Composition of metabolic flux distributions by functionally interpretable minimal flux modes (MinModes).

All cellular functions are ultimately linked to the metabolism which constitutes a highly branched network of thousands of enzyme-catalyzed chemical reactions and carrier-mediated transport processes. Depending on the prevailing functions (e.g. detoxification of a toxin or accumulation of biomass) the distribution of fluxes in the metabolic network may vary considerably. To better reveal and quantify this flux-function relationship we propose a novel computational approach which identifies distinct contributions--so called minimal flux modes (short: MinModes)--to a stationary flux distribution in the network. Each of these contributions is characterized by a single metabolic output. A MinMode is a minimal (according to a defined cost function) steady state flux distribution that enables the production of a single metabolite. We apply this concept to a metabolic network of Methylobacterium extorquens AM1 comprising of 95 reactions and 74 metabolites, 17 of these metabolites entering the biomass of the bacterium and are thus considered as the metabolic output of the network. MinModes represent a manageable set of fundamental flux modes in the network having a clear physiological meaning and--although not representing a basis in strict mathematical sense - provide a satisfactory approximation of the overall flux distribution in cases tested so far.

[1]  Juan Carlos Nuño,et al.  METATOOL: for studying metabolic networks , 1999, Bioinform..

[2]  R. Heinrich,et al.  Metabolic Pathway Analysis: Basic Concepts and Scientific Applications in the Post‐genomic Era , 1999, Biotechnology progress.

[3]  B. Palsson,et al.  Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. , 2000, Journal of theoretical biology.

[4]  Jason A. Papin,et al.  Determination of redundancy and systems properties of the metabolic network of Helicobacter pylori using genome-scale extreme pathway analysis. , 2002, Genome research.

[5]  M. Lidstrom,et al.  Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C(3) and C(4) metabolism. , 2002, Biotechnology and bioengineering.

[6]  H. Matsuzawa,et al.  An absolute requirement of fructose 1,6-bisphosphate for the Lactobacillus casei L-lactate dehydrogenase activity induced by a single amino acid substitution. , 2002, Protein engineering.

[7]  H. Qian,et al.  Energy balance for analysis of complex metabolic networks. , 2002, Biophysical journal.

[8]  A. Cornish-Bowden,et al.  Systems biology: Metabolic balance sheets , 2002, Nature.

[9]  Jason A. Papin,et al.  The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. , 2002, Journal of theoretical biology.

[10]  D. Fell,et al.  Reaction routes in biochemical reaction systems: Algebraic properties, validated calculation procedure and example from nucleotide metabolism , 2002, Journal of mathematical biology.

[11]  T. Strovas,et al.  Quantification of central metabolic fluxes in the facultative methylotroph methylobacterium extorquens AM1 using 13C‐label tracing and mass spectrometry , 2003, Biotechnology and bioengineering.

[12]  M. Lidstrom,et al.  Genetic Characterization of the Carotenoid Biosynthetic Pathway in Methylobacterium extorquens AM1 and Isolation of a Colorless Mutant , 2003, Applied and Environmental Microbiology.

[13]  B. Palsson,et al.  Constraints-based models: regulation of gene expression reduces the steady-state solution space. , 2003, Journal of theoretical biology.

[14]  Steffen Klamt,et al.  FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps , 2003, Bioinform..

[15]  A. Lapidus,et al.  Methylotrophy in Methylobacterium extorquens AM1 from a Genomic Point of View , 2003, Journal of bacteriology.

[16]  Jason A. Papin,et al.  Metabolic pathways in the post-genome era. , 2003, Trends in biochemical sciences.

[17]  J. Stelling,et al.  Combinatorial Complexity of Pathway Analysis in Metabolic Networks , 2004, Molecular Biology Reports.

[18]  M. Lidstrom,et al.  Multiple Formate Dehydrogenase Enzymes in the Facultative Methylotroph Methylobacterium extorquens AM1 Are Dispensable for Growth on Methanol , 2004, Journal of bacteriology.

[19]  Steffen Klamt,et al.  Minimal cut sets in biochemical reaction networks , 2004, Bioinform..

[20]  H. Holzhütter.  The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. , 2004, European journal of biochemistry.

[21]  H. Qian,et al.  Thermodynamic constraints for biochemical networks. , 2004, Journal of theoretical biology.

[22]  H. Holzhütter,et al.  Computational Design of Reduced Metabolic Networks , 2004, Chembiochem : a European journal of chemical biology.

[23]  Steffen Klamt,et al.  Computation of elementary modes: a unifying framework and the new binary approach , 2004, BMC Bioinformatics.

[24]  Minoru Kanehisa,et al.  Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis , 2006, BMC Bioinformatics.

[25]  Robert Urbanczik,et al.  The geometry of the flux cone of a metabolic network. , 2005, Biophysical journal.

[26]  Minoru Kanehisa,et al.  A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes , 2005, ECCB/JBI.

[27]  B. L. Clarke.  Stoichiometric network analysis , 2008, Cell Biophysics.