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Kim T, Lee S, Kwak Y, Choi MS, Park J, Hwang SJ, Kim SG. READRetro: natural product biosynthesis predicting with retrieval-augmented dual-view retrosynthesis. THE NEW PHYTOLOGIST 2024; 243:2512-2527. [PMID: 39081009 DOI: 10.1111/nph.20012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/08/2024] [Indexed: 08/23/2024]
Abstract
Plants, as a sessile organism, produce various secondary metabolites to interact with the environment. These chemicals have fascinated the plant science community because of their ecological significance and notable biological activity. However, predicting the complete biosynthetic pathways from target molecules to metabolic building blocks remains a challenge. Here, we propose retrieval-augmented dual-view retrosynthesis (READRetro) as a practical bio-retrosynthesis tool to predict the biosynthetic pathways of plant natural products. Conventional bio-retrosynthesis models have been limited in their ability to predict biosynthetic pathways for natural products. READRetro was optimized for the prediction of complex metabolic pathways by incorporating cutting-edge deep learning architectures, an ensemble approach, and two retrievers. Evaluation of single- and multi-step retrosynthesis showed that each component of READRetro significantly improved its ability to predict biosynthetic pathways. READRetro was also able to propose the known pathways of secondary metabolites such as monoterpene indole alkaloids and the unknown pathway of menisdaurilide, demonstrating its applicability to real-world bio-retrosynthesis of plant natural products. For researchers interested in the biosynthesis and production of secondary metabolites, a user-friendly website (https://readretro.net) and the open-source code of READRetro have been made available.
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Affiliation(s)
- Taein Kim
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
| | - Seul Lee
- Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Korea
| | - Yejin Kwak
- Department of BioMedical Convergence Engineering, Pusan National University, Yangsan, 50612, Korea
| | - Min-Soo Choi
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
| | - Jeongbin Park
- Department of BioMedical Convergence Engineering, Pusan National University, Yangsan, 50612, Korea
| | - Sung Ju Hwang
- Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Korea
- School of Computing, KAIST, Daejeon, 34141, Korea
| | - Sang-Gyu Kim
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
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Xu Z, Mahadevan R. Efficient Enumeration of Branched Novel Biochemical Pathways Using a Probabilistic Technique. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c02211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhiqing Xu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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Maithani D, Sharma A, Gangola S, Choudhary P, Bhatt P. Insights into applications and strategies for discovery of microbial bioactive metabolites. Microbiol Res 2022; 261:127053. [DOI: 10.1016/j.micres.2022.127053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 03/12/2022] [Accepted: 04/26/2022] [Indexed: 10/25/2022]
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Wang L, Upadhyay V, Maranas CD. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. PLoS Comput Biol 2021; 17:e1009448. [PMID: 34570771 PMCID: PMC8496854 DOI: 10.1371/journal.pcbi.1009448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/07/2021] [Accepted: 09/13/2021] [Indexed: 11/19/2022] Open
Abstract
Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor). The standard Gibbs energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs energy change. Here, we introduce a molecular fingerprint-based thermodynamic tool, dGPredictor, that enables distinguishing between (stereo)isomers in metabolic reactions leading to improved reaction coverage and comparable prediction accuracy as GC methods. dGPredictor can also be used alongside de novo pathway design tools to ensure the correct directionality of chosen reaction steps. We applied and tested dGPredictor on reactions from the KEGG database and applied it to screen an isobutanol synthesis pathway design. An open-source, user-friendly web interface is provided to facilitate easy access for standard Gibbs energy change of reactions at different pH values. (https://github.com/maranasgroup/dGPredictor).
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Vikas Upadhyay
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
- * E-mail:
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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Wohlgemuth R. Biocatalysis - Key enabling tools from biocatalytic one-step and multi-step reactions to biocatalytic total synthesis. N Biotechnol 2020; 60:113-123. [PMID: 33045418 DOI: 10.1016/j.nbt.2020.08.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/07/2020] [Accepted: 08/31/2020] [Indexed: 12/20/2022]
Abstract
In the area of human-made innovations to improve the quality of life, biocatalysis has already had a great impact and contributed enormously to a growing number of catalytic transformations aimed at the detection and analysis of compounds, the bioconversion of starting materials and the preparation of target compounds at any scale, from laboratory small scale to industrial large scale. The key enabling tools which have been developed in biocatalysis over the last decades also provide great opportunities for further development and numerous applications in various sectors of the global bioeconomy. Systems biocatalysis is a modular, bottom-up approach to designing the architecture of enzyme-catalyzed reaction steps in a synthetic route from starting materials to target molecules. The integration of biocatalysis and sustainable chemistry in vitro aims at ideal conversions with high molecular economy and their intensification. Retrosynthetic analysis in the chemical and biological domain has been a valuable tool for target-oriented synthesis while, on the other hand, diversity-oriented synthesis builds on forward-looking analysis. Bioinformatic tools for rapid identification of the required enzyme functions, efficient enzyme production systems, as well as generalized bioprocess design tools, are important for rapid prototyping of the biocatalytic reactions. The tools for enzyme engineering and the reaction engineering of each enzyme-catalyzed one-step reaction are also valuable for coupling reactions. The tools to overcome interaction issues with other components or enzymes are of great interest in designing multi-step reactions as well as in biocatalytic total synthesis.
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Affiliation(s)
- Roland Wohlgemuth
- Institute of Molecular and Industrial Biotechnology, Lodz University of Technology, Lodz, Poland; Swiss Coordination Committee on Biotechnology (SKB), Nordstrasse 15, 8021 Zürich, Switzerland.
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9
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Systems biology based metabolic engineering for non-natural chemicals. Biotechnol Adv 2019; 37:107379. [DOI: 10.1016/j.biotechadv.2019.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/23/2019] [Accepted: 04/01/2019] [Indexed: 12/17/2022]
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Whitmore LS, Nguyen B, Pinar A, George A, Hudson CM. RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms. BMC Bioinformatics 2019; 20:461. [PMID: 31500573 PMCID: PMC6734243 DOI: 10.1186/s12859-019-3025-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/12/2019] [Indexed: 11/24/2022] Open
Abstract
Background The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production. Electronic supplementary material The online version of this article (10.1186/s12859-019-3025-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Bernard Nguyen
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Ali Pinar
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Anthe George
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Corey M Hudson
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA.
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Lin GM, Warden-Rothman R, Voigt CA. Retrosynthetic design of metabolic pathways to chemicals not found in nature. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A 2019; 116:7298-7307. [PMID: 30910961 PMCID: PMC6462048 DOI: 10.1073/pnas.1818877116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations. Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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Carbonell P, Koch M, Duigou T, Faulon JL. Enzyme Discovery: Enzyme Selection and Pathway Design. Methods Enzymol 2018; 608:3-27. [PMID: 30173766 DOI: 10.1016/bs.mie.2018.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom; Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France; School of Chemistry, The University of Manchester, Manchester, United Kingdom.
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Delépine B, Duigou T, Carbonell P, Faulon JL. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metab Eng 2018; 45:158-170. [DOI: 10.1016/j.ymben.2017.12.002] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 11/03/2017] [Accepted: 12/05/2017] [Indexed: 12/01/2022]
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Campodonico MA, Sukumara S, Feist AM, Herrgård MJ. Computational Methods to Assess the Production Potential of Bio-Based Chemicals. Methods Mol Biol 2018; 1671:97-116. [PMID: 29170955 DOI: 10.1007/978-1-4939-7295-1_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.
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Affiliation(s)
- Miguel A Campodonico
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| | - Sumesh Sukumara
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Adam M Feist
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0412, USA
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
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Asplund-Samuelsson J, Janasch M, Hudson EP. Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential. Metab Eng 2017; 45:223-236. [PMID: 29278749 DOI: 10.1016/j.ymben.2017.12.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 12/04/2017] [Accepted: 12/20/2017] [Indexed: 01/09/2023]
Abstract
Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified.
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Affiliation(s)
- Johannes Asplund-Samuelsson
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
| | - Markus Janasch
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
| | - Elton P Hudson
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
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17
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Koch M, Duigou T, Carbonell P, Faulon JL. Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0. J Cheminform 2017; 9:64. [PMID: 29260340 PMCID: PMC5736515 DOI: 10.1186/s13321-017-0252-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 12/08/2017] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Network generation tools coupled with chemical reaction rules have been mainly developed for synthesis planning and more recently for metabolic engineering. Using the same core algorithm, these tools apply a set of rules to a source set of compounds, stopping when a sink set of compounds has been produced. When using the appropriate sink, source and rules, this core algorithm can be used for a variety of applications beyond those it has been developed for. RESULTS Here, we showcase the use of the open source workflow RetroPath2.0. First, we mathematically prove that we can generate all structural isomers of a molecule using a reduced set of reaction rules. We then use this enumeration strategy to screen the chemical space around a set of monomers and predict their glass transition temperatures, as well as around aminoglycosides to search structures maximizing antibacterial activity. We also perform a screening around aminoglycosides with enzymatic reaction rules to ensure biosynthetic accessibility. We finally use our workflow on an E. coli model to complete E. coli metabolome, with novel molecules generated using promiscuous enzymatic reaction rules. These novel molecules are searched on the MS spectra of an E. coli cell lysate interfacing our workflow with OpenMS through the KNIME Analytics Platform. CONCLUSION We provide an easy to use and modify, modular, and open-source workflow. We demonstrate its versatility through a variety of use cases including molecular structure enumeration, virtual screening in the chemical space, and metabolome completion. Because it is open source and freely available on MyExperiment.org, workflow community contributions should likely expand further the features of the tool, even beyond the use cases presented in the paper.
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Affiliation(s)
- Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Pablo Carbonell
- SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France. .,SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. .,CNRS-UMR8030/Laboratoire iSSB, Université Paris-Saclay, 91000, Évry, France.
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Wang L, Dash S, Ng CY, Maranas CD. A review of computational tools for design and reconstruction of metabolic pathways. Synth Syst Biotechnol 2017; 2:243-252. [PMID: 29552648 PMCID: PMC5851934 DOI: 10.1016/j.synbio.2017.11.002] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/06/2017] [Accepted: 11/06/2017] [Indexed: 11/28/2022] Open
Abstract
Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
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19
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Abstract
Systems metabolic engineering, which recently emerged as metabolic engineering integrated with systems biology, synthetic biology, and evolutionary engineering, allows engineering of microorganisms on a systemic level for the production of valuable chemicals far beyond its native capabilities. Here, we review the strategies for systems metabolic engineering and particularly its applications in Escherichia coli. First, we cover the various tools developed for genetic manipulation in E. coli to increase the production titers of desired chemicals. Next, we detail the strategies for systems metabolic engineering in E. coli, covering the engineering of the native metabolism, the expansion of metabolism with synthetic pathways, and the process engineering aspects undertaken to achieve higher production titers of desired chemicals. Finally, we examine a couple of notable products as case studies produced in E. coli strains developed by systems metabolic engineering. The large portfolio of chemical products successfully produced by engineered E. coli listed here demonstrates the sheer capacity of what can be envisioned and achieved with respect to microbial production of chemicals. Systems metabolic engineering is no longer in its infancy; it is now widely employed and is also positioned to further embrace next-generation interdisciplinary principles and innovation for its upgrade. Systems metabolic engineering will play increasingly important roles in developing industrial strains including E. coli that are capable of efficiently producing natural and nonnatural chemicals and materials from renewable nonfood biomass.
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20
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Blaß LK, Weyler C, Heinzle E. Network design and analysis for multi-enzyme biocatalysis. BMC Bioinformatics 2017; 18:366. [PMID: 28797226 PMCID: PMC5553788 DOI: 10.1186/s12859-017-1773-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 07/30/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. RESULTS We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. CONCLUSION We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.
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Affiliation(s)
- Lisa Katharina Blaß
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Christian Weyler
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Elmar Heinzle
- Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany.
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21
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Sankar A, Ranu S, Raman K. Predicting novel metabolic pathways through subgraph mining. Bioinformatics 2017; 33:3955-3963. [DOI: 10.1093/bioinformatics/btx481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aravind Sankar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Sayan Ranu
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
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22
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Chao R, Mishra S, Si T, Zhao H. Engineering biological systems using automated biofoundries. Metab Eng 2017; 42:98-108. [PMID: 28602523 PMCID: PMC5544601 DOI: 10.1016/j.ymben.2017.06.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022]
Abstract
Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.
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Affiliation(s)
- Ran Chao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Shekhar Mishra
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tong Si
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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23
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Chen X, Gao C, Guo L, Hu G, Luo Q, Liu J, Nielsen J, Chen J, Liu L. DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals. Chem Rev 2017; 118:4-72. [DOI: 10.1021/acs.chemrev.6b00804] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Xiulai Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liang Guo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guipeng Hu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Qiuling Luo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jia Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jens Nielsen
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark
| | - Jian Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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24
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Purdy HM, Reed JL. Evaluating the capabilities of microbial chemical production using genome-scale metabolic models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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25
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Arthur LL, Chung JJ, Jankirama P, Keefer KM, Kolotilin I, Pavlovic-Djuranovic S, Chalker DL, Grbic V, Green R, Menassa R, True HL, Skeath JB, Djuranovic S. Rapid generation of hypomorphic mutations. Nat Commun 2017; 8:14112. [PMID: 28106166 PMCID: PMC5263891 DOI: 10.1038/ncomms14112] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 11/30/2016] [Indexed: 01/05/2023] Open
Abstract
Hypomorphic mutations are a valuable tool for both genetic analysis of gene function and for synthetic biology applications. However, current methods to generate hypomorphic mutations are limited to a specific organism, change gene expression unpredictably, or depend on changes in spatial-temporal expression of the targeted gene. Here we present a simple and predictable method to generate hypomorphic mutations in model organisms by targeting translation elongation. Adding consecutive adenosine nucleotides, so-called polyA tracks, to the gene coding sequence of interest will decrease translation elongation efficiency, and in all tested cell cultures and model organisms, this decreases mRNA stability and protein expression. We show that protein expression is adjustable independent of promoter strength and can be further modulated by changing sequence features of the polyA tracks. These characteristics make this method highly predictable and tractable for generation of programmable allelic series with a range of expression levels.
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Affiliation(s)
- Laura L. Arthur
- Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Joyce J. Chung
- Department of Biology, Washington University, St Louis, Missouri 63105, USA
| | - Preetam Jankirama
- Department of Biology, The University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada N6A5B7
- Science and Technology Branch, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario, Canada N5V4T3
| | - Kathryn M. Keefer
- Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Igor Kolotilin
- Scattered Gold Biotechnology Inc. 14 Denali Terrace, London, Ontario, Canada N5X 3W2
| | - Slavica Pavlovic-Djuranovic
- Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Douglas L. Chalker
- Department of Biology, Washington University, St Louis, Missouri 63105, USA
| | - Vojislava Grbic
- Department of Biology, The University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada N6A5B7
| | - Rachel Green
- Department of Molecular Biology and Genetics, Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, 725 North Wolfe Street, Baltimore, Maryland 21205, USA
| | - Rima Menassa
- Science and Technology Branch, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario, Canada N5V4T3
| | - Heather L. True
- Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
- The Hope Center for Neurological Diseases, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - James B. Skeath
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Sergej Djuranovic
- Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
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26
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Chen Z, Zeng AP. Protein engineering approaches to chemical biotechnology. Curr Opin Biotechnol 2016; 42:198-205. [DOI: 10.1016/j.copbio.2016.07.007] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Revised: 06/10/2016] [Accepted: 07/30/2016] [Indexed: 01/09/2023]
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27
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A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals. Biochem Soc Trans 2016; 43:1151-6. [PMID: 26614653 DOI: 10.1042/bst20150144] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry.
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28
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Making sense of genomes of parasitic worms: Tackling bioinformatic challenges. Biotechnol Adv 2016; 34:663-686. [DOI: 10.1016/j.biotechadv.2016.03.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/25/2016] [Accepted: 03/01/2016] [Indexed: 01/25/2023]
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29
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Julien-Laferrière A, Bulteau L, Parrot D, Marchetti-Spaccamela A, Stougie L, Vinga S, Mary A, Sagot MF. A Combinatorial Algorithm for Microbial Consortia Synthetic Design. Sci Rep 2016; 6:29182. [PMID: 27373593 PMCID: PMC4931573 DOI: 10.1038/srep29182] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/07/2016] [Indexed: 11/16/2022] Open
Abstract
Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.
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Affiliation(s)
- Alice Julien-Laferrière
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Laurent Bulteau
- Université Paris-Est, LIGM (UMR 8049), CNRS, UPEM, ESIEE Paris, ENPC, F-77454, Marne-la-Vallée, France
| | - Delphine Parrot
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Alberto Marchetti-Spaccamela
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- Sapienza University of Rome, Italy
| | - Leen Stougie
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- VU University and CWI, Amsterdam, The Netherlands
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - Arnaud Mary
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Marie-France Sagot
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
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30
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31
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Mellor J, Grigoras I, Carbonell P, Faulon JL. Semisupervised Gaussian Process for Automated Enzyme Search. ACS Synth Biol 2016; 5:518-28. [PMID: 27007080 DOI: 10.1021/acssynbio.5b00294] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Synthetic biology is today harnessing the design of novel and greener biosynthesis routes for the production of added-value chemicals and natural products. The design of novel pathways often requires a detailed selection of enzyme sequences to import into the chassis at each of the reaction steps. To address such design requirements in an automated way, we present here a tool for exploring the space of enzymatic reactions. Given a reaction and an enzyme the tool provides a probability estimate that the enzyme catalyzes the reaction. Our tool first considers the similarity of a reaction to known biochemical reactions with respect to signatures around their reaction centers. Signatures are defined based on chemical transformation rules by using extended connectivity fingerprint descriptors. A semisupervised Gaussian process model associated with the similar known reactions then provides the probability estimate. The Gaussian process model uses information about both the reaction and the enzyme in providing the estimate. These estimates were validated experimentally by the application of the Gaussian process model to a newly identified metabolite in Escherichia coli in order to search for the enzymes catalyzing its associated reactions. Furthermore, we show with several pathway design examples how such ability to assign probability estimates to enzymatic reactions provides the potential to assist in bioengineering applications, providing experimental validation to our proposed approach. To the best of our knowledge, the proposed approach is the first application of Gaussian processes dealing with biological sequences and chemicals, the use of a semisupervised Gaussian process framework is also novel in the context of machine learning applied to bioinformatics. However, the ability of an enzyme to catalyze a reaction depends on the affinity between the substrates of the reaction and the enzyme. This affinity is generally quantified by the Michaelis constant KM. Therefore, we also demonstrate using Gaussian process regression to predict KM given a substrate-enzyme pair.
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Affiliation(s)
- Joseph Mellor
- School
of Chemistry, University of Manchester, Manchester M13 9PL, U.K
- Manchester
Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
| | - Ioana Grigoras
- iSSB,
Institute of Systems and Synthetic Biology, CNRS, University of Évry-Val-d’Essonne, 91000 Évry, France
| | - Pablo Carbonell
- SYNBIOCHEM
Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
| | - Jean-Loup Faulon
- School
of Chemistry, University of Manchester, Manchester M13 9PL, U.K
- iSSB,
Institute of Systems and Synthetic Biology, CNRS, University of Évry-Val-d’Essonne, 91000 Évry, France
- SYNBIOCHEM
Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
- MICALIS Institute, INRA, 78352 Jouy en Jossas, France
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32
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Oberhardt MA, Zarecki R, Reshef L, Xia F, Duran-Frigola M, Schreiber R, Henry CS, Ben-Tal N, Dwyer DJ, Gophna U, Ruppin E. Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli. PLoS Comput Biol 2016; 12:e1004705. [PMID: 26821166 PMCID: PMC4731195 DOI: 10.1371/journal.pcbi.1004705] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 12/14/2015] [Indexed: 11/18/2022] Open
Abstract
Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.
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Affiliation(s)
- Matthew A. Oberhardt
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
- Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, Maryland, United States of America
- * E-mail: (MAO); (ER)
| | - Raphy Zarecki
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Leah Reshef
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Fangfang Xia
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, United States of America
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain
| | - Rachel Schreiber
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Christopher S. Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, United States of America
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Daniel J. Dwyer
- Department of Cell Biology and Molecular Genetics, Institute for Physical Science and Technology, Department of Bioengineering, Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, United States of America
| | - Uri Gophna
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Eytan Ruppin
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, Maryland, United States of America
- * E-mail: (MAO); (ER)
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33
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Morgado G, Gerngross D, Roberts TM, Panke S. Synthetic Biology for Cell-Free Biosynthesis: Fundamentals of Designing Novel In Vitro Multi-Enzyme Reaction Networks. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 162:117-146. [PMID: 27757475 DOI: 10.1007/10_2016_13] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cell-free biosynthesis in the form of in vitro multi-enzyme reaction networks or enzyme cascade reactions emerges as a promising tool to carry out complex catalysis in one-step, one-vessel settings. It combines the advantages of well-established in vitro biocatalysis with the power of multi-step in vivo pathways. Such cascades have been successfully applied to the synthesis of fine and bulk chemicals, monomers and complex polymers of chemical importance, and energy molecules from renewable resources as well as electricity. The scale of these initial attempts remains small, suggesting that more robust control of such systems and more efficient optimization are currently major bottlenecks. To this end, the very nature of enzyme cascade reactions as multi-membered systems requires novel approaches for implementation and optimization, some of which can be obtained from in vivo disciplines (such as pathway refactoring and DNA assembly), and some of which can be built on the unique, cell-free properties of cascade reactions (such as easy analytical access to all system intermediates to facilitate modeling).
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Affiliation(s)
- Gaspar Morgado
- Bioprocess Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Daniel Gerngross
- Bioprocess Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Tania M Roberts
- Bioprocess Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Sven Panke
- Bioprocess Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
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Sorokina M, Medigue C, Vallenet D. A new network representation of the metabolism to detect chemical transformation modules. BMC Bioinformatics 2015; 16:385. [PMID: 26573681 PMCID: PMC4647279 DOI: 10.1186/s12859-015-0809-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 10/29/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RESULTS RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. CONCLUSIONS This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.
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Affiliation(s)
- Maria Sorokina
- Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France.
- CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France.
- UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France.
| | - Claudine Medigue
- Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France.
- CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France.
- UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France.
| | - David Vallenet
- Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France.
- CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France.
- UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France.
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Tu W, Zhang H, Liu J, Hu QN. BioSynther: a customized biosynthetic potential explorer. Bioinformatics 2015; 32:472-3. [DOI: 10.1093/bioinformatics/btv599] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 10/12/2015] [Indexed: 11/13/2022] Open
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Advancing metabolic engineering through systems biology of industrial microorganisms. Curr Opin Biotechnol 2015; 36:8-15. [PMID: 26318074 DOI: 10.1016/j.copbio.2015.08.006] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 08/06/2015] [Accepted: 08/09/2015] [Indexed: 11/21/2022]
Abstract
Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further.
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Fehér T, Libis V, Carbonell P, Faulon JL. A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli. Front Bioeng Biotechnol 2015; 3:46. [PMID: 25905101 PMCID: PMC4389729 DOI: 10.3389/fbioe.2015.00046] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Accepted: 03/23/2015] [Indexed: 01/26/2023] Open
Abstract
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose–response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor’s use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.
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Affiliation(s)
- Tamás Fehér
- Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences , Szeged , Hungary
| | - Vincent Libis
- Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Paris Diderot University , Paris , France
| | - Pablo Carbonell
- Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra , Barcelona , Spain ; SYNBIOCHEM Center, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester , Manchester , UK
| | - Jean-Loup Faulon
- Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; SYNBIOCHEM Center, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester , Manchester , UK
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Currin A, Swainston N, Day PJ, Kell DB. Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem Soc Rev 2015; 44:1172-239. [PMID: 25503938 PMCID: PMC4349129 DOI: 10.1039/c4cs00351a] [Citation(s) in RCA: 251] [Impact Index Per Article: 27.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Indexed: 12/21/2022]
Abstract
The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
| | - Neil Swainston
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- School of Computer Science , The University of Manchester , Manchester M13 9PL , UK
| | - Philip J. Day
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , 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 , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
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Kim B, Kim WJ, Kim DI, Lee SY. Applications of genome-scale metabolic network model in metabolic engineering. ACTA ACUST UNITED AC 2015; 42:339-48. [DOI: 10.1007/s10295-014-1554-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 11/19/2014] [Indexed: 12/11/2022]
Abstract
Abstract
Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.
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Affiliation(s)
- Byoungjin Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Won Jun Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Dong In Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Sang Yup Lee
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
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40
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Li M, Borodina I. Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae. FEMS Yeast Res 2015; 15:1-12. [PMID: 25238571 DOI: 10.1111/1567-1364.12213] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 05/13/2014] [Accepted: 09/15/2014] [Indexed: 11/29/2022] Open
Abstract
Synthetic biology and metabolic engineering enable generation of novel cell factories that efficiently convert renewable feedstocks into biofuels, bulk, and fine chemicals, thus creating the basis for biosustainable economy independent on fossil resources. While over a hundred proof-of-concept chemicals have been made in yeast, only a very small fraction of those has reached commercial-scale production so far. The limiting factor is the high research cost associated with the development of a robust cell factory that can produce the desired chemical at high titer, rate, and yield. Synthetic biology has the potential to bring down this cost by improving our ability to predictably engineer biological systems. This review highlights synthetic biology applications for design, assembly, and optimization of non-native biochemical pathways in baker's yeast Saccharomyces cerevisiae We describe computational tools for the prediction of biochemical pathways, molecular biology methods for assembly of DNA parts into pathways, and for introducing the pathways into the host, and finally approaches for optimizing performance of the introduced pathways.
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Affiliation(s)
- Mingji Li
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | - Irina Borodina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
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41
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Long MR, Ong WK, Reed JL. Computational methods in metabolic engineering for strain design. Curr Opin Biotechnol 2015; 34:135-41. [PMID: 25576846 DOI: 10.1016/j.copbio.2014.12.019] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Revised: 12/17/2014] [Accepted: 12/18/2014] [Indexed: 11/28/2022]
Abstract
Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms.
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Affiliation(s)
- Matthew R Long
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Wai Kit Ong
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, United States; Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, United States
| | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, United States; Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, United States.
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42
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Muschiol J, Peters C, Oberleitner N, Mihovilovic MD, Bornscheuer UT, Rudroff F. Cascade catalysis – strategies and challenges en route to preparative synthetic biology. Chem Commun (Camb) 2015; 51:5798-811. [DOI: 10.1039/c4cc08752f] [Citation(s) in RCA: 251] [Impact Index Per Article: 27.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In this feature article recent progress and future perspectives of cascade catalysis combining bio/bio or bio/chemo catalysts are presented.
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Affiliation(s)
- Jan Muschiol
- Institute of Biochemistry
- Dept. of Biotechnology & Enzyme Catalysis
- Greifswald University
- 17489 Greifswald
- Germany
| | - Christin Peters
- Institute of Biochemistry
- Dept. of Biotechnology & Enzyme Catalysis
- Greifswald University
- 17489 Greifswald
- Germany
| | - Nikolin Oberleitner
- Institute of Applied Synthetic Chemistry
- Vienna University of Technology
- 1060 Vienna
- Austria
| | - Marko D. Mihovilovic
- Institute of Applied Synthetic Chemistry
- Vienna University of Technology
- 1060 Vienna
- Austria
| | - Uwe T. Bornscheuer
- Institute of Biochemistry
- Dept. of Biotechnology & Enzyme Catalysis
- Greifswald University
- 17489 Greifswald
- Germany
| | - Florian Rudroff
- Institute of Applied Synthetic Chemistry
- Vienna University of Technology
- 1060 Vienna
- Austria
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43
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Singh V, Mani I, Chaudhary DK. Metabolic Engineering of Microorganisms for Biosynthesis of Antibiotics. SYSTEMS AND SYNTHETIC BIOLOGY 2015. [DOI: 10.1007/978-94-017-9514-2_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abstract
Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.
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45
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Sajo Mienda B, Shahir Shamsir M. An overview of pathway prediction tools for synthetic design of microbial chemical factories. AIMS BIOENGINEERING 2015. [DOI: 10.3934/bioeng.2015.1.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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46
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Hara KY, Araki M, Okai N, Wakai S, Hasunuma T, Kondo A. Development of bio-based fine chemical production through synthetic bioengineering. Microb Cell Fact 2014; 13:173. [PMID: 25494636 PMCID: PMC4302092 DOI: 10.1186/s12934-014-0173-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 11/23/2014] [Indexed: 01/23/2023] Open
Abstract
Fine chemicals that are physiologically active, such as pharmaceuticals, cosmetics, nutritional supplements, flavoring agents as well as additives for foods, feed, and fertilizer are produced by enzymatically or through microbial fermentation. The identification of enzymes that catalyze the target reaction makes possible the enzymatic synthesis of the desired fine chemical. The genes encoding these enzymes are then introduced into suitable microbial hosts that are cultured with inexpensive, naturally abundant carbon sources, and other nutrients. Metabolic engineering create efficient microbial cell factories for producing chemicals at higher yields. Molecular genetic techniques are then used to optimize metabolic pathways of genetically and metabolically well-characterized hosts. Synthetic bioengineering represents a novel approach to employ a combination of computer simulation and metabolic analysis to design artificial metabolic pathways suitable for mass production of target chemicals in host strains. In the present review, we summarize recent studies on bio-based fine chemical production and assess the potential of synthetic bioengineering for further improving their productivity.
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Affiliation(s)
- Kiyotaka Y Hara
- Organization of Advanced Science and Technology, Kobe University, Nada, Kobe, Japan.
| | - Michihiro Araki
- Organization of Advanced Science and Technology, Kobe University, Nada, Kobe, Japan.
| | - Naoko Okai
- Organization of Advanced Science and Technology, Kobe University, Nada, Kobe, Japan.
| | - Satoshi Wakai
- Organization of Advanced Science and Technology, Kobe University, Nada, Kobe, Japan.
| | - Tomohisa Hasunuma
- Organization of Advanced Science and Technology, Kobe University, Nada, Kobe, Japan.
| | - Akihiko Kondo
- Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe, 657-8501, Japan.
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Fehér T, Planson AG, Carbonell P, Fernández-Castané A, Grigoras I, Dariy E, Perret A, Faulon JL. Validation of RetroPath, a computer-aided design tool for metabolic pathway engineering. Biotechnol J 2014; 9:1446-57. [PMID: 25224453 DOI: 10.1002/biot.201400055] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2014] [Revised: 07/28/2014] [Accepted: 09/15/2014] [Indexed: 01/29/2023]
Abstract
Metabolic engineering has succeeded in biosynthesis of numerous commodity or high value compounds. However, the choice of pathways and enzymes used for production was many times made ad hoc, or required expert knowledge of the specific biochemical reactions. In order to rationalize the process of engineering producer strains, we developed the computer-aided design (CAD) tool RetroPath that explores and enumerates metabolic pathways connecting the endogenous metabolites of a chassis cell to the target compound. To experimentally validate our tool, we constructed 12 top-ranked enzyme combinations producing the flavonoid pinocembrin, four of which displayed significant yields. Namely, our tool queried the enzymes found in metabolic databases based on their annotated and predicted activities. Next, it ranked pathways based on the predicted efficiency of the available enzymes, the toxicity of the intermediate metabolites and the calculated maximum product flux. To implement the top-ranking pathway, our procedure narrowed down a list of nine million possible enzyme combinations to 12, a number easily assembled and tested. One round of metabolic network optimization based on RetroPath output further increased pinocembrin titers 17-fold. In total, 12 out of the 13 enzymes tested in this work displayed a relative performance that was in accordance with its predicted score. These results validate the ranking function of our CAD tool, and open the way to its utilization in the biosynthesis of novel compounds.
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Affiliation(s)
- Tamás Fehér
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Evry Cedex, France
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Zanghellini A. de novo computational enzyme design. Curr Opin Biotechnol 2014; 29:132-8. [DOI: 10.1016/j.copbio.2014.03.002] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 03/10/2014] [Indexed: 11/25/2022]
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49
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Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. Metab Eng 2014; 25:140-58. [DOI: 10.1016/j.ymben.2014.07.009] [Citation(s) in RCA: 139] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 07/17/2014] [Accepted: 07/21/2014] [Indexed: 11/17/2022]
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50
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Carbonell P, Parutto P, Baudier C, Junot C, Faulon JL. Retropath: automated pipeline for embedded metabolic circuits. ACS Synth Biol 2014; 3:565-77. [PMID: 24131345 DOI: 10.1021/sb4001273] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolic circuits are a promising alternative to other conventional genetic circuits as modular parts implementing functionalities required for synthetic biology applications. To date, metabolic design has been mainly focused on production circuits. Emergent applications such as smart therapeutics, however, require circuits that enable sensing and regulation. Here, we present RetroPath, an automated pipeline for embedded metabolic circuits that explores the circuit design space from a given set of specifications and selects the best circuits to implement based on desired constraints. Synthetic biology circuits embedded in a chassis organism that are capable of controlling the production, processing, sensing, and the release of specific molecules were enumerated in the metabolic space through a standard procedure. In that way, design and implementation of applications such as therapeutic circuits that autonomously diagnose and treat disease, are enabled, and their optimization is streamlined.
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