Sparsity as cellular objective to infer directed metabolic networks from steady-state metabolome data: a theoretical analysis.

ABSTRACT: Since metabolome data are derived from the underlying metabolic network, reverse engineering of such data to recover the network topology is of wide interest. Lyapunov equation puts a constraint to the link between data and network by coupling the covariance of data with the strength of interactions (Jacobian matrix). This equation, when expressed as a linear set of equations at steady state, constitutes a basis to infer the network structure given the covariance matrix of data. The sparse structure of metabolic networks points to reactions which are active based on minimal enzyme production, hinting at sparsity as a cellular objective. Therefore, for a given covariance matrix, we solved Lyapunov equation to calculate Jacobian matrix by a simultaneous use of minimization of Euclidean norm

SUBMITTER: Oksuz M 

PROVIDER: S-EPMC3877278 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature