1
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization. Microb Cell Fact 2024; 23:37. [PMID: 38287320 PMCID: PMC10823710 DOI: 10.1186/s12934-023-02277-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/15/2023] [Indexed: 01/31/2024] Open
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .
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Affiliation(s)
- Leila Hassani
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Mohammad R Moosavi
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Payam Setoodeh
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran
- Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
| | - Habil Zare
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, USA.
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2
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Kim WJ, Lee Y, Kim HU, Ryu JY, Yang JE, Lee SY. Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes. Cell Syst 2023; 14:990-1001.e5. [PMID: 37935194 DOI: 10.1016/j.cels.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 05/23/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023]
Abstract
In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets. Using iBridge, overexpression and downregulation targets are suggested for the production of 298 chemicals and validated for 36 chemicals experimentally demonstrated in previous studies. Finally, iBridge is employed to engineer Escherichia coli strains capable of producing 10.3 g/L of D-panthenol, a compound not previously produced, as well as putrescine and 4-hydroxyphenyllactate at enhanced titers, 63.7 and 8.3 g/L, respectively.
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Affiliation(s)
- Won Jun Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea
| | - Youngjoon Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea
| | - Hyun Uk Kim
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea; Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
| | - Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Jung Eun Yang
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.
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3
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization. RESEARCH SQUARE 2023:rs.3.rs-3126389. [PMID: 37503204 PMCID: PMC10371132 DOI: 10.21203/rs.3.rs-3126389/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.
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Affiliation(s)
| | | | | | - Habil Zare
- University of Texas Health Science Center
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4
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Ibrahim M, Raajaraam L, Raman K. Modelling microbial communities: Harnessing consortia for biotechnological applications. Comput Struct Biotechnol J 2021; 19:3892-3907. [PMID: 34584635 PMCID: PMC8441623 DOI: 10.1016/j.csbj.2021.06.048] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023] Open
Abstract
Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.
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Affiliation(s)
- Maziya Ibrahim
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lavanya Raajaraam
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
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5
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Tian M, Reed JL. Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. Bioinformatics 2019; 34:3882-3888. [PMID: 29878053 DOI: 10.1093/bioinformatics/bty445] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 06/01/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models. Results In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA. Availability and implementation Code is available in the Supplementary data available at Bioinformatics online. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingyuan Tian
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jennifer L Reed
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
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6
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O'Brien C, Allman A, Daoutidis P, Hu WS. Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations. Metab Eng 2019; 56:154-164. [PMID: 31400493 DOI: 10.1016/j.ymben.2019.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/05/2019] [Accepted: 08/06/2019] [Indexed: 12/17/2022]
Abstract
Pathway engineering is a powerful tool in biotechnological and clinical applications. However, many phenomena cannot be rewired with a single enzyme change, and in a complex network like energy metabolism, the selection of combinations of targets to engineer is a daunting task. To facilitate this process, we have developed an optimization framework and applied it to a mechanistic kinetic model of energy metabolism. We then identified combinations of enzyme alternations that led to the elimination of the Warburg effect seen in the metabolism of cancer cells and cell lines, a phenomenon coupling rapid proliferation to lactate production. Typically, optimization approaches use integer variables to achieve the desired flux redistribution with a minimum number of altered genes. This framework uses convex penalty terms to replace these integer variables and improve computational tractability. Optimal solutions are identified which substantially reduce or eliminate lactate production while maintaining the requirements for cellular proliferation using three or more enzymes.
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Affiliation(s)
- Conor O'Brien
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Andrew Allman
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA.
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7
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Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design. Methods Mol Biol 2018; 1671:63-82. [PMID: 29170953 DOI: 10.1007/978-1-4939-7295-1_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.
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8
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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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9
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Hassanpour N, Ullah E, Yousofshahi M, Nair NU, Hassoun S. Selection Finder (SelFi): A computational metabolic engineering tool to enable directed evolution of enzymes. Metab Eng Commun 2017; 4:37-47. [PMID: 29468131 PMCID: PMC5779715 DOI: 10.1016/j.meteno.2017.02.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 10/30/2016] [Accepted: 02/27/2017] [Indexed: 11/26/2022] Open
Abstract
Directed evolution of enzymes consists of an iterative process of creating mutant libraries and choosing desired phenotypes through screening or selection until the enzymatic activity reaches a desired goal. The biggest challenge in directed enzyme evolution is identifying high-throughput screens or selections to isolate the variant(s) with the desired property. We present in this paper a computational metabolic engineering framework, Selection Finder (SelFi), to construct a selection pathway from a desired enzymatic product to a cellular host and to couple the pathway with cell survival. We applied SelFi to construct selection pathways for four enzymes and their desired enzymatic products xylitol, D-ribulose-1,5-bisphosphate, methanol, and aniline. Two of the selection pathways identified by SelFi were previously experimentally validated for engineering Xylose Reductase and RuBisCO. Importantly, SelFi advances directed evolution of enzymes as there is currently no known generalized strategies or computational techniques for identifying high-throughput selections for engineering enzymes.
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Affiliation(s)
- Neda Hassanpour
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Ehsan Ullah
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Mona Yousofshahi
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Nikhil U Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, United States
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA 02155, United States.,Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, United States
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10
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Machado D, Herrgård MJ, Rocha I. Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction. PLoS Comput Biol 2016; 12:e1005140. [PMID: 27711110 PMCID: PMC5053500 DOI: 10.1371/journal.pcbi.1005140] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 09/13/2016] [Indexed: 12/05/2022] Open
Abstract
Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.
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Affiliation(s)
- Daniel Machado
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Horsølm, Denmark
| | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
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11
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Yeast metabolic chassis designs for diverse biotechnological products. Sci Rep 2016; 6:29694. [PMID: 27430744 PMCID: PMC4949481 DOI: 10.1038/srep29694] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 06/21/2016] [Indexed: 02/07/2023] Open
Abstract
The diversity of industrially important molecules for which microbial production routes have been experimentally demonstrated is rapidly increasing. The development of economically viable producer cells is, however, lagging behind, as it requires substantial engineering of the host metabolism. A chassis strain suitable for production of a range of molecules is therefore highly sought after but remains elusive. Here, we propose a genome-scale metabolic modeling approach to design chassis strains of Saccharomyces cerevisiae - a widely used microbial cell factory. For a group of 29 products covering a broad range of biochemistry and applications, we identified modular metabolic engineering strategies for re-routing carbon flux towards the desired product. We find distinct product families with shared targets forming the basis for the corresponding chassis cells. The design strategies include overexpression targets that group products by similarity in precursor and cofactor requirements, as well as gene deletion strategies for growth-product coupling that lead to non-intuitive product groups. Our results reveal the extent and the nature of flux re-routing necessary for producing a diverse range of products in a widely used cell factory and provide blueprints for constructing pre-optimized chassis strains.
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12
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Fu W, Wichuk K, Brynjólfsson S. Developing diatoms for value-added products: challenges and opportunities. N Biotechnol 2015; 32:547-51. [DOI: 10.1016/j.nbt.2015.03.016] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 03/17/2015] [Accepted: 03/28/2015] [Indexed: 11/29/2022]
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13
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In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories. Microbiol Mol Biol Rev 2015; 80:45-67. [PMID: 26609052 DOI: 10.1128/mmbr.00014-15] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.
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14
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Liu R, Bassalo MC, Zeitoun RI, Gill RT. Genome scale engineering techniques for metabolic engineering. Metab Eng 2015; 32:143-154. [PMID: 26453944 DOI: 10.1016/j.ymben.2015.09.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 08/15/2015] [Accepted: 09/02/2015] [Indexed: 12/18/2022]
Abstract
Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.
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Affiliation(s)
- Rongming Liu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Marcelo C Bassalo
- Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ramsey I Zeitoun
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ryan T Gill
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
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15
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Co-evolution of strain design methods based on flux balance and elementary mode analysis. Metab Eng Commun 2015; 2:85-92. [PMID: 34150512 PMCID: PMC8193246 DOI: 10.1016/j.meteno.2015.04.001] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 04/17/2015] [Accepted: 04/29/2015] [Indexed: 01/16/2023] Open
Abstract
More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last 10 years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications. Computational strain design methods are divided into two main families. We trace the evolutionary history of these two families. Surveyed successful cases of model-guided strain design for industrial applications. Most proposed methods have not yet been tested in real applications. Agreement between in silico and in vivo results shows potential of tested methods.
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16
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Mahadevan R, von Kamp A, Klamt S. Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 2015; 31:2844-51. [PMID: 25913205 DOI: 10.1093/bioinformatics/btv217] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 04/16/2015] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype. RESULTS We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals. AVAILABILITY AND IMPLEMENTATION MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html. CONTACT krishna.mahadevan@utoronto.ca or klamt@mpi-magdeburg.mpg.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S3E5, Canada, Institute of Biomaterials and Biomedical Engineering, Toronto, ON, M5S 3G9, Canada and
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, D-39106, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, D-39106, Germany
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17
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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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18
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Matsuoka Y, Shimizu K. Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. BIORESOUR BIOPROCESS 2015. [DOI: 10.1186/s40643-014-0031-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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19
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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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20
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Khodayari A, Chowdhury A, Maranas CD. Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model. Front Bioeng Biotechnol 2015; 2:76. [PMID: 25601910 PMCID: PMC4283520 DOI: 10.3389/fbioe.2014.00076] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Accepted: 12/05/2014] [Indexed: 01/25/2023] Open
Abstract
Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.
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Affiliation(s)
- Ali Khodayari
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Anupam Chowdhury
- 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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21
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Chowdhury A, Zomorrodi AR, Maranas CD. Bilevel optimization techniques in computational strain design. Comput Chem Eng 2015. [DOI: 10.1016/j.compchemeng.2014.06.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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22
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Gudmundsson S, Nogales J. Cyanobacteria as photosynthetic biocatalysts: a systems biology perspective. MOLECULAR BIOSYSTEMS 2015; 11:60-70. [DOI: 10.1039/c4mb00335g] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A review of cyanobacterial biocatalysts highlighting their metabolic features that argues for the need for systems-level metabolic engineering.
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Affiliation(s)
| | - Juan Nogales
- Department of Environmental Biology
- Centro de Investigaciones Biológicas-CSIC
- 28040 Madrid
- Spain
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23
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Yang L, Srinivasan S, Mahadevan R, Cluett WR. Characterizing metabolic pathway diversification in the context of perturbation size. Metab Eng 2014; 28:114-122. [PMID: 25542850 DOI: 10.1016/j.ymben.2014.11.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Revised: 11/08/2014] [Accepted: 11/11/2014] [Indexed: 11/30/2022]
Abstract
Cell metabolism is an important platform for sustainable biofuel, chemical and pharmaceutical production but its complexity presents a major challenge for scientists and engineers. Although in silico strains have been designed in the past with predicted performances near the theoretical maximum, real-world performance is often sub-optimal. Here, we simulate how strain performance is impacted when subjected to many randomly varying perturbations, including discrepancies between gene expression and in vivo flux, osmotic stress, and substrate uptake perturbations due to concentration gradients in bioreactors. This computational study asks whether robust performance can be achieved by adopting robustness-enhancing mechanisms from naturally evolved organisms-in particular, redundancy. Our study shows that redundancy, typically perceived as a ubiquitous robustness-enhancing strategy in nature, can either improve or undermine robustness depending on the magnitude of the perturbations. We also show that the optimal number of redundant pathways used can be predicted for a given perturbation size.
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Affiliation(s)
- Laurence Yang
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, Canada M5S 3E5
| | - Shyamsundhar Srinivasan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, Canada M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, Canada M5S 3E5; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada M5S 3G9.
| | - William R Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, Canada M5S 3E5
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24
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Tervo CJ, Reed JL. Expanding Metabolic Engineering Algorithms Using Feasible Space and Shadow Price Constraint Modules. Metab Eng Commun 2014; 1:1-11. [PMID: 25478320 PMCID: PMC4249821 DOI: 10.1016/j.meteno.2014.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
While numerous computational methods have been developed that use genome-scale models to propose mutants for the purpose of metabolic engineering, they generally compare mutants based on a single criteria (e.g., production rate at a mutant׳s maximum growth rate). As such, these approaches remain limited in their ability to include multiple complex engineering constraints. To address this shortcoming, we have developed feasible space and shadow price constraint (FaceCon and ShadowCon) modules that can be added to existing mixed integer linear adaptive evolution metabolic engineering algorithms, such as OptKnock and OptORF. These modules allow strain designs to be identified amongst a set of multiple metabolic engineering algorithm solutions that are capable of high chemical production while also satisfying additional design criteria. We describe the various module implementations and their potential applications to the field of metabolic engineering. We then incorporated these modules into the OptORF metabolic engineering algorithm. Using an Escherichia coli genome-scale model (iJO1366), we generated different strain designs for the anaerobic production of ethanol from glucose, thus demonstrating the tractability and potential utility of these modules in metabolic engineering algorithms. Added modules to impose multiple design criteria for engineering algorithms. Examples are provided to eliminate by-product secretion. Examples are provided to control coupling between product and biomass formation. Modules are tractable for genome-scale design.
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Affiliation(s)
- Christopher J Tervo
- Department of Chemical and Biological Engineering, University of Wisconsin -Madison, Madison, WI 53706, USA
| | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin -Madison, Madison, WI 53706, USA
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25
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Liu H, Li Y, Wang X. OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites. QUANTITATIVE BIOLOGY 2014. [DOI: 10.1007/s40484-014-0033-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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26
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Fong SS. Computational approaches to metabolic engineering utilizing systems biology and synthetic biology. Comput Struct Biotechnol J 2014; 11:28-34. [PMID: 25379141 PMCID: PMC4212286 DOI: 10.1016/j.csbj.2014.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.
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Affiliation(s)
- Stephen S. Fong
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, 601 W. Main St., Richmond, VA 23284, United States
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27
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Multivariate modular metabolic engineering for pathway and strain optimization. Curr Opin Biotechnol 2014; 29:156-62. [PMID: 24927371 DOI: 10.1016/j.copbio.2014.05.005] [Citation(s) in RCA: 108] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 05/19/2014] [Indexed: 12/21/2022]
Abstract
Despite the potential in utilizing microbial fermentation for chemical production, the field of industrial biotechnology still lacks a standard, universally applicable principle for strain optimization. A key challenge has been in finding and applying effective ways to address metabolic flux imbalances. Strategies based on rational design require significant a priori knowledge and often fail to take a holistic view of cellular metabolism. Combinatorial approaches enable more global searches but require a high-throughput screen. Here, we present the recent advances and promises of a novel approach to metabolic pathway and strain optimization called multivariate modular metabolic engineering (MMME). In this technique, key enzymes are organized into distinct modules and simultaneously varied based on expression to balance flux through a pathway. Because of its simplicity and broad applicability, MMME has the potential to systematize and revolutionize the field of metabolic engineering and industrial biotechnology.
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28
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k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 2014; 10:e1003487. [PMID: 24586136 PMCID: PMC3930495 DOI: 10.1371/journal.pcbi.1003487] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 01/10/2014] [Indexed: 11/19/2022] Open
Abstract
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.
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29
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Glasgow JE, Tullman-Ercek D. Synthetic biologists spring into action at the 245th American Chemical Society National Meeting. ACS Synth Biol 2013; 2:293-5. [PMID: 24884108 DOI: 10.1021/sb400046t] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
As the field of synthetic biology continues to define itself, it has merged concepts from many related areas of research: molecular biology, genetics, bioengineering, and chemistry. At the 2013 Spring American Chemical Society National Meeting in New Orleans, LA, this mixture was manifested in a wealth of sessions emphasizing the use of modern synthetic biological approaches to solve many of today's biggest chemical problems. As a result of the field's diverse yet pervasive nature, synthetic biology concepts were present in several of the conferences many divisions, including Biological Chemistry, Biochemical Technology, Cellulose and Renewable Materials, and several others. Here we offer a snapshot of some of the exciting research discussed in the dedicated synthetic biology sessions throughout the week.
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Affiliation(s)
- Jeff E. Glasgow
- Department
of Chemistry and ‡Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720,
United States
| | - Danielle Tullman-Ercek
- Department
of Chemistry and ‡Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720,
United States
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30
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Alper HS, Wittmann C. Editorial: how multiplexed tools and approaches speed up the progress of metabolic engineering. Biotechnol J 2013; 8:506-7. [PMID: 23636973 DOI: 10.1002/biot.201300167] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Systems metabolic engineering is becoming a widely-evoked paradigm for industrial strain design and optimization. Specifically, systems wide experimental and computational analyses of cells and their environments enable guide metabolic engineers to quickly parse the genome and creating desirable overproduction phenotypes.
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Affiliation(s)
- Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, USA.
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