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Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae

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METABOLIC ENGINEERING 2014年 24卷 117-128页

作者： King, Zachary A. Feist, Adam M. Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark

Maintaining cofactor balance is a critical function in microorganisms, but often the native cofactor balance does not match the needs of an engineered metabolic flux state. Here, an optimization procedure is utilized to identify optimal cofactor-specificity "swaps" for oxidoreductase enzymes utilizing NAD(H) or NADP(H) in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae. The theoretical yields of all native carbon-containing molecules are considered, as well as theoretical yields of twelve heterologous production pathways in E. coli. Swapping the cofactor specificity of central metabolic enzymes (especially GAPD and ALCD2x) is shown to increase NADPH production and increase theoretical yields for native products in E. coli and yeast-including L-aspartate, L-lysine, L-isoleucine, L-proline, L-serine, and putrescine-and non-native products in E. coli-including 1,3-propanediol, 3-hydroxybutyrate, 3-hydroxypropanoate, 3-hydroxyvalerate, and styrene. (C) 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

关键词： Cofactor balancing Escherichia coli Saccharomyces cerevisiae constraint-based modeling MILP Theoretical yield Metabolic engineering

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constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering

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BIOTECHNOLOGY JOURNAL 2013年第5期8卷 595-604页

作者： Cotten, Cameron Reed, Jennifer L. Univ Wisconsin Dept Chem & Biol Engn Madison WI USA Univ Wisconsin Great Lakes Bioenergy Res Ctr Madison WI USA

In recent years, a growing number of metabolic engineering strain design techniques have employed constraint-based modeling to determine metabolic and regulatory network changes which are needed to improve chemical production. These methods use systems-level analysis of metabolism to help guide experimental efforts by identifying deletions, additions, downregulations, and upregulations of metabolic genes that will increase biological production of a desired metabolic product. In this work, we propose a new strain design method with continuous modifications (CosMos) that provides strategies for deletions, downregulations, and upregulations of fluxes that will lead to the production of the desired products. The method is conceptually simple and easy to implement, and can provide additional strategies over current approaches. We found that the method was able to find strain design strategies that required fewer modifications and had larger predicted yields than strategies from previous methods in example and genome-scale networks. Using CosMos, we identified modification strategies for producing a variety of metabolic products, compared strategies derived from Escherichia coli and Saccharomyces cerevisiae metabolic models, and examined how imperfect implementation may affect experimental outcomes. This study gives a powerful and flexible technique for strain engineering and examines some of the unexpected outcomes that may arise when strategies are implemented experimentally.

关键词： constraint-based modeling Enzyme expression Flux balance analysis OptKnock Strain design

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Minimal metabolic pathway structure is consistent with associated biomolecular interactions

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MOLECULAR SYSTEMS BIOLOGY 2014年第7期10卷 737-737页

作者： Bordbar, Aarash Nagarajan, Harish Lewis, Nathan E. Latif, Haythem Ebrahim, Ali Federowicz, Stephen Schellenberger, Jan Palsson, Bernhard O. Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Bioinformat & Syst Biol Program La Jolla CA 92093 USA Univ Calif San Diego Sch Med Dept Pediat La Jolla CA 92093 USA Harvard Univ Sch Med Wyss Inst Biol Inspired Engn Boston MA USA Harvard Univ Sch Med Dept Genet Boston MA USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures;namely, that pathways may be minimal, independent, and segregated.

关键词： constraint-based modeling genetic interactions pathway analysis protein-protein interactions transcriptional regulatory networks

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A genome-scale metabolic flux model of Escherichia coli K-12 derived from the EcoCyc database

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 79页

作者： Weaver, Daniel S. Keseler, Ingrid M. Mackie, Amanda Paulsen, Ian T. Karp, Peter D. SRI Int Bioinformat Res Grp Menlo Pk CA 94025 USA Macquarie Univ Dept Biomol & Chem Engn N Ryde NSW 2109 Australia

Background: constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. Results: We present EcoCyc-18.0-GEM, a genome-scale model of the E. coli K-12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc-18.0-GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc-18.0-GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model's derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc-18.0-GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. Conclusion: Significant advantages can be derive

关键词： Escherichia coli Flux balance analysis constraint-based modeling Metabolic network reconstruction Metabolic modeling Genome-scale model Gene essentiality Systems biology EcoCyc Pathway Tools

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CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 123页

作者： Carreira, Rafael Evangelista, Pedro Maia, Paulo Vilaca, Paulo Pont, Marcellinus Tomb, Jean-Francois Rocha, Isabel Rocha, Miguel Univ Minho Ctr Biol Engn Braga Portugal Univ Minho CCTC Braga Portugal EI DuPont De Nemours & Co Inc Wilmington DE USA SilicoLife Lda Braga Portugal

Background: Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. Results: This work presents constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e. g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. Conclusions: A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.

关键词： constraint-based modeling Metabolic Flux analysis Metabolic engineering Open-source software

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Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 110页

作者： Liu, Joanne K. O'Brien, Edward J. Lerman, Joshua A. Zengler, Karsten Palsson, Bernhard O. Feist, Adam M. Univ Calif San Diego La Jolla CA 92093 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Dept Pediat La Jolla CA 92093 USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark

Background: Membranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane;thus it represents a notable constraint on cellular functions. Membrane-and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models. Results: The recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass. Conclusion: iJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components.

关键词： constraint-based modeling gene expression metabolism protein translocation compartmentalization

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Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 16-16页

作者： Lindfors, Erno Jouhten, Paula Oja, Merja Rintala, Eija Oresic, Matej Penttila, Merja VTT Tech Res Ctr Finland Espoo Finland

Background: Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function. Results: The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network. Conclusions: The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

关键词： Saccharomyces cerevisiae Network biology Molecular path finding Data integration Oxygen constraint-based modeling

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Elementary Flux Modes, Flux Balance Analysis, and Their Application to Plant Metabolism

Elementary Flux Modes, Flux Balance Analysis, and Their Appl...

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作者： Katrin Lotz Anja Hartmann Eva Grafahrend-Belau Falk Schreiber Björn H. Junker

In recent years the number of sequenced and annotated plant genomes has increased significantly, and novel approaches are required to retrieve valuable information from these data sets. The field of systems biology has accelerated the simulation and prediction of phenotypes derived from specific genotypic modifications under defined growth conditions. The biochemical potential of a cell from a specific plant tissue (e.g., seed endosperm) can be derived from its genome in the form of a mathematical model by the method of metabolic network reconstruction. This model can be further analyzed by studying its network properties, analyzing feasible pathway routes through the network, or simulating possible flux distributions of the network . Here, we describe two approaches for identification of all feasible routes through the network (elementary mode analysis) and for simulation of flux distribution in the network based on plant physiological uptake and excretion rates (flux balance analysis). less

关键词： Metabolic Flux Analysis Metabolic Engineering Fermentation MATLAB Flux balance analysis Metabolic modeling constraint-based modeling Metabolic reconstruction Elementary flux modes Primary plant metabolism

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Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism

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BIOTECHNOLOGY LETTERS 2013年第12期35卷 2039-2044页

作者： Tabe-Bordbar, Shayan Marashi, Sayed-Amir Univ Tehran Coll Sci Dept Biotechnol Tehran Iran

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing "estimated" control-effective flux values in Escherichia coli metabolic network.

关键词： constraint-based modeling Elementary flux modes Flux balancing analysis Flux coupling analysis Metabolic networks Metabolic pathways Random sampling

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Basic concepts and principles of stoichiometric modeling of metabolic networks

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BIOTECHNOLOGY JOURNAL 2013年第9期8卷 997-U52页

作者： Maarleveld, Timo R. Khandelwal, Ruchir A. Olivier, Brett G. Teusink, Bas Bruggeman, Frank J. Ctr Math & Comp Sci Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Inst Mol Med & Syst Amsterdam Netherlands BioSolar Cells Wageningen Netherlands Kluyver Ctr Genom Ind Fermentat NCSB Delft Netherlands

Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. constraint-based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint-based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.

关键词： constraint-based modeling Flux balance analysis Flux modes Metabolism Optimal solution space

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