Matthias P Gerstl1, Christian Jungreuthmayer1, Jürgen Zanghellini1. 1. Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
Abstract
UNLABELLED: : Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: https://github.com/mpgerstl/tEFMA CONTACT: : christian.jungreuthmayer@boku.ac.at or juergen.zanghellini@boku.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: : Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: https://github.com/mpgerstl/tEFMA CONTACT: : christian.jungreuthmayer@boku.ac.at or juergen.zanghellini@boku.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Matthias P Gerstl; David E Ruckerbauer; Diethard Mattanovich; Christian Jungreuthmayer; Jürgen Zanghellini Journal: Sci Rep Date: 2015-03-10 Impact factor: 4.379
Authors: Steffen Klamt; Georg Regensburger; Matthias P Gerstl; Christian Jungreuthmayer; Stefan Schuster; Radhakrishnan Mahadevan; Jürgen Zanghellini; Stefan Müller Journal: PLoS Comput Biol Date: 2017-04-13 Impact factor: 4.475
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