title: Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.
abstract: Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle.
authors.affiliation: Austrian Centre of Industrial Biotechnology, Vienna, Austria, EU; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.
authors.forename: Christian
authors.initials: C
authors.lastname: Jungreuthmayer
authors.affiliation: Austrian Centre of Industrial Biotechnology, Vienna, Austria, EU; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.
authors.forename: David E
authors.initials: DE
authors.lastname: Ruckerbauer
authors.affiliation: Austrian Centre of Industrial Biotechnology, Vienna, Austria, EU; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.
authors.forename: Matthias P
authors.initials: MP
authors.lastname: Gerstl
authors.affiliation: Austrian Centre of Industrial Biotechnology, Vienna, Austria, EU; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanscho
authors.affiliation: Austrian Centre of Industrial Biotechnology, Vienna, Austria, EU; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Zanghellini
mesh_terms: D019295:Computational Biology / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D014158:Transcription, Genetic