Computationally efficient flux variability analysis

By Steinn Gudmundsson, Ines Thiele. BMC Bioinformatics, 2010

369 citations recorded by the source corpus.

BackgroundFlux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.ResultsWe present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.ConclusionsNetworks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/41cc25f8f7a361cc32a8ca84cd29a6d98716b2e2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Molecular Biology

Citations in the corpus

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “Alternative optimal solutions are enumerated using mixed-integer linear programing (MILP) 47 , and the ranges spanned by alternative optima are found for each reaction using flux variability analysis 48,49 (FIG.”