title: Algorithmic approaches for computing elementary modes in large biochemical reaction networks.
abstract: The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.
authors.affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. klamt@mpi-magdeburg.mpg.de
authors.forename: S
authors.initials: S
authors.lastname: Klamt
authors.forename: J
authors.initials: J
authors.lastname: Gagneur
authors.forename: A
authors.initials: A
authors.lastname: von Kamp
mesh_terms: D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods*; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
chemical_list: D020543:Proteome