Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

ABSTRACT: Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly

SUBMITTER: Robaina Estevez S 

PROVIDER: S-EPMC4497637 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature