scholarly journals Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones

2021 ◽  
Author(s):  
Isaac Klapper ◽  
Daniel B. Szyld ◽  
Kai Zhao
Keyword(s):  
Metabolic Networks ◽  
Polyhedral Cones ◽  
Elementary Flux Modes

scholarly journals Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

2020 ◽  
Vol 36 (14) ◽  
pp. 4163-4170
Author(s):  
Francisco Guil ◽  
José F Hidalgo ◽  
José M García
Keyword(s):  
Linear Programming ◽  
Metabolic Networks ◽  
Supplementary Information ◽  
Programming Approach ◽  
Supplementary Data ◽  
Main Interest ◽  
Elementary Flux Modes ◽  
Order Of Magnitude ◽  
Genome Scale ◽  
Efficiency Rate

Abstract Motivation Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. Results Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. Availability and implementation Software is freely available at https://github.com/biogacop/Boost_LP_EFM. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals tEFMA: computing thermodynamically feasible elementary flux modes in metabolic networks

2015 ◽  
Vol 31 (13) ◽  
pp. 2232-2234 ◽  
Author(s):  
Matthias P. Gerstl ◽  
Christian Jungreuthmayer ◽  
Jürgen Zanghellini
Keyword(s):  
Metabolic Networks ◽  
Elementary Flux Modes

scholarly journals Computing the shortest elementary flux modes in genome-scale metabolic networks

2009 ◽  
Vol 25 (23) ◽  
pp. 3158-3165 ◽  
Author(s):  
Luis F. de Figueiredo ◽  
Adam Podhorski ◽  
Angel Rubio ◽  
Christoph Kaleta ◽  
John E. Beasley ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Elementary Flux Modes ◽  
Genome Scale

Identification of flux regulation coefficients from elementary flux modes: A systems biology tool for analysis of metabolic networks

2007 ◽  
Vol 97 (6) ◽  
pp. 1535-1549 ◽  
Author(s):  
Intawat Nookaew ◽  
Asawin Meechai ◽  
Chinae Thammarongtham ◽  
Kobkul Laoteng ◽  
Vasimon Ruanglek ◽  
...  
Keyword(s):  
Systems Biology ◽  
Metabolic Networks ◽  
Elementary Flux Modes

scholarly journals Finding MEMo: Minimum sets of elementary flux modes

2019 ◽  
Author(s):  
Annika Röhl ◽  
Alexander Bockmayr
Keyword(s):  
Steady State ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Point Of View ◽  
Computational Systems Biology ◽  
Huge Number ◽  
Elementary Flux Modes ◽  
Practical Point ◽  
Generating Sets ◽  
Computational Systems

AbstractMetabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

Sign in / Sign up

Export Citation Format

Share Document