Jean-Marc Schwartz1, Minoru Kanehisa. 1. Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto, Japan. jean@kuicr.kyoto-u.ac.jp
Abstract
MOTIVATION: It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions. RESULTS: We introduce an approach for decomposing steady-state flux distributions onto elementary modes based on quadratic programming. The decomposition is performed so as to favour modes that are closest to the actual state of the system, i.e. most relevant for biological interpretation. As an illustration, an application of this approach to a model of yeast glycolysis is presented. AVAILABILITY: Software is available upon request from the authors.
MOTIVATION: It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions. RESULTS: We introduce an approach for decomposing steady-state flux distributions onto elementary modes based on quadratic programming. The decomposition is performed so as to favour modes that are closest to the actual state of the system, i.e. most relevant for biological interpretation. As an illustration, an application of this approach to a model of yeast glycolysis is presented. AVAILABILITY: Software is available upon request from the authors.
Authors: Mehmet A Orman; Marianthi G Ierapetritou; Ioannis P Androulakis; Francois Berthiaume Journal: Biotechnol Bioeng Date: 2011-08-04 Impact factor: 4.530
Authors: Mehmet A Orman; Francois Berthiaume; Ioannis P Androulakis; Marianthi G Ierapetritou Journal: J Theor Biol Date: 2010-12-14 Impact factor: 2.691