Model selection in the reconstruction of regulatory networks from time-series data.

ABSTRACT:

Background

A widely used approach to reconstruct regulatory networks from time-series data is based on the first-order, linear ordinary differential equations. This approach is justified if it is applied to system relaxations after weak perturbations. However, weak perturbations may not be informative enough to reveal network structures. Other approaches are based on specific models of gene regulation and therefore are of limited applicability.

Findings

We have developed a generalized approach for the reconstruction of regulatory networks from time-series data. This approach uses elements of control theory and the state-space formalism to approximate interactions between two observable nodes (e.g. measured genes). This leads to a reconstruction model formulated in terms of i

SUBMITTER: Novikov E 

PROVIDER: S-EPMC2688516 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature