ArticlePloS one2015
Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- On the representativeness and stability of a set of EFMs.Bioinformatics (Oxford, England) · 2023Article
- Computing optimal factories in metabolic networks with negative regulation.Bioinformatics (Oxford, England) · 2022Article
- Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth.PLoS computational biology · 2022Article
- Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks.Patterns (New York, N.Y.) · 2021Article
- Towards scaling elementary flux mode computation.Briefings in bioinformatics · 2020Article
- Flux tope analysis: studying the coordination of reaction directions in metabolic networks.Bioinformatics (Oxford, England) · 2019Article
- From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints.PLoS computational biology · 2017Review
- Which sets of elementary flux modes form thermodynamically feasible flux distributions?The FEBS journal · 2016Article
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5 authors.
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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.
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