used among others for simulating entire cells with all gene expression steps.
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Affiliation(s)
- David Shallom
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Danny Naiger
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Shlomo Weiss
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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5
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Raman K, Sinha H, Vickers CE, Nikel PI. Synthetic biology beyond borders. Microb Biotechnol 2021; 14:2254-2256. [PMID: 34792854 PMCID: PMC8601182 DOI: 10.1111/1751-7915.13966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 10/22/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- Karthik Raman
- Department of BiotechnologyCentre for Integrative Biology and Systems Medicine (IBSE)Indian Institute of Technology MadrasChennaiIndia
| | - Himanshu Sinha
- Department of BiotechnologyCentre for Integrative Biology and Systems Medicine (IBSE)Indian Institute of Technology MadrasChennaiIndia
| | - Claudia E. Vickers
- CSIRO Future Science Platform in Synthetic BiologyCommonwealth Scientific and Industrial Research Organization (CSIRO)Dutton ParkAustralia
| | - Pablo I. Nikel
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
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Martinez-Jimenez F, de Arruda Ribeiro MP, Sargo CR, Ienczak JL, Morais ER, da Costa AC. Dynamic Modeling Application To Evaluate the Performance of Spathaspora passalidarum in Second-Generation Ethanol Production: Parametric Dynamics and the Likelihood Confidence Region. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c02299] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Fernan Martinez-Jimenez
- School of Chemical Engineering, University of Campinas (UNICAMP), Campinas, São Paulo 13083-852, Brazil
- Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo 13083-970, Brazil
| | | | - Cintia Regina Sargo
- Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo 13083-970, Brazil
| | - Jaciane Lutz Ienczak
- Chemical Engineering and Food Engineering Department, Santa Catarina Federal University, Florianópolis, Santa Catarina 88040-900, Brazil
| | - Edvaldo Rodrigo Morais
- Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo 13083-970, Brazil
| | - Aline Carvalho da Costa
- School of Chemical Engineering, University of Campinas (UNICAMP), Campinas, São Paulo 13083-852, Brazil
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Asemoloye MD, Marchisio MA, Gupta VK, Pecoraro L. Genome-based engineering of ligninolytic enzymes in fungi. Microb Cell Fact 2021; 20:20. [PMID: 33478513 PMCID: PMC7819241 DOI: 10.1186/s12934-021-01510-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/07/2021] [Indexed: 12/23/2022] Open
Abstract
Background Many fungi grow as saprobic organisms and obtain nutrients from a wide range of dead organic materials. Among saprobes, fungal species that grow on wood or in polluted environments have evolved prolific mechanisms for the production of degrading compounds, such as ligninolytic enzymes. These enzymes include arrays of intense redox-potential oxidoreductase, such as laccase, catalase, and peroxidases. The ability to produce ligninolytic enzymes makes a variety of fungal species suitable for application in many industries, including the production of biofuels and antibiotics, bioremediation, and biomedical application as biosensors. However, fungal ligninolytic enzymes are produced naturally in small quantities that may not meet the industrial or market demands. Over the last decade, combined synthetic biology and computational designs have yielded significant results in enhancing the synthesis of natural compounds in fungi. Main body of the abstract In this review, we gave insights into different protein engineering methods, including rational, semi-rational, and directed evolution approaches that have been employed to enhance the production of some important ligninolytic enzymes in fungi. We described the role of metabolic pathway engineering to optimize the synthesis of chemical compounds of interest in various fields. We highlighted synthetic biology novel techniques for biosynthetic gene cluster (BGC) activation in fungo and heterologous reconstruction of BGC in microbial cells. We also discussed in detail some recombinant ligninolytic enzymes that have been successfully enhanced and expressed in different heterologous hosts. Finally, we described recent advance in CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR associated) protein systems as the most promising biotechnology for large-scale production of ligninolytic enzymes. Short conclusion Aggregation, expression, and regulation of ligninolytic enzymes in fungi require very complex procedures with many interfering factors. Synthetic and computational biology strategies, as explained in this review, are powerful tools that can be combined to solve these puzzles. These integrated strategies can lead to the production of enzymes with special abilities, such as wide substrate specifications, thermo-stability, tolerance to long time storage, and stability in different substrate conditions, such as pH and nutrients.
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Affiliation(s)
- Michael Dare Asemoloye
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China
| | - Mario Andrea Marchisio
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China.
| | - Vijai Kumar Gupta
- Biorefining and Advanced Materials Research Center, Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Lorenzo Pecoraro
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China.
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Automated Biocircuit Design with SYNBADm. Methods Mol Biol 2021. [PMID: 33405218 DOI: 10.1007/978-1-0716-1032-9_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
SYNBADm is a Matlab toolbox for the automated design of biocircuits using a model-based optimization approach. It enables the design of biocircuits with pre-defined functions starting from libraries of biological parts. SYNBADm makes use of mixed integer global optimization and allows both single and multi-objective design problems. Here we describe a basic protocol for the design of synthetic gene regulatory circuits. We illustrate step-by-step how to solve two different problems: (1) the (single objective) design of a synthetic oscillator and (2) the (multi-objective) design of a circuit with switch-like behavior upon induction, with a good compromise between performance and protein production cost.
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Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 33381857 DOI: 10.1007/10_2020_154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.
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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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Meyer A, Saaem I, Silverman A, Varaljay VA, Mickol R, Blum S, Tobias AV, Schwalm ND, Mojadedi W, Onderko E, Bristol C, Liu S, Pratt K, Casini A, Eluere R, Moser F, Drake C, Gupta M, Kelley-Loughnane N, Lucks JP, Akingbade KL, Lux MP, Glaven S, Crookes-Goodson W, Jewett MC, Gordon DB, Voigt CA. Organism Engineering for the Bioproduction of the Triaminotrinitrobenzene (TATB) Precursor Phloroglucinol (PG). ACS Synth Biol 2019; 8:2746-2755. [PMID: 31750651 DOI: 10.1021/acssynbio.9b00393] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Organism engineering requires the selection of an appropriate chassis, editing its genome, combining traits from different source species, and controlling genes with synthetic circuits. When a strain is needed for a new target objective, for example, to produce a chemical-of-need, the best strains, genes, techniques, software, and expertise may be distributed across laboratories. Here, we report a project where we were assigned phloroglucinol (PG) as a target, and then combined unique capabilities across the United States Army, Navy, and Air Force service laboratories with the shared goal of designing an organism to produce this molecule. In addition to the laboratory strain Escherichia coli, organisms were screened from soil and seawater. Putative PG-producing enzymes were mined from a strain bank of bacteria isolated from aircraft and fuel depots. The best enzyme was introduced into the ocean strain Marinobacter atlanticus CP1 with its genome edited to redirect carbon flux from natural fatty acid ester (FAE) production. PG production was also attempted in Bacillus subtilis and Clostridium acetobutylicum. A genetic circuit was constructed in E. coli that responds to PG accumulation, which was then ported to an in vitro paper-based system that could serve as a platform for future low-cost strain screening or for in-field sensing. Collectively, these efforts show how distributed biotechnology laboratories with domain-specific expertise can be marshalled to quickly provide a solution for a targeted organism engineering project, and highlights data and material sharing protocols needed to accelerate future efforts.
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Affiliation(s)
- Adam Meyer
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Ishtiaq Saaem
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Adam Silverman
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Vanessa A. Varaljay
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Rebecca Mickol
- American Society for Engineering Education, 1818 N Street NW Suite 600, Washington, D.C. 20036, United States
| | - Steven Blum
- U.S. Army Combat Capabilities Development Command Chemical Biological Center, 8198 Blackhawk Road, Aberdeen Proving Ground, Maryland 21010, United States
| | - Alexander V. Tobias
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Nathan D. Schwalm
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Wais Mojadedi
- Oak Ridge Associate Universities, P.O.
Box 117, MS-29, Oak Ridge, Tennessee 37831, United States
| | - Elizabeth Onderko
- National Research Council, 500 5th Street NW, Washington, D.C. 20001, United States
| | - Cassandra Bristol
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Shangtao Liu
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
| | - Katelin Pratt
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Arturo Casini
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Raissa Eluere
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Felix Moser
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Carrie Drake
- UES, Inc., 4401 Dayton-Xenia Road, Dayton, Ohio 45432, United States
| | - Maneesh Gupta
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Nancy Kelley-Loughnane
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Julius P. Lucks
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Katherine L. Akingbade
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Matthew P. Lux
- U.S. Army Combat Capabilities Development Command Chemical Biological Center, 8198 Blackhawk Road, Aberdeen Proving Ground, Maryland 21010, United States
| | - Sarah Glaven
- Center for Bio/Molecular Science and Engineering, Naval Research Laboratory, Washington, D.C. 20375, United States
| | - Wendy Crookes-Goodson
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Michael C. Jewett
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - D. Benjamin Gordon
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Christopher A. Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
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Szymanski E, Scher E. Models for DNA Design Tools: The Trouble with Metaphors Is That They Don't Go Away. ACS Synth Biol 2019; 8:2635-2641. [PMID: 31580653 DOI: 10.1021/acssynbio.9b00302] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Synthetic biology relies heavily on DNA design tools to enable manipulation of DNA in silico. Existing tools, however, are falling short of enabling aspirations for the field that emphasize efficient, automated design pipelines. We review existing DNA design tools, identify underlying similarities in how they model correlations between DNA structure and function, and suggest that iterating the existing model is unlikely to overcome limitations in matching software applications to design aspirations. The current model is predicated on metaphors conceptualizing DNA as linear text, accounting for relatively little of the known complexity of DNA function. New models that can account for more of that complexity and thus enable more ambitious DNA design goals are likely to call for new underlying metaphors-a need that may be addressed by rethinking DNA in terms of human rather than computer languages.
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Affiliation(s)
- Erika Szymanski
- Colorado State University, Department of English, Fort Collins, Colorado 80523, United States
| | - Emily Scher
- Informatics Forum, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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13
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Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107247] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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14
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Rabinowitch I. What would a synthetic connectome look like? Phys Life Rev 2019; 33:1-15. [PMID: 31296448 DOI: 10.1016/j.plrev.2019.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/25/2019] [Indexed: 02/07/2023]
Abstract
A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.
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Affiliation(s)
- Ithai Rabinowitch
- Department of Medical Neurobiology, IMRIC - Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, 9112002, Israel.
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15
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Distilling Robust Design Principles of Biocircuits Using Mixed Integer Dynamic Optimization. Processes (Basel) 2019. [DOI: 10.3390/pr7020092] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
A major challenge in model-based design of synthetic biochemical circuits is how to address uncertainty in the parameters. A circuit whose behavior is robust to variations in the parameters will have more chances to behave as predicted when implemented in practice, and also to function reliably in presence of fluctuations and noise. Here, we extend our recent work on automated-design based on mixed-integer multi-criteria dynamic optimization to take into account parametric uncertainty. We exploit the intensive sampling of the design space performed by a global optimization algorithm to obtain the robustness of the topologies without significant additional computational effort. Our procedure provides automatically topologies that best trade-off performance and robustness against parameter fluctuations. We illustrate our approach considering the automated design of gene circuits achieving adaptation.
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16
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Xiang Y, Dalchau N, Wang B. Scaling up genetic circuit design for cellular computing: advances and prospects. NATURAL COMPUTING 2018; 17:833-853. [PMID: 30524216 PMCID: PMC6244767 DOI: 10.1007/s11047-018-9715-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.
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Affiliation(s)
- Yiyu Xiang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
| | | | - Baojun Wang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
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17
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In situ biomolecule production by bacteria; a synthetic biology approach to medicine. J Control Release 2018; 275:217-228. [DOI: 10.1016/j.jconrel.2018.02.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/14/2018] [Accepted: 02/15/2018] [Indexed: 02/06/2023]
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18
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Conradi C, Feliu E, Mincheva M, Wiuf C. Identifying parameter regions for multistationarity. PLoS Comput Biol 2017; 13:e1005751. [PMID: 28972969 PMCID: PMC5626113 DOI: 10.1371/journal.pcbi.1005751] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Accepted: 08/31/2017] [Indexed: 01/20/2023] Open
Abstract
Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity.
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Affiliation(s)
| | - Elisenda Feliu
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Maya Mincheva
- Department of Mathematical Sciences, Northern Illinois University, DeKalb, Illinois, United States of America
| | - Carsten Wiuf
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
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19
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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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20
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Mısırlı G, Madsen C, Murieta IS, Bultelle M, Flanagan K, Pocock M, Hallinan J, McLaughlin JA, Clark‐Casey J, Lyne M, Micklem G, Stan G, Kitney R, Wipat A. Constructing synthetic biology workflows in the cloud. ENGINEERING BIOLOGY 2017. [DOI: 10.1049/enb.2017.0001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Göksel Mısırlı
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | - Curtis Madsen
- Electrical & Computer Engineering Department Boston University Boston USA
| | | | | | - Keith Flanagan
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | | | | | | | - Justin Clark‐Casey
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Mike Lyne
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Gos Micklem
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Guy‐Bart Stan
- Department of Bioengineering Imperial College London London UK
| | - Richard Kitney
- Department of Bioengineering Imperial College London London UK
| | - Anil Wipat
- School of Computing Science Newcastle University Newcastle upon Tyne UK
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21
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Schreiber J, Arter M, Lapique N, Haefliger B, Benenson Y. Model-guided combinatorial optimization of complex synthetic gene networks. Mol Syst Biol 2016; 12:899. [PMID: 28031353 PMCID: PMC5199127 DOI: 10.15252/msb.20167265] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 11/18/2016] [Accepted: 11/25/2016] [Indexed: 01/25/2023] Open
Abstract
Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.
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Affiliation(s)
- Joerg Schreiber
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zürich), Basel, Switzerland
| | - Meret Arter
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zürich), Basel, Switzerland
| | - Nicolas Lapique
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zürich), Basel, Switzerland
| | - Benjamin Haefliger
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zürich), Basel, Switzerland
| | - Yaakov Benenson
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zürich), Basel, Switzerland
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22
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Sun J, Alper H. Synthetic Biology: An Emerging Approach for Strain Engineering. Ind Biotechnol (New Rochelle N Y) 2016. [DOI: 10.1002/9783527807796.ch2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Jie Sun
- Department of Chemical Engineering; The University of Texas at Austin; 200 E Dean Keeton Street Stop C0400, Austin TX 78712 USA
| | - Hal Alper
- Department of Chemical Engineering; The University of Texas at Austin; 200 E Dean Keeton Street Stop C0400, Austin TX 78712 USA
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23
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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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24
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Schwarz KA, Leonard JN. Engineering cell-based therapies to interface robustly with host physiology. Adv Drug Deliv Rev 2016; 105:55-65. [PMID: 27266446 DOI: 10.1016/j.addr.2016.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 05/10/2016] [Accepted: 05/24/2016] [Indexed: 12/21/2022]
Abstract
Engineered cell-based therapies comprise a rapidly growing clinical technology for treating disease by leveraging the natural capabilities of cells, including migration, information transduction, and biosynthesis and secretion. There now exists a substantial portfolio of intracellular and extracellular sensors that enable bioengineers to program cells to execute defined responses to specific changes in state or environmental cues. As our capability to construct more sophisticated cellular programs increases, assessing and improving the degree to which cell-based therapies perform as desired in vivo will become an increasingly important consideration and opportunity for technological advancement. In this review, we seek to describe both current capabilities and potential needs for building cell-based therapies that interface with host physiology in a manner that is robust - a phrase we use in this context to describe the achievement of therapeutic efficacy across a range of patients and implementations. We first review the portfolio of sensors and outputs currently available for use in cell-based therapies by highlighting key advancements and current gaps. Then, we propose a conceptual framework for evaluating and pursuing robust clinical performance of engineered cell-based therapies.
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25
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Kushwaha M, Rostain W, Prakash S, Duncan JN, Jaramillo A. Using RNA as Molecular Code for Programming Cellular Function. ACS Synth Biol 2016; 5:795-809. [PMID: 26999422 DOI: 10.1021/acssynbio.5b00297] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
RNA is involved in a wide-range of important molecular processes in the cell, serving diverse functions: regulatory, enzymatic, and structural. Together with its ease and predictability of design, these properties can lead RNA to become a useful handle for biological engineers with which to control the cellular machinery. By modifying the many RNA links in cellular processes, it is possible to reprogram cells toward specific design goals. We propose that RNA can be viewed as a molecular programming language that, together with protein-based execution platforms, can be used to rewrite wide ranging aspects of cellular function. In this review, we catalogue developments in the use of RNA parts, methods, and associated computational models that have contributed to the programmability of biology. We discuss how RNA part repertoires have been combined to build complex genetic circuits, and review recent applications of RNA-based parts and circuitry. We explore the future potential of RNA engineering and posit that RNA programmability is an important resource for firmly establishing an era of rationally designed synthetic biology.
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Affiliation(s)
- Manish Kushwaha
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - William Rostain
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
- iSSB, Genopole,
CNRS, UEVE, Université Paris-Saclay, Évry, France
| | - Satya Prakash
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - John N. Duncan
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - Alfonso Jaramillo
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
- iSSB, Genopole,
CNRS, UEVE, Université Paris-Saclay, Évry, France
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26
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Costa RS, Hartmann A, Vinga S. Kinetic modeling of cell metabolism for microbial production. J Biotechnol 2015; 219:126-41. [PMID: 26724578 DOI: 10.1016/j.jbiotec.2015.12.023] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/25/2015] [Accepted: 12/15/2015] [Indexed: 12/20/2022]
Abstract
Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.
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Affiliation(s)
- Rafael S Costa
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal.
| | - Andras Hartmann
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
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27
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Gábor A, Banga JR. Robust and efficient parameter estimation in dynamic models of biological systems. BMC SYSTEMS BIOLOGY 2015; 9:74. [PMID: 26515482 PMCID: PMC4625902 DOI: 10.1186/s12918-015-0219-2] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 10/08/2015] [Indexed: 11/16/2022]
Abstract
Background Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation in nonlinear dynamic models remains a very challenging inverse problem due to its nonconvexity and ill-conditioning. Associated issues like overfitting and local solutions are usually not properly addressed in the systems biology literature despite their importance. Here we present a method for robust and efficient parameter estimation which uses two main strategies to surmount the aforementioned difficulties: (i) efficient global optimization to deal with nonconvexity, and (ii) proper regularization methods to handle ill-conditioning. In the case of regularization, we present a detailed critical comparison of methods and guidelines for properly tuning them. Further, we show how regularized estimations ensure the best trade-offs between bias and variance, reducing overfitting, and allowing the incorporation of prior knowledge in a systematic way. Results We illustrate the performance of the presented method with seven case studies of different nature and increasing complexity, considering several scenarios of data availability, measurement noise and prior knowledge. We show how our method ensures improved estimations with faster and more stable convergence. We also show how the calibrated models are more generalizable. Finally, we give a set of simple guidelines to apply this strategy to a wide variety of calibration problems. Conclusions Here we provide a parameter estimation strategy which combines efficient global optimization with a regularization scheme. This method is able to calibrate dynamic models in an efficient and robust way, effectively fighting overfitting and allowing the incorporation of prior information. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0219-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Attila Gábor
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
| | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
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28
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Courbet A, Molina F, Amar P. Computing with synthetic protocells. Acta Biotheor 2015; 63:309-23. [PMID: 25969126 DOI: 10.1007/s10441-015-9258-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Accepted: 05/05/2015] [Indexed: 11/30/2022]
Abstract
In this article we present a new kind of computing device that uses biochemical reactions networks as building blocks to implement logic gates. The architecture of a computing machine relies on these generic and composable building blocks, computation units, that can be used in multiple instances to perform complex boolean functions. Standard logical operations are implemented by biochemical networks, encapsulated and insulated within synthetic vesicles called protocells. These protocells are capable of exchanging energy and information with each other through transmembrane electron transfer. In the paradigm of computation we propose, protoputing, a machine can solve only one problem and therefore has to be built specifically. Thus, the programming phase in the standard computing paradigm is represented in our approach by the set of assembly instructions (specific attachments) that directs the wiring of the protocells that constitute the machine itself. To demonstrate the computing power of protocellular machines, we apply it to solve a NP-complete problem, known to be very demanding in computing power, the 3-SAT problem. We show how to program the assembly of a machine that can verify the satisfiability of a given boolean formula. Then we show how to use the massive parallelism of these machines to verify in less than 20 min all the valuations of the input variables and output a fluorescent signal when the formula is satisfiable or no signal at all otherwise.
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Affiliation(s)
- Alexis Courbet
- Sys2diag, FRE CNRS 3690, 1682 rue de la Valsière, 34184, Montpellier, France
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29
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Dushek O, Lellouch AC, Vaux DJ, Shahrezaei V. Biosensor architectures for high-fidelity reporting of cellular signaling. Biophys J 2015; 107:773-782. [PMID: 25099816 PMCID: PMC4129486 DOI: 10.1016/j.bpj.2014.06.021] [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: 03/06/2014] [Revised: 05/26/2014] [Accepted: 06/10/2014] [Indexed: 11/29/2022] Open
Abstract
Understanding mechanisms of information processing in cellular signaling networks requires quantitative measurements of protein activities in living cells. Biosensors are molecular probes that have been developed to directly track the activity of specific signaling proteins and their use is revolutionizing our understanding of signal transduction. The use of biosensors relies on the assumption that their activity is linearly proportional to the activity of the signaling protein they have been engineered to track. We use mechanistic mathematical models of common biosensor architectures (single-chain FRET-based biosensors), which include both intramolecular and intermolecular reactions, to study the validity of the linearity assumption. As a result of the classic mechanism of zero-order ultrasensitivity, we find that biosensor activity can be highly nonlinear so that small changes in signaling protein activity can give rise to large changes in biosensor activity and vice versa. This nonlinearity is abolished in architectures that favor the formation of biosensor oligomers, but oligomeric biosensors produce complicated FRET states. Based on this finding, we show that high-fidelity reporting is possible when a single-chain intermolecular biosensor is used that cannot undergo intramolecular reactions and is restricted to forming dimers. We provide phase diagrams that compare various trade-offs, including observer effects, which further highlight the utility of biosensor architectures that favor intermolecular over intramolecular binding. We discuss challenges in calibrating and constructing biosensors and highlight the utility of mathematical models in designing novel probes for cellular signaling.
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Affiliation(s)
- Omer Dushek
- Sir William Dunn School of Pathology, University of Oxford, United Kingdom; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom.
| | - Annemarie C Lellouch
- Aix Marseille Université, Laboratoire d'Adhésion et Inflammation, Marseille, France; Institut National de la Santé et de la Recherche Médicale U1067, Marseille, France; Centre National de la Recherche Scientifique UMR 7333, Marseille, France
| | - David J Vaux
- Sir William Dunn School of Pathology, University of Oxford, United Kingdom
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College London, United Kingdom.
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30
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Biarnes-Carrera M, Breitling R, Takano E. Butyrolactone signalling circuits for synthetic biology. Curr Opin Chem Biol 2015; 28:91-8. [PMID: 26164547 DOI: 10.1016/j.cbpa.2015.06.024] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 06/16/2015] [Accepted: 06/20/2015] [Indexed: 01/14/2023]
Abstract
Signalling circuits based on quorum sensing mechanisms have been popular tools for synthetic biology. Recent advances in our understanding of the analogous systems regulating antibiotics production in soil bacteria suggest that these might provide useful complementary tools to increase the complexity of possible circuit designs. Here we discuss the diversity of these natural circuits, which use γ-butyrolactones (GBLs) as their main inter-cellular signal, highlighting the range of new building blocks they could provide, as well as a number of exciting recent applications of GBL-based circuits in heterologous systems. We conclude by presenting examples of the novel circuit complexity that could become accessible through the use of GBL-based designs.
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Affiliation(s)
- Marc Biarnes-Carrera
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom.
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31
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Wells DK, Kath WL, Motter AE. Control of Stochastic and Induced Switching in Biophysical Networks. PHYSICAL REVIEW. X 2015; 5:031036. [PMID: 26451275 PMCID: PMC4594957 DOI: 10.1103/physrevx.5.031036] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Noise caused by fluctuations at the molecular level is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here we present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. We apply this methodology to models of cell differentiation and show how predicted manipulations of tunable factors can induce lineage changes, and further utilize it to identify new candidate strategies for cancer therapy in a cell death pathway model. This framework offers a systems approach to identifying the key factors for rationally manipulating biophysical dynamics, and should also find use in controlling other classes of noisy complex networks.
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Affiliation(s)
- Daniel K. Wells
- Department of Engineering Sciences and Applied Mathematics,
Northwestern University, Evanston, IL 60208, USA
- Northwestern Physical Sciences-Oncology Center,
Northwestern University, Evanston, IL 60208, USA
| | - William L. Kath
- Department of Engineering Sciences and Applied Mathematics,
Northwestern University, Evanston, IL 60208, USA
- Northwestern Physical Sciences-Oncology Center,
Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern
University, Evanston, IL 60208, USA
| | - Adilson E. Motter
- Northwestern Physical Sciences-Oncology Center,
Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern
University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern
University, Evanston IL, 60208, USA
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32
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Liu W, Stewart CN. Plant synthetic biology. TRENDS IN PLANT SCIENCE 2015; 20:309-317. [PMID: 25825364 DOI: 10.1016/j.tplants.2015.02.004] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Revised: 02/11/2015] [Accepted: 02/25/2015] [Indexed: 05/18/2023]
Abstract
Plant synthetic biology is an emerging field that combines engineering principles with plant biology toward the design and production of new devices. This emerging field should play an important role in future agriculture for traditional crop improvement, but also in enabling novel bioproduction in plants. In this review we discuss the design cycles of synthetic biology as well as key engineering principles, genetic parts, and computational tools that can be utilized in plant synthetic biology. Some pioneering examples are offered as a demonstration of how synthetic biology can be used to modify plants for specific purposes. These include synthetic sensors, synthetic metabolic pathways, and synthetic genomes. We also speculate about the future of synthetic biology of plants.
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Affiliation(s)
- Wusheng Liu
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996-4561, USA
| | - C Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996-4561, USA; BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6037, USA.
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33
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Patil M, Dhar PK. A Brief Introduction to Synthetic Biology. SYSTEMS AND SYNTHETIC BIOLOGY 2015. [DOI: 10.1007/978-94-017-9514-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Samish I, Bourne PE, Najmanovich RJ. Achievements and challenges in structural bioinformatics and computational biophysics. Bioinformatics 2014; 31:146-50. [PMID: 25488929 PMCID: PMC4271151 DOI: 10.1093/bioinformatics/btu769] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects. Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results. Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field. Contact:Rafael.Najmanovich@USherbrooke.ca
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Affiliation(s)
- Ilan Samish
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - Philip E Bourne
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - Rafael J Najmanovich
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
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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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A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput Struct Biotechnol J 2014; 11:91-9. [PMID: 25379147 PMCID: PMC4212277 DOI: 10.1016/j.csbj.2014.08.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.
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Egea JA, Henriques D, Cokelaer T, Villaverde AF, MacNamara A, Danciu DP, Banga JR, Saez-Rodriguez J. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics 2014; 15:136. [PMID: 24885957 PMCID: PMC4025564 DOI: 10.1186/1471-2105-15-136] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Accepted: 04/24/2014] [Indexed: 11/28/2022] Open
Abstract
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
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Affiliation(s)
| | | | | | | | | | | | - Julio R Banga
- (Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain.
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Swainston N, Currin A, Day PJ, Kell DB. GeneGenie: optimized oligomer design for directed evolution. Nucleic Acids Res 2014; 42:W395-400. [PMID: 24782527 PMCID: PMC4086129 DOI: 10.1093/nar/gku336] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
GeneGenie, a new online tool available at http://www.gene-genie.org, is introduced to support the design and self-assembly of synthetic genes and constructs. GeneGenie allows for the design of oligonucleotide cohorts encoding the gene sequence optimized for expression in any suitable host through an intuitive, easy-to-use web interface. The tool ensures consistent oligomer overlapping melting temperatures, minimizes the likelihood of misannealing, optimizes codon usage for expression in a selected host, allows for specification of forward and reverse cloning sequences (for downstream ligation) and also provides support for mutagenesis or directed evolution studies. Directed evolution studies are enabled through the construction of variant libraries via the optional specification of ‘variant codons’, containing mixtures of bases, at any position. For example, specifying the variant codon TNT (where N is any nucleotide) will generate an equimolar mixture of the codons TAT, TCT, TGT and TTT at that position, encoding a mixture of the amino acids Tyr, Ser, Cys and Phe. This facility is demonstrated through the use of GeneGenie to develop and synthesize a library of enhanced green fluorescent protein variants.
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Affiliation(s)
- Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
| | - Andrew Currin
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | - Philip J Day
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
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Abstract
Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined.
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Affiliation(s)
- Javier Macia
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain
- Institut de Biologia Evolutiva, UPF-CSIC, Barcelona, Spain
- * E-mail: (JM); (RS)
| | - Ricard Sole
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain
- Institut de Biologia Evolutiva, UPF-CSIC, Barcelona, Spain
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail: (JM); (RS)
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40
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Accelerated protein engineering for chemical biotechnology via homologous recombination. Curr Opin Biotechnol 2013; 24:1017-22. [DOI: 10.1016/j.copbio.2013.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Revised: 02/28/2013] [Accepted: 03/05/2013] [Indexed: 12/22/2022]
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Giese B, Koenigstein S, Wigger H, Schmidt JC, von Gleich A. Rational Engineering Principles in Synthetic Biology: A Framework for Quantitative Analysis and an Initial Assessment. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/s13752-013-0130-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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44
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Chuang JS. Engineering multicellular traits in synthetic microbial populations. Curr Opin Chem Biol 2012; 16:370-8. [DOI: 10.1016/j.cbpa.2012.04.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2012] [Accepted: 04/03/2012] [Indexed: 01/02/2023]
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Higuera C, Villaverde AF, Banga JR, Ross J, Morán F. Multi-criteria optimization of regulation in metabolic networks. PLoS One 2012; 7:e41122. [PMID: 22848435 PMCID: PMC3406099 DOI: 10.1371/journal.pone.0041122] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Accepted: 06/21/2012] [Indexed: 02/01/2023] Open
Abstract
Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different "environments" (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.
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Affiliation(s)
- Clara Higuera
- Department of Biochemistry and Molecular Biology, Complutense University, Madrid, Spain
| | | | - Julio R. Banga
- Bio Process Engineering Group IIM-CSIC (Spanish National Research Council), Vigo, Spain
| | - John Ross
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Federico Morán
- Department of Biochemistry and Molecular Biology, Complutense University, Madrid, Spain
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Hoffman-Sommer M, Supady A, Klipp E. Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates. Front Physiol 2012; 3:287. [PMID: 22934039 PMCID: PMC3429059 DOI: 10.3389/fphys.2012.00287] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2012] [Accepted: 07/02/2012] [Indexed: 12/29/2022] Open
Abstract
One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.
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Affiliation(s)
- Marta Hoffman-Sommer
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin Berlin, Germany
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Abstract
As the field of synthetic biology is developing, the prospects for de novo design of biosynthetic pathways are becoming more and more realistic. Hence, there is an increasing need for computational tools that can support these efforts. A range of algorithms has been developed that can be used to identify all possible metabolic pathways and their corresponding enzymatic parts. These can then be ranked according to various properties and modelled in an organism-specific context. Finally, design software can aid the biologist in the integration of a selected pathway into smartly regulated transcriptional units. Here, we review key existing tools and offer suggestions for how informatics can help to shape the future of synthetic microbiology.
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49
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Integrative Systems Biology II—Molecular Biology: Phase 2 Lead Discovery and In Silico Screening. SYSTEMS BIOLOGY IN BIOTECH & PHARMA 2012. [DOI: 10.1007/978-94-007-2849-3_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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50
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Lebiedz D, Rehberg M, Skanda D. Robust optimal design of synthetic biological networks. Methods Mol Biol 2012; 813:45-55. [PMID: 22083735 DOI: 10.1007/978-1-61779-412-4_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In engineering, the use of mathematical modeling for design purposes has a long history. Long before any technical realization, a system is planned, simulated, and tested extensively on the computer. In biosciences, however, the application of model-based design before going to the wet lab is still rather rare but has particularly high potential in synthetic biology. We demonstrate exemplarily how mathematical modeling and numerical optimization can be used for the design of a circadian rhythm that is supposed to oscillate robustly with respect to uncertainty in system parameters.
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Affiliation(s)
- Dirk Lebiedz
- Faculty of Biology, Centre for Biological Systems Analysis, University of Freiburg, Freiburg, Germany.
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