When the optimal is not the best: parameter estimation in complex biological models.

ABSTRACT:

Background

The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values.

Results

We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explici

SUBMITTER: Fernandez Slezak D 

PROVIDER: S-EPMC2963600 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature