A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks.

ABSTRACT:

Background

Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation.

Results

We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal prot

SUBMITTER: Yugi K 

PROVIDER: S-EPMC1326213 | biostudies-literature | 2005 Dec

REPOSITORIES: biostudies-literature