1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg2School of Computer Science, Algorithms and Randomness Center, Georgia Institute of Technology, Atlanta, GA, USA
Checking for direct PDF access through Ovid
Summary:In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.Availability and Implementation:https://github.com/opencobra/cobratoolbox.Contact:firstname.lastname@example.org or email@example.comSupplementary information:Supplementary data are available at Bioinformatics online.