A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

ABSTRACT:

Background

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging.

Results

To address the issue of inadequate experimental data, we propose a continuous optimization appr

SUBMITTER: Deng Z 

PROVIDER: S-EPMC4261783 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature