Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

ABSTRACT: The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metaboli

SUBMITTER: De Martino D 

PROVIDER: S-EPMC4388631 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature