Literature DB >> 20230840

Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism.

R M T Fleming1, I Thiele, G Provan, H P Nasheuer.   

Abstract

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in Escherichia coli and compare favourably with in silico prediction by flux balance analysis. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20230840      PMCID: PMC2868105          DOI: 10.1016/j.jtbi.2010.02.044

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  36 in total

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5.  Group contribution method for thermodynamic analysis of complex metabolic networks.

Authors:  Matthew D Jankowski; Christopher S Henry; Linda J Broadbelt; Vassily Hatzimanikatis
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6.  Estimation of standard Gibbs energy changes of biotransformations.

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9.  Relationship between thermodynamic driving force and one-way fluxes in reversible processes.

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Journal:  PLoS One       Date:  2007-01-03       Impact factor: 3.240

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Authors:  Matthew Yeung; Ines Thiele; Bernard O Palsson
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  20 in total

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2.  Elimination of thermodynamically infeasible loops in steady-state metabolic models.

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5.  A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws.

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6.  Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Authors:  Laurent Heirendt; Sylvain Arreckx; Thomas Pfau; Sebastián N Mendoza; Anne Richelle; Almut Heinken; Hulda S Haraldsdóttir; Jacek Wachowiak; Sarah M Keating; Vanja Vlasov; Stefania Magnusdóttir; Chiam Yu Ng; German Preciat; Alise Žagare; Siu H J Chan; Maike K Aurich; Catherine M Clancy; Jennifer Modamio; John T Sauls; Alberto Noronha; Aarash Bordbar; Benjamin Cousins; Diana C El Assal; Luis V Valcarcel; Iñigo Apaolaza; Susan Ghaderi; Masoud Ahookhosh; Marouen Ben Guebila; Andrejs Kostromins; Nicolas Sompairac; Hoai M Le; Ding Ma; Yuekai Sun; Lin Wang; James T Yurkovich; Miguel A P Oliveira; Phan T Vuong; Lemmer P El Assal; Inna Kuperstein; Andrei Zinovyev; H Scott Hinton; William A Bryant; Francisco J Aragón Artacho; Francisco J Planes; Egils Stalidzans; Alejandro Maass; Santosh Vempala; Michael Hucka; Michael A Saunders; Costas D Maranas; Nathan E Lewis; Thomas Sauter; Bernhard Ø Palsson; Ines Thiele; Ronan M T Fleming
Journal:  Nat Protoc       Date:  2019-03       Impact factor: 13.491

7.  A proof for loop-law constraints in stoichiometric metabolic networks.

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Review 9.  Shrinking the metabolic solution space using experimental datasets.

Authors:  Jennifer L Reed
Journal:  PLoS Comput Biol       Date:  2012-08-30       Impact factor: 4.475

10.  Model-driven elucidation of the inherent capacity of Geobacter sulfurreducens for electricity generation.

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Journal:  J Biol Eng       Date:  2013-05-29       Impact factor: 4.355

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