Automated refinement and inference of analytical models for metabolic networks.

ABSTRACT: The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillatio

SUBMITTER: Schmidt MD 

PROVIDER: S-EPMC4109817 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature