Literature DB >> 11835134

Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria.

Steffen Klamt1, Stefan Schuster, Ernst Dieter Gilles.   

Abstract

Metabolite balancing has turned out to be a powerful computational tool in metabolic engineering. However, the linear equation systems occurring in this analysis are often underdetermined. If it is difficult or impossible to find the missing constraints, it is nevertheless feasible in some cases to determine the values of a subset of the unknown rates. Here, a procedure for finding out which reaction rates can be uniquely calculated in underdetermined metabolic networks and computing these rates is given. The method is based on the null space to the stoichiometry matrix corresponding to the reactions with unknown rates. It is shown that this method is considerably easier to handle than an algorithm given previously (Van der Heijden et al., 1994a). Furthermore, a useful elementary representation of the null space is presented which is closely related with the elementary flux modes. This unique representation is central to a more general approach to observability/calculability analysis. In particular, it allows one to find, in an easy way, those sets of measurable rates that enable a calculation of a certain unknown rate. Besides, rates which are never calculable by metabolite balancing may be easily detected by this method. The applicability of these methods is illustrated by a model of the central metabolism in purple nonsulfur bacteria. The photoheterotrophic growth of these representatives of anoxygenic photosynthetic bacteria is stoichiometrically analyzed. Interesting metabolic constraints caused by the necessary balancing of NADPH can be detected in a highly underdetermined system. This is, to our knowledge, the first application of stoichiometric analysis to the metabolic network in this bacteria group using metabolite balancing techniques. A new software tool, the FluxAnalyzer, is introduced. It allows quantitative and structural analysis of metabolic networks in a graphical user interface. Copyright 2002 John Wiley & Sons, Inc. Biotechnol Bioeng 77: 734-751, 2002; DOI 10.1002/bit.10153

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Year:  2002        PMID: 11835134     DOI: 10.1002/bit.10153

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  31 in total

1.  Experimental identification and quantification of glucose metabolism in seven bacterial species.

Authors:  Tobias Fuhrer; Eliane Fischer; Uwe Sauer
Journal:  J Bacteriol       Date:  2005-03       Impact factor: 3.490

2.  Calculating as many fluxes as possible in underdetermined metabolic networks.

Authors:  Steffen Klamt; Stefan Schuster
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

3.  Combinatorial complexity of pathway analysis in metabolic networks.

Authors:  Steffen Klamt; Jörg Stelling
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

4.  On dynamically generating relevant elementary flux modes in a metabolic network using optimization.

Authors:  Hildur Æsa Oddsdóttir; Erika Hagrot; Véronique Chotteau; Anders Forsgren
Journal:  J Math Biol       Date:  2014-10-17       Impact factor: 2.259

Review 5.  Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies.

Authors:  Predrag Horvat; Martin Koller; Gerhart Braunegg
Journal:  World J Microbiol Biotechnol       Date:  2015-06-12       Impact factor: 3.312

6.  Analysis of optimality in natural and perturbed metabolic networks.

Authors:  Daniel Segrè; Dennis Vitkup; George M Church
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-01       Impact factor: 11.205

7.  Calvin cycle mutants of photoheterotrophic purple nonsulfur bacteria fail to grow due to an electron imbalance rather than toxic metabolite accumulation.

Authors:  Gina C Gordon; James B McKinlay
Journal:  J Bacteriol       Date:  2014-01-10       Impact factor: 3.490

8.  Carbon dioxide fixation as a central redox cofactor recycling mechanism in bacteria.

Authors:  James B McKinlay; Caroline S Harwood
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-17       Impact factor: 11.205

Review 9.  Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism.

Authors:  Cong T Trinh; Aaron Wlaschin; Friedrich Srienc
Journal:  Appl Microbiol Biotechnol       Date:  2008-11-15       Impact factor: 4.813

10.  A possibilistic framework for constraint-based metabolic flux analysis.

Authors:  Francisco Llaneras; Antonio Sala; Jesús Picó
Journal:  BMC Syst Biol       Date:  2009-07-31
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