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

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 protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. Conclusion: We have demonstrated the MASK method for the const...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Katsuyuki Yugi, Yoichi Nakayama, Shigen Kojima, Tomoya Kitayama, Masaru Tomita
• Keywords: BMCBI 2005, Genetic Networks, Genetic Regulatory Networks, Regulatory Networks