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Zhang X, Tervo CJ, Reed JL. Metabolic assessment of E. coli as a Biofactory for commercial products. Metab Eng 2016; 35:64-74. [DOI: 10.1016/j.ymben.2016.01.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 01/11/2016] [Accepted: 01/25/2016] [Indexed: 11/24/2022]
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Kuwahara H, Alazmi M, Cui X, Gao X. MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind. Nucleic Acids Res 2016; 44:W217-25. [PMID: 27131375 PMCID: PMC4987905 DOI: 10.1093/nar/gkw342] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 04/18/2016] [Indexed: 01/01/2023] Open
Abstract
To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel web server, called MRE, which, for a given pair of starting and desired compounds in a given chassis organism, ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.
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Affiliation(s)
- Hiroyuki Kuwahara
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Meshari Alazmi
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Xuefeng Cui
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955, Saudi Arabia
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Basler G, Küken A, Fernie AR, Nikoloski Z. Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence? Front Bioeng Biotechnol 2016; 4:31. [PMID: 27092301 PMCID: PMC4823303 DOI: 10.3389/fbioe.2016.00031] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 03/24/2016] [Indexed: 11/13/2022] Open
Abstract
Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO2 exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.
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Affiliation(s)
- Georg Basler
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA; Department of Environmental Protection, Estación Experimental del Zaidín CSIC, Granada, Spain
| | - Anika Küken
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
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104
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Chubukov V, Mukhopadhyay A, Petzold CJ, Keasling JD, Martín HG. Synthetic and systems biology for microbial production of commodity chemicals. NPJ Syst Biol Appl 2016; 2:16009. [PMID: 28725470 PMCID: PMC5516863 DOI: 10.1038/npjsba.2016.9] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 02/01/2016] [Accepted: 02/05/2016] [Indexed: 01/08/2023] Open
Abstract
The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.
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Affiliation(s)
- Victor Chubukov
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jay D Keasling
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Héctor García Martín
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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105
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Gu D, Zhang C, Zhou S, Wei L, Hua Q. IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction. Comput Biol Chem 2016; 61:229-37. [DOI: 10.1016/j.compbiolchem.2016.02.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 07/09/2015] [Accepted: 02/15/2016] [Indexed: 12/11/2022]
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106
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Tervo CJ, Reed JL. MapMaker and PathTracer for tracking carbon in genome-scale metabolic models. Biotechnol J 2016; 11:648-61. [PMID: 26771089 DOI: 10.1002/biot.201500267] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 11/11/2015] [Accepted: 01/07/2016] [Indexed: 11/09/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites.
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Affiliation(s)
- Christopher J Tervo
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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107
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Wu SG, Shimizu K, Tang JKH, Tang YJ. Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning. CHEMBIOENG REVIEWS 2016. [DOI: 10.1002/cben.201500024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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108
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Shirai T, Osanai T, Kondo A. Designing intracellular metabolism for production of target compounds by introducing a heterologous metabolic reaction based on a Synechosystis sp. 6803 genome-scale model. Microb Cell Fact 2016; 15:13. [PMID: 26783098 PMCID: PMC4717628 DOI: 10.1186/s12934-016-0416-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 01/07/2016] [Indexed: 12/18/2022] Open
Abstract
Background Designing optimal intracellular metabolism is essential for using microorganisms to produce useful compounds. Computerized calculations for flux balance analysis utilizing a genome-scale model have been performed for such designs. Many genome-scale models have been developed for different microorganisms. However, optimal designs of intracellular metabolism aimed at producing a useful compound often utilize metabolic reactions of only the host microbial cells. In the present study, we added reactions other than the metabolic reactions with Synechosystis sp. 6803 as a host to its genome-scale model, and constructed a metabolic model of hybrid cells (SyHyMeP) using computerized analysis. Using this model provided a metabolic design that improves the theoretical yield of succinic acid, which is a useful compound. Results Constructing the SyHyMeP model enabled new metabolic designs for producing useful compounds. In the present study, we developed a metabolic design that allowed for improved theoretical yield in the production of succinic acid during glycogen metabolism by Synechosystis sp. 6803. The theoretical yield of succinic acid production using a genome-scale model of these cells was 1.00 mol/mol-glucose, but use of the SyHyMeP model enabled a metabolic design with which a 33 % increase in theoretical yield is expected due to the introduction of isocitrate lyase, adding activations of endogenous tree reactions via D-glycerate in Synechosystis sp. 6803. Conclusions The SyHyMeP model developed in this study has provided a new metabolic design that is not restricted only to the metabolic reactions of individual microbial cells. The concept of construction of this model requires only replacement of the genome-scale model of the host microbial cells and can thus be applied to various useful microorganisms for metabolic design to produce compounds. Electronic supplementary material The online version of this article (doi:10.1186/s12934-016-0416-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tomokazu Shirai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
| | - Takashi Osanai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. .,Department of Agricultural Chemistry, School of Agriculture, Meiji University, 1-1-1, Higashimita, Tamaku, Kawasaki, Kanagawa, 214-8571, Japan.
| | - Akihiko Kondo
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. .,Organization of Advanced Science and Technology, Kobe University, 1-1 Rokkodaicho, Nada, Kobe, 657-8501, Japan. .,Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe, 657-8501, Japan.
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109
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Zhang C, Hua Q. Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine. Front Physiol 2016; 6:413. [PMID: 26779040 PMCID: PMC4703781 DOI: 10.3389/fphys.2015.00413] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/15/2015] [Indexed: 12/21/2022] Open
Abstract
Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.
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Affiliation(s)
- Cheng Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyShanghai, China
| | - Qiang Hua
- State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyShanghai, China
- Shanghai Collaborative Innovation Center for Biomanufacturing TechnologyShanghai, China
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110
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Mohammadi R, Fallah-Mehrabadi J, Bidkhori G, Zahiri J, Javad Niroomand M, Masoudi-Nejad A. A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production. MOLECULAR BIOSYSTEMS 2016; 12:2552-61. [DOI: 10.1039/c6mb00119j] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Metabolic network models can be optimized for the production of desired materials like biofuels.
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Affiliation(s)
- Reza Mohammadi
- Laboratory of Systems Biology and Bioinformatics (LBB)
- Institute of Biochemistry and Biophysics
- University of Tehran
- Tehran
- Iran
| | | | | | - Javad Zahiri
- Bioinformatics and Computational Omics Lab (BioCOOL)
- Department of Biophysics
- Faculty of Biological Sciences
- Tarbiat Modares University
- Tehran
| | - Mohammad Javad Niroomand
- Learning Intelligent Systems Lab
- School of Electrical and Computer Engineering
- University of Tehran
- Tehran
- Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB)
- Institute of Biochemistry and Biophysics
- University of Tehran
- Tehran
- Iran
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111
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Kim J, Fabris M, Baart G, Kim MK, Goossens A, Vyverman W, Falkowski PG, Lun DS. Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 85:161-176. [PMID: 26590126 DOI: 10.1111/tpj.13081] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2014] [Revised: 11/04/2015] [Accepted: 11/09/2015] [Indexed: 06/05/2023]
Abstract
Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome-scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock-in transformants. Here we present a genome-scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.
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Affiliation(s)
- Joomi Kim
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Michele Fabris
- Plant Functional Biology and Climate Change Cluster (C3), Faculty of Science University of Technology, Sydney, New South Wales, Australia
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
| | - Gino Baart
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
- Centre of Microbial and Plant Genetics Lab for Genetics and Genomics and Leuven Institute for Beer Research, Leuven University, Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Min K Kim
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA
| | - Alain Goossens
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
| | - Wim Vyverman
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
| | - Paul G Falkowski
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Desmond S Lun
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA
- Department of Plant Biology and Pathology, Rutgers University, New Brunswick, NJ, 08901, USA
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia, Australia
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112
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Sengupta S, Jonnalagadda S, Goonewardena L, Juturu V. Metabolic engineering of a novel muconic acid biosynthesis pathway via 4-hydroxybenzoic acid in Escherichia coli. Appl Environ Microbiol 2015; 81:8037-43. [PMID: 26362984 PMCID: PMC4651072 DOI: 10.1128/aem.01386-15] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 09/10/2015] [Indexed: 11/20/2022] Open
Abstract
cis,cis-Muconic acid (MA) is a commercially important raw material used in pharmaceuticals, functional resins, and agrochemicals. MA is also a potential platform chemical for the production of adipic acid (AA), terephthalic acid, caprolactam, and 1,6-hexanediol. A strain of Escherichia coli K-12, BW25113, was genetically modified, and a novel nonnative metabolic pathway was introduced for the synthesis of MA from glucose. The proposed pathway converted chorismate from the aromatic amino acid pathway to MA via 4-hydroxybenzoic acid (PHB). Three nonnative genes, pobA, aroY, and catA, coding for 4-hydroxybenzoate hydrolyase, protocatechuate decarboxylase, and catechol 1,2-dioxygenase, respectively, were functionally expressed in E. coli to establish the MA biosynthetic pathway. E. coli native genes ubiC, aroF(FBR), aroE, and aroL were overexpressed and the genes ptsH, ptsI, crr, and pykF were deleted from the E. coli genome in order to increase the precursors of the proposed MA pathway. The final engineered E. coli strain produced nearly 170 mg/liter of MA from simple carbon sources in shake flask experiments. The proposed pathway was proved to be functionally active, and the strategy can be used for future metabolic engineering efforts for production of MA from renewable sugars.
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Affiliation(s)
- Sudeshna Sengupta
- Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Sudhakar Jonnalagadda
- Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Lakshani Goonewardena
- Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Veeresh Juturu
- Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
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113
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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: 95] [Impact Index Per Article: 10.6] [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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114
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Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 2015; 5:16009. [PMID: 26530953 PMCID: PMC4632160 DOI: 10.1038/srep16009] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 10/07/2015] [Indexed: 02/07/2023] Open
Abstract
Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.
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115
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Lakshmanan M, Kim TY, Chung BKS, Lee SY, Lee DY. Flux-sum analysis identifies metabolite targets for strain improvement. BMC SYSTEMS BIOLOGY 2015; 9:73. [PMID: 26510838 PMCID: PMC4625974 DOI: 10.1186/s12918-015-0198-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 08/18/2015] [Indexed: 11/21/2022]
Abstract
Background Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets. Results In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology. Conclusions The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore.
| | - Tae Yong Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. .,Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.
| | - Bevan K S Chung
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore.
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. .,Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore. .,Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117576, Singapore. .,Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore.
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Trinh CT, Liu Y, Conner DJ. Rational design of efficient modular cells. Metab Eng 2015; 32:220-231. [PMID: 26497627 DOI: 10.1016/j.ymben.2015.10.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 05/07/2015] [Accepted: 10/14/2015] [Indexed: 01/27/2023]
Abstract
The modular cell design principle is formulated to devise modular (chassis) cells. These cells can be assembled with exchangeable production modules in a plug-and-play fashion to build microbial cell factories for efficient combinatorial biosynthesis of novel molecules, requiring minimal iterative strain optimization steps. A modular cell is designed to be auxotrophic, containing core metabolic pathways that are necessary but insufficient to support cell growth and maintenance. To be functional, it must tightly couple with an exchangeable production module containing auxiliary metabolic pathways that not only complement cell growth but also enhance production of targeted molecules. We developed a MODCELL (modular cell) framework based on metabolic pathway analysis to implement the modular cell design principle. MODCELL identifies genetic modifications and requirements to construct modular cell candidates and their associated exchangeable production modules. By defining the degree of similarity and coupling metrics, MODCELL can evaluate which exchangeable production module(s) can be tightly coupled with a modular cell candidate. We first demonstrated how MODCELL works in a step-by-step manner for example metabolic networks, and then applied it to design modular Escherichia coli cells for efficient combinatorial biosynthesis of five alcohols (ethanol, propanol, isopropanol, butanol and isobutanol) and five butyrate esters (ethyl butyrate, propyl butyrate, isopropyl butyrate, butyl butyrate and isobutyl butyrate) from pentose sugars (arabinose and xylose) and hexose sugars (glucose, mannose, and galactose) under anaerobic conditions. We identified three modular cells, MODCELL1, MODCELL2 and MODCELL3, that can couple well with Group 1 of modules (ethanol, isobutanol, butanol, ethyl butyrate, isobutyl butyrate, butyl butyrate), Group 2 (isopropanol, isopropyl butyrate), and Group 3 (propanol, isopropanol), respectively. We validated the design of MODCELL1 for anaerobic production of ethanol, butanol, and ethyl butyrate using experimental data available in literature.
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Affiliation(s)
- Cong T Trinh
- Department of Chemical and Biomolecular Engineering, United States; UTK-ORNL Joint Institute of Biological Science, United States; Bredesen Center for Interdisciplinary Research and Graduate Education, United States; Institute of Biomedical Engineering, The University of Tennessee, Knoxville, TN, United States; BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
| | - Yan Liu
- Department of Chemical and Biomolecular Engineering, United States
| | - David J Conner
- Department of Chemical and Biomolecular Engineering, United States
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Khosraviani M, Saheb Zamani M, Bidkhori G. FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction. Bioinformatics 2015; 32:398-408. [PMID: 26454274 DOI: 10.1093/bioinformatics/btv578] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 10/02/2015] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION A fundamental computational problem in the area of metabolic engineering is finding metabolic pathways between a pair of source and target metabolites efficiently. We present an approach, namely FogLight, for searching metabolic networks utilizing Boolean (AND-OR) operations represented in matrix notation to efficiently reduce the search space. This enables the enumeration of all pathways between metabolites that are too distant for the application of brute-force methods. RESULTS Benchmarking tests run with FogLight show that it can reduce the search space by up to 98%, after which the accelerated search for high accurate results is guaranteed. Using FogLight, several pathways between eight given pairs of metabolites are found of which the pathways from CO2 to ethanol are specifically discussed. Additionally, in comparison with three path-finding tools, namely PHT, FMM and RouteSearch, FogLight can find shorter and more pathways for attempted source-target metabolite pairs. CONTACT szamani@aut.ac.ir, gholamreza.bidkhori@vtt.fi SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mehrshad Khosraviani
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
| | - Morteza Saheb Zamani
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
| | - Gholamreza Bidkhori
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
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Jamshidi N, Raghunathan A. Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods. Front Microbiol 2015; 6:1032. [PMID: 26500611 PMCID: PMC4594423 DOI: 10.3389/fmicb.2015.01032] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 09/11/2015] [Indexed: 12/12/2022] Open
Abstract
Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.
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Affiliation(s)
- Neema Jamshidi
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA ; Department of Radiological Sciences, University of California, Los Angeles Los Angeles, CA, USA
| | - Anu Raghunathan
- Chemical Engineering Division, National Chemical Laboratory Pune, India
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119
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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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120
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Wang FS, Wu WH. Optimal design of growth-coupled production strains using nested hybrid differential evolution. J Taiwan Inst Chem Eng 2015. [DOI: 10.1016/j.jtice.2015.03.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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121
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Exhaustive Analysis of a Genotype Space Comprising 10(15 )Central Carbon Metabolisms Reveals an Organization Conducive to Metabolic Innovation. PLoS Comput Biol 2015; 11:e1004329. [PMID: 26252881 PMCID: PMC4529314 DOI: 10.1371/journal.pcbi.1004329] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 04/28/2015] [Indexed: 11/24/2022] Open
Abstract
All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients. Here we study metabolism’s potential for innovation by analyzing an exhaustive genotype-phenotype map for a space of 1015 metabolisms that encodes all possible subsets of 51 reactions in central carbon metabolism. Using flux balance analysis, we predict the viability of these metabolisms on 10 different carbon sources which give rise to 1024 potential metabolic phenotypes. Although viable metabolisms with any one phenotype comprise a tiny fraction of genotype space, their absolute numbers exceed 109 for some phenotypes. Metabolisms with any one phenotype typically form a single network of genotypes that extends far or all the way through metabolic genotype space, where any two genotypes can be reached from each other through a series of single reaction changes. The minimal distance of genotype networks associated with different phenotypes is small, such that one can reach metabolisms with novel phenotypes – viable on new carbon sources – through one or few genotypic changes. Exceptions to these principles exist for those metabolisms whose complexity (number of reactions) is close to the minimum needed for viability. Increasing metabolic complexity enhances the potential for both evolutionary conservation and evolutionary innovation. Genotype-phenotype mapping is one of the ultimate goals of computational systems biology, and can provide new insights into the function and evolution of biological systems. We present a comprehensive genotype-phenotype map for a space of metabolic genotypes that comprises more than 1015 central carbon metabolisms. Only one in a million of these metabolisms can sustain life on any one of 10 carbon sources we consider, but these viable metabolisms form connected genotype networks that extend far through genotype space. In addition, they render multiple novel metabolic phenotypes in their immediate neighborhood accessible through small evolutionary changes that require only the alteration of single metabolic reactions. The map we construct reveals an organization of core metabolism that simultaneously facilitates evolutionary conservation of existing metabolic phenotypes, and the origination of novel metabolic traits that allow viability on novel carbon sources. Such metabolic innovation is essential, particularly for organisms that experience unexpected environmental changes, and that explore or invade new habitats.
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Computational Methods for Modification of Metabolic Networks. Comput Struct Biotechnol J 2015; 13:376-81. [PMID: 26106462 PMCID: PMC4477032 DOI: 10.1016/j.csbj.2015.05.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Revised: 05/20/2015] [Accepted: 05/24/2015] [Indexed: 11/22/2022] Open
Abstract
In metabolic engineering, modification of metabolic networks is an important biotechnology and a challenging computational task. In the metabolic network modification, we should modify metabolic networks by newly adding enzymes or/and knocking-out genes to maximize the biomass production with minimum side-effect. In this mini-review, we briefly review constraint-based formalizations for Minimum Reaction Cut (MRC) problem where the minimum set of reactions is deleted so that the target compound becomes non-producible from the view point of the flux balance analysis (FBA), elementary mode (EM), and Boolean models. Minimum Reaction Insertion (MRI) problem where the minimum set of reactions is added so that the target compound newly becomes producible is also explained with a similar formalization approach. The relation between the accuracy of the models and the risk of overfitting is also discussed.
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123
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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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124
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Imam S, Schäuble S, Brooks AN, Baliga NS, Price ND. Data-driven integration of genome-scale regulatory and metabolic network models. Front Microbiol 2015; 6:409. [PMID: 25999934 PMCID: PMC4419725 DOI: 10.3389/fmicb.2015.00409] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Accepted: 04/20/2015] [Indexed: 12/21/2022] Open
Abstract
Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert-a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.
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Affiliation(s)
- Saheed Imam
- Institute for Systems Biology Seattle, WA, USA
| | - Sascha Schäuble
- Institute for Systems Biology Seattle, WA, USA ; Jena University Language and Information Engineering Lab, Friedrich-Schiller-University Jena Jena, Germany
| | | | - Nitin S Baliga
- Institute for Systems Biology Seattle, WA, USA ; Departments of Biology and Microbiology, University of Washington Seattle, WA, USA ; Molecular and Cellular Biology Program, University of Washington Seattle, WA, USA ; Lawrence Berkeley National Lab Berkeley, CA, USA
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125
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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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126
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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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127
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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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128
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Liu F, Vilaça P, Rocha I, Rocha M. Development and application of efficient pathway enumeration algorithms for metabolic engineering applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 118:134-146. [PMID: 25580014 DOI: 10.1016/j.cmpb.2014.11.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Revised: 10/31/2014] [Accepted: 11/26/2014] [Indexed: 06/04/2023]
Abstract
Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation.
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Affiliation(s)
- F Liu
- Centre for Biological Engineering, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
| | - P Vilaça
- Centre for Biological Engineering, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal; SilicoLife Lda., Rua do Canastreiro 15, 4715-387 Braga, Portugal
| | - I Rocha
- Centre for Biological Engineering, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
| | - M Rocha
- Centre for Biological Engineering, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal.
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129
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Beal J. Bridging the gap: a roadmap to breaking the biological design barrier. Front Bioeng Biotechnol 2015; 2:87. [PMID: 25654077 PMCID: PMC4299508 DOI: 10.3389/fbioe.2014.00087] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 12/21/2014] [Indexed: 12/11/2022] Open
Abstract
This paper presents an analysis of an emerging bottleneck in organism engineering, and paths by which it may be overcome. Recent years have seen the development of a profusion of synthetic biology tools, largely falling into two categories: high-level “design” tools aimed at mapping from organism specifications to nucleic acid sequences implementing those specifications, and low-level “build and test” tools aimed at faster, cheaper, and more reliable fabrication of those sequences and assays of their behavior in engineered biological organisms. Between the two families, however, there is a major gap: we still largely lack the predictive models and component characterization data required to effectively determine which of the many possible candidate sequences considered in the design phase are the most likely to produce useful results when built and tested. As low-level tools continue to mature, the bottleneck in biological systems engineering is shifting to be dominated by design, making this gap a critical barrier to progress. Considering how to address this gap, we find that widespread adoption of readily available analytic and assay methods is likely to lead to rapid improvement in available predictive models and component characterization models, as evidenced by a number of recent results. Such an enabling development is, in turn, likely to allow high-level tools to break the design barrier and support rapid development of transformative biological applications.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies , Cambridge, MA , USA
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130
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Li Y, Tian P. Contemplating 3-Hydroxypropionic Acid Biosynthesis in Klebsiella pneumoniae. Indian J Microbiol 2015; 55:131-9. [PMID: 25805899 DOI: 10.1007/s12088-015-0513-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 01/09/2015] [Indexed: 11/30/2022] Open
Abstract
3-Hydroxypropionic acid (3-HP) is a commercially valuable platform chemical from which an array of C3 compounds can be generated. Klebsiella pneumoniae has been considered a promising species for biological production of 3-HP. Despite a wealth of reports related to 3-HP biosynthesis in K. pneumoniae, its commercialization is still in infancy. The major hurdle hindering 3-HP overproduction lies in the poor understanding of glycerol dissimilation in K. pneumoniae. To surmount this problem, this review aims to portray a picture of 3-HP biosynthesis, involving 3-HP-synthesizing strains, biochemical attributes, metabolic pathways and key enzymes. Inspired by the state-of-the-art advances in metabolic engineering and synthetic biology, here we advocate protocols for overproducing 3-HP in K. pneumoniae. These protocols range from cofactor regeneration, alleviation of metabolite toxicity, genome editing, remodeling of transport system, to carbon flux partition via logic gate. The feasibility for these protocols was also discussed.
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Affiliation(s)
- Ying Li
- Beijing Key Lab of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 People's Republic of China
| | - Pingfang Tian
- Beijing Key Lab of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 People's Republic of China
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131
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Ye C, Xu N, Chen H, Chen YQ, Chen W, Liu L. Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus Mortierella alpina. BMC SYSTEMS BIOLOGY 2015; 9:1. [PMID: 25582171 PMCID: PMC4301621 DOI: 10.1186/s12918-014-0137-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 12/11/2014] [Indexed: 12/30/2022]
Abstract
Background Mortierella alpina is an oleaginous fungus used in the industrial scale production of arachidonic acid (ARA). In order to investigate the metabolic characteristics at a systems level and to explore potential strategies for enhanced lipid production, a genome-scale metabolic model of M. alpina was reconstructed. Results This model included 1106 genes, 1854 reactions and 1732 metabolites. On minimal growth medium, 86 genes were identified as essential, whereas 49 essential genes were identified on yeast extract medium. A series of sequential desaturase and elongase catalysed steps are involved in the synthesis of polyunsaturated fatty acids (PUFAs) from acetyl-CoA precursors, with concomitant NADPH consumption, and these steps were investigated in this study. Oxygen is known to affect the degree of unsaturation of PUFAs, and robustness analysis determined that an oxygen uptake rate of 2.0 mmol gDW−1 h−1 was optimal for ARA accumulation. The flux of 53 reactions involving NADPH was significantly altered at different ARA levels. Of these, malic enzyme (ME) was confirmed as a key component in ARA production and NADPH generation. When using minimization of metabolic adjustment, a knock-out of ME led to a 38.28% decrease in ARA production. Conclusions The simulation results confirmed the model as a useful tool for future research on the metabolism of PUFAs. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0137-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
| | - Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
| | - Haiqin Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Synergistic Innovation Center for Food Safety and Nutrition, School of Food Science and technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
| | - Yong Q Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Synergistic Innovation Center for Food Safety and Nutrition, School of Food Science and technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
| | - Wei Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Synergistic Innovation Center for Food Safety and Nutrition, School of Food Science and technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
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132
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Genome-scale modeling for metabolic engineering. J Ind Microbiol Biotechnol 2015; 42:327-38. [PMID: 25578304 DOI: 10.1007/s10295-014-1576-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 12/20/2014] [Indexed: 01/04/2023]
Abstract
We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.
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133
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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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134
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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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135
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Basler G. Computational prediction of essential metabolic genes using constraint-based approaches. Methods Mol Biol 2015; 1279:183-204. [PMID: 25636620 DOI: 10.1007/978-1-4939-2398-4_12] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.
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Affiliation(s)
- Georg Basler
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Profesor Albareda 1, 18008, Granada, Spain,
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136
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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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137
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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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138
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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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139
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Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains. Comput Biol Chem 2014; 53PB:175-183. [DOI: 10.1016/j.compbiolchem.2014.09.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 09/14/2014] [Accepted: 09/23/2014] [Indexed: 11/23/2022]
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140
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Erickson KE, Gill RT, Chatterjee A. CONSTRICTOR: constraint modification provides insight into design of biochemical networks. PLoS One 2014; 9:e113820. [PMID: 25422896 PMCID: PMC4244162 DOI: 10.1371/journal.pone.0113820] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 10/30/2014] [Indexed: 11/18/2022] Open
Abstract
Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.
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Affiliation(s)
- Keesha E. Erickson
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado, United States of America
| | - Ryan T. Gill
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado, United States of America
| | - Anushree Chatterjee
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado, United States of America
- BioFrontiers Institute, University of Colorado, Boulder, Colorado, United States of America
- * E-mail:
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141
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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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142
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Computational approaches for microalgal biofuel optimization: a review. BIOMED RESEARCH INTERNATIONAL 2014; 2014:649453. [PMID: 25309916 PMCID: PMC4189764 DOI: 10.1155/2014/649453] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 08/28/2014] [Accepted: 09/01/2014] [Indexed: 11/18/2022]
Abstract
The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.
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143
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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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144
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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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145
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DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae. PLoS One 2014; 9:e104499. [PMID: 25105494 PMCID: PMC4126706 DOI: 10.1371/journal.pone.0104499] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 07/12/2014] [Indexed: 11/23/2022] Open
Abstract
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.
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146
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Kunjapur AM, Tarasova Y, Prather KLJ. Synthesis and accumulation of aromatic aldehydes in an engineered strain of Escherichia coli. J Am Chem Soc 2014; 136:11644-54. [PMID: 25076127 DOI: 10.1021/ja506664a] [Citation(s) in RCA: 190] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Aromatic aldehydes are useful in numerous applications, especially as flavors, fragrances, and pharmaceutical precursors. However, microbial synthesis of aldehydes is hindered by rapid, endogenous, and redundant conversion of aldehydes to their corresponding alcohols. We report the construction of an Escherichia coli K-12 MG1655 strain with reduced aromatic aldehyde reduction (RARE) that serves as a platform for aromatic aldehyde biosynthesis. Six genes with reported activity on the model substrate benzaldehyde were rationally targeted for deletion: three genes that encode aldo-keto reductases and three genes that encode alcohol dehydrogenases. Upon expression of a recombinant carboxylic acid reductase in the RARE strain and addition of benzoate during growth, benzaldehyde remained in the culture after 24 h, with less than 12% conversion of benzaldehyde to benzyl alcohol. Although individual overexpression results demonstrated that all six genes could contribute to benzaldehyde reduction in vivo, additional experiments featuring subset deletion strains revealed that two of the gene deletions were dispensable under the conditions tested. The engineered strain was next investigated for the production of vanillin from vanillate and succeeded in preventing formation of the byproduct vanillyl alcohol. A pathway for the biosynthesis of vanillin directly from glucose was introduced and resulted in a 55-fold improvement in vanillin titer when using the RARE strain versus the wild-type strain. Finally, synthesis of the chiral pharmaceutical intermediate L-phenylacetylcarbinol (L-PAC) was demonstrated from benzaldehyde and glucose upon expression of a recombinant mutant pyruvate decarboxylase in the RARE strain. Beyond allowing accumulation of aromatic aldehydes as end products in E. coli, the RARE strain expands the classes of chemicals that can be produced microbially via aldehyde intermediates.
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Affiliation(s)
- Aditya M Kunjapur
- Department of Chemical Engineering, ‡Synthetic Biology Engineering Research Center (SynBERC), §Microbiology Graduate Program, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States
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147
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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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148
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Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol Syst Biol 2014; 10:744. [PMID: 25086087 PMCID: PMC4299514 DOI: 10.15252/msb.20134993] [Citation(s) in RCA: 107] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Over the last decade, the field of cancer metabolism has mainly focused on studying the role of
tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first
genome-scale computational study of the metabolic underpinnings of cancer migration. We build
genome-scale metabolic models of the NCI-60 cell lines that capture the Warburg effect (aerobic
glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these
cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is
found to be highly positively associated with cancer cell migration. We hence predicted that
targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer
migration. By testing the anti-migratory effects of silencing such 17 top predicted genes in four
breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly
attenuate cell migration either in all or one cell line only, while having almost no effect on cell
proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed
in the ratio between experimentally measured ECAR and OCR levels following these perturbations.
Inhibiting anti-migratory targets is a promising future avenue in treating cancer since it may
decrease cytotoxic-related side effects that plague current anti-proliferative treatments.
Furthermore, it may reduce cytotoxic-related clonal selection of more aggressive cancer cells and
the likelihood of emerging resistance.
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Sylvia E Le Dévédec
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Vasiliki Maria Rogkoti
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Franziska Baenke
- Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK
| | - Vincent C de Boer
- Laboratory Genetic Metabolic Diseases, Academic Medical Center, Amsterdam, The Netherlands
| | - Christian Frezza
- MRC Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Almut Schulze
- Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Eytan Ruppin
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel The Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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149
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Arora PK, Bae H. Integration of bioinformatics to biodegradation. Biol Proced Online 2014; 16:8. [PMID: 24808763 PMCID: PMC4012781 DOI: 10.1186/1480-9222-16-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 04/19/2014] [Indexed: 12/22/2022] Open
Abstract
Bioinformatics and biodegradation are two primary scientific fields in applied microbiology and biotechnology. The present review describes development of various bioinformatics tools that may be applied in the field of biodegradation. Several databases, including the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology Database (Bionemo) MetaCyc, and BioCyc have been developed to enable access to information related to biochemistry and genetics of microbial degradation. In addition, several bioinformatics tools for predicting toxicity and biodegradation of chemicals have been developed. Furthermore, the whole genomes of several potential degrading bacteria have been sequenced and annotated using bioinformatics tools.
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Affiliation(s)
- Pankaj Kumar Arora
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
| | - Hanhong Bae
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
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150
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Elementary Flux Mode Analysis of Acetyl-CoA Pathway in Carboxydothermus hydrogenoformans Z-2901. Adv Bioinformatics 2014; 2014:928038. [PMID: 24822064 PMCID: PMC4009226 DOI: 10.1155/2014/928038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 03/10/2014] [Accepted: 03/14/2014] [Indexed: 11/26/2022] Open
Abstract
Carboxydothermus hydrogenoformans is a carboxydotrophic hydrogenogenic bacterium species that produces hydrogen molecule by utilizing carbon monoxide (CO) or pyruvate as a carbon source. To investigate the underlying biochemical mechanism of hydrogen production, an elementary mode analysis of acetyl-CoA pathway was performed to determine the intermediate fluxes by combining linear programming (LP) method available in CellNetAnalyzer software. We hypothesized that addition of enzymes necessary for carbon monoxide fixation and pyruvate dissimilation would enhance the theoretical yield of hydrogen. An in silico gene knockout of pyk, pykC, and mdh genes of modeled acetyl-CoA pathway allows the maximum theoretical hydrogen yield of 47.62 mmol/gCDW/h for 1 mole of carbon monoxide (CO) uptake. The obtained hydrogen yield is comparatively two times greater than the previous experimental data. Therefore, it could be concluded that this elementary flux mode analysis is a crucial way to achieve efficient hydrogen production through acetyl-CoA pathway and act as a model for strain improvement.
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