Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Open Badges Research article

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

Katsuyuki Yugi, Yoichi Nakayama*, Shigen Kojima, Tomoya Kitayama and Masaru Tomita

Author Affiliations

Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035, Japan

For all author emails, please log on.

BMC Bioinformatics 2005, 6:299  doi:10.1186/1471-2105-6-299

Published: 13 December 2005



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.


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 protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model.


We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.