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Hadadi N, Hatzimanikatis V. Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Curr Opin Chem Biol 2015; 28:99-104. [DOI: 10.1016/j.cbpa.2015.06.025] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 06/16/2015] [Accepted: 06/21/2015] [Indexed: 12/28/2022]
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102
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Ng CY, Khodayari A, Chowdhury A, Maranas CD. Advances in de novo strain design using integrated systems and synthetic biology tools. Curr Opin Chem Biol 2015; 28:105-14. [DOI: 10.1016/j.cbpa.2015.06.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 06/13/2015] [Accepted: 06/21/2015] [Indexed: 11/17/2022]
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103
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Ataman M, Hatzimanikatis V. Heading in the right direction: thermodynamics-based network analysis and pathway engineering. Curr Opin Biotechnol 2015; 36:176-82. [PMID: 26360871 DOI: 10.1016/j.copbio.2015.08.021] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 08/11/2015] [Accepted: 08/18/2015] [Indexed: 11/28/2022]
Abstract
Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods.
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Affiliation(s)
- Meric Ataman
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland.
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104
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Petzold CJ, Chan LJG, Nhan M, Adams PD. Analytics for Metabolic Engineering. Front Bioeng Biotechnol 2015; 3:135. [PMID: 26442249 PMCID: PMC4561385 DOI: 10.3389/fbioe.2015.00135] [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: 07/07/2015] [Accepted: 08/24/2015] [Indexed: 12/20/2022] Open
Abstract
Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.
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Affiliation(s)
- Christopher J Petzold
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Leanne Jade G Chan
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Melissa Nhan
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Paul D Adams
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA ; Department of Bioengineering, University of California Berkeley , Berkeley, CA , USA
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105
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Cueto-Rojas HF, van Maris A, Wahl SA, Heijnen J. Thermodynamics-based design of microbial cell factories for anaerobic product formation. Trends Biotechnol 2015; 33:534-46. [DOI: 10.1016/j.tibtech.2015.06.010] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Revised: 05/20/2015] [Accepted: 06/23/2015] [Indexed: 11/29/2022]
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106
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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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107
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Chu HS, Ahn JH, Yun J, Choi IS, Nam TW, Cho KM. Direct fermentation route for the production of acrylic acid. Metab Eng 2015; 32:23-29. [PMID: 26319589 DOI: 10.1016/j.ymben.2015.08.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 07/30/2015] [Accepted: 08/18/2015] [Indexed: 11/18/2022]
Abstract
There have been growing concerns regarding the limited fossil resources and global climate changes resulting from modern civilization. Currently, finding renewable alternatives to conventional petrochemical processes has become one of the major focus areas of the global chemical industry sector. Since over 4.2 million tons of acrylic acid (AA) is annually employed for the manufacture of various products via petrochemical processes, this chemical has been the target of efforts to replace the petrochemical route by ecofriendly processes. However, there has been limited success in developing an approach combining the biological production of 3-hydroxypropionic acid (3-HP) and its chemical conversion to AA. Here, we report the first direct fermentative route for producing 0.12 g/L of AA from glucose via 3-HP, 3-HP-CoA, and Acryloyl-CoA, leading to a strain of Escherichia coli capable of directly producing acrylic acid. This route was developed through extensive screening of key enzymes and designing a novel metabolic pathway for AA.
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Affiliation(s)
- Hun Su Chu
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Jin-Ho Ahn
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Jiae Yun
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - In Suk Choi
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Tae-Wook Nam
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Kwang Myung Cho
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea.
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108
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Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation. Antonie van Leeuwenhoek 2015; 108:1075-90. [DOI: 10.1007/s10482-015-0561-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 08/18/2015] [Indexed: 12/22/2022]
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109
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O'Brien EJ, Monk JM, Palsson BO. Using Genome-scale Models to Predict Biological Capabilities. Cell 2015; 161:971-987. [PMID: 26000478 DOI: 10.1016/j.cell.2015.05.019] [Citation(s) in RCA: 439] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Indexed: 11/29/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.
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Affiliation(s)
- Edward J O'Brien
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark.
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110
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Zhuang KH, Herrgård MJ. Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production. Metab Eng 2015; 31:1-12. [PMID: 26116515 DOI: 10.1016/j.ymben.2015.05.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 05/06/2015] [Accepted: 05/26/2015] [Indexed: 10/23/2022]
Abstract
In recent years, bio-based chemicals have gained traction as a sustainable alternative to petrochemicals. However, despite rapid advances in metabolic engineering and synthetic biology, there remain significant economic and environmental challenges. In order to maximize the impact of research investment in a new bio-based chemical industry, there is a need for assessing the technological, economic, and environmental potentials of combinations of biomass feedstocks, biochemical products, bioprocess technologies, and metabolic engineering approaches in the early phase of development of cell factories. To address this issue, we have developed a comprehensive Multi-scale framework for modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes and economic impact assessment. We demonstrate the use of the MuSIC framework in a case study where two major polymer precursors (1,3-propanediol and 3-hydroxypropionic acid) are produced from two biomass feedstocks (corn-based glucose and soy-based glycerol) through 66 proposed biosynthetic pathways in two host organisms (Escherichia coli and Saccharomyces cerevisiae). The MuSIC framework allows exploration of tradeoffs and interactions between economy-scale objectives (e.g. profit maximization, emission minimization), constraints (e.g. land-use constraints) and process- and cell-scale technology choices (e.g. strain design or oxygenation conditions). We demonstrate that economy-scale assessment can be used to guide specific strain design decisions in metabolic engineering, and that these design decisions can be affected by non-intuitive dependencies across multiple scales.
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Affiliation(s)
- Kai H Zhuang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Alle 6, Hørsholm DK-2930, Denmark.
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Alle 6, Hørsholm DK-2930, Denmark.
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111
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On the feasibility of growth-coupled product synthesis in microbial strains. Metab Eng 2015; 30:166-178. [PMID: 26112955 DOI: 10.1016/j.ymben.2015.05.006] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 03/11/2015] [Accepted: 05/08/2015] [Indexed: 11/22/2022]
Abstract
Enforcing obligate coupling of growth with synthesis of a desired product has become a key principle for metabolic engineering of microbial production strains. Various methods from stoichiometric and constraint-based modeling have been developed to calculate intervention strategies by which a given microorganism can only grow if it synthesizes a desired compound as a mandatory by-product. However, growth-coupled synthesis is not necessarily feasible for every compound of a metabolic network and no rigorous criterion is currently known to test feasibility of coupled product and biomass formation (before searching for suitable intervention strategies). In this work, we show which properties a network must fulfill such that strain designs guaranteeing coupled biomass and product synthesis can exist at all. In networks without flux bounds, coupling is feasible if and only if an elementary mode exists that leads to formation of both biomass and product. Setting flux boundaries leads to more complicated inhomogeneous problems. Making use of the concept of elementary (flux) vectors, a generalization of elementary modes, a criterion for feasibility can also be derived for this situation. We applied our criteria to a metabolic model of Escherichia coli and determined for each metabolite, whether its net production can be coupled with biomass synthesis and calculated the maximal (guaranteed) coupling yield. The somewhat surprising result is that, under aerobic conditions, coupling is indeed possible for each carbon metabolite of the central metabolism. This also holds true for most metabolites under anaerobic conditions but consideration of ATP maintenance requirements implies infeasibility of coupling for certain compounds. On the other hand, ATP maintenance may also increase the maximal coupling yield for some metabolites. Overall, our work provides important insights and novel tools for a central problem of computational strain design.
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112
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113
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Bayer T, Milker S, Wiesinger T, Rudroff F, Mihovilovic MD. Designer Microorganisms for Optimized Redox Cascade Reactions - Challenges and Future Perspectives. Adv Synth Catal 2015. [DOI: 10.1002/adsc.201500202] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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114
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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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115
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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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116
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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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117
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Liu Y, Shin HD, Li J, Liu L. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects. Appl Microbiol Biotechnol 2014; 99:1109-18. [DOI: 10.1007/s00253-014-6298-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Revised: 12/02/2014] [Accepted: 12/04/2014] [Indexed: 12/22/2022]
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