» Articles » PMID: 25701571

TEFMA: Computing Thermodynamically Feasible Elementary Flux Modes in Metabolic Networks

Overview
Journal Bioinformatics
Specialty Biology
Date 2015 Feb 22
PMID 25701571
Citations 15
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.

Mahout M, Carlson R, Simon L, Peres S NPJ Syst Biol Appl. 2024; 10(1):34.

PMID: 38565568 PMC: 10987626. DOI: 10.1038/s41540-024-00360-6.


On the representativeness and stability of a set of EFMs.

Guil F, Hidalgo J, Garcia J Bioinformatics. 2023; 39(6).

PMID: 37252834 PMC: 10264373. DOI: 10.1093/bioinformatics/btad356.


Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities.

Beck A, Kleiner M, Garrell A Front Plant Sci. 2022; 13:910377.

PMID: 35795346 PMC: 9251461. DOI: 10.3389/fpls.2022.910377.


Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth.

Muller S, Szeliova D, Zanghellini J PLoS Comput Biol. 2022; 18(2):e1009843.

PMID: 35104290 PMC: 8853647. DOI: 10.1371/journal.pcbi.1009843.


Addressing uncertainty in genome-scale metabolic model reconstruction and analysis.

Bernstein D, Sulheim S, Almaas E, Segre D Genome Biol. 2021; 22(1):64.

PMID: 33602294 PMC: 7890832. DOI: 10.1186/s13059-021-02289-z.