A cooperative strategy for parameter estimation in large scale systems biology models.

ABSTRACT:

Background

Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently.

Results

A new approac

SUBMITTER: Villaverde AF 

PROVIDER: S-EPMC3512509 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature