Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization.

ABSTRACT:

Background

Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality.

Results

Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which c

SUBMITTER: Guillen-Gosalbez G 

PROVIDER: S-EPMC3832746 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature