Wolfram Liebermeister1, Jannis Uhlendorf, Edda Klipp. 1. Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany. wolfram.liebermeister@biologie.hu-berlin.de
Abstract
MOTIVATION: Standard rate laws are a key requisite for systematically turning metabolic networks into kinetic models. They should provide simple, general and biochemically plausible formulae for reaction velocities and reaction elasticities. At the same time, they need to respect thermodynamic relations between the kinetic constants and the metabolic fluxes and concentrations. RESULTS: We present a family of reversible rate laws for reactions with arbitrary stoichiometries and various types of regulation, including mass-action, Michaelis-Menten and uni-uni reversible Hill kinetics as special cases. With a thermodynamically safe parameterization of these rate laws, parameter sets obtained by model fitting, sampling or optimization are guaranteed to lead to consistent chemical equilibrium states. A reformulation using saturation values yields simple formulae for rates and elasticities, which can be easily adjusted to the given stationary flux distributions. Furthermore, this formulation highlights the role of chemical potential differences as thermodynamic driving forces. We compare the modular rate laws to the thermodynamic-kinetic modelling formalism and discuss a simplified rate law in which the reaction rate directly depends on the reaction affinity. For automatic handling of modular rate laws, we propose a standard syntax and semantic annotations for the Systems Biology Markup Language. AVAILABILITY: An online tool for inserting the rate laws into SBML models is freely available at www.semanticsbml.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Standard rate laws are a key requisite for systematically turning metabolic networks into kinetic models. They should provide simple, general and biochemically plausible formulae for reaction velocities and reaction elasticities. At the same time, they need to respect thermodynamic relations between the kinetic constants and the metabolic fluxes and concentrations. RESULTS: We present a family of reversible rate laws for reactions with arbitrary stoichiometries and various types of regulation, including mass-action, Michaelis-Menten and uni-uni reversible Hill kinetics as special cases. With a thermodynamically safe parameterization of these rate laws, parameter sets obtained by model fitting, sampling or optimization are guaranteed to lead to consistent chemical equilibrium states. A reformulation using saturation values yields simple formulae for rates and elasticities, which can be easily adjusted to the given stationary flux distributions. Furthermore, this formulation highlights the role of chemical potential differences as thermodynamic driving forces. We compare the modular rate laws to the thermodynamic-kinetic modelling formalism and discuss a simplified rate law in which the reaction rate directly depends on the reaction affinity. For automatic handling of modular rate laws, we propose a standard syntax and semantic annotations for the Systems Biology Markup Language. AVAILABILITY: An online tool for inserting the rate laws into SBML models is freely available at www.semanticsbml.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Wilbert B Copeland; Bryan A Bartley; Deepak Chandran; Michal Galdzicki; Kyung H Kim; Sean C Sleight; Costas D Maranas; Herbert M Sauro Journal: Metab Eng Date: 2012-05 Impact factor: 9.783
Authors: Michael Pan; Peter J Gawthrop; Kenneth Tran; Joseph Cursons; Edmund J Crampin Journal: Proc Math Phys Eng Sci Date: 2018-06-27 Impact factor: 2.704
Authors: Dan Davidi; Elad Noor; Wolfram Liebermeister; Arren Bar-Even; Avi Flamholz; Katja Tummler; Uri Barenholz; Miki Goldenfeld; Tomer Shlomi; Ron Milo Journal: Proc Natl Acad Sci U S A Date: 2016-03-07 Impact factor: 11.205
Authors: W Lee Pang; Amardeep Kaur; Alexander V Ratushny; Aleksandar Cvetkovic; Sunil Kumar; Min Pan; Adam P Arkin; John D Aitchison; Michael W W Adams; Nitin S Baliga Journal: PLoS Comput Biol Date: 2013-01-17 Impact factor: 4.475