Skip to Main Content
One of the main aims of system biology is to understand the structure and dynamics of genomic systems. A computational approach, facilitated by new technologies for high-throughput quantitative experimental data, is put forward to investigate the regulatory system of dynamic interaction among genes in Kaposi's sarcoma-associated herpesvirus network after induction of lytic replication. A reconstruction of transcription factor activity and gene-regulatory kinetics using data from a time-course microarray experiment is proposed. The computational approach uses nonlinear differential equations. In particular, the quantitative Michaelis-Menten model of gene- regulatory kinetics is extended to allow for post-transcriptional modifications and synergic interactions between target genes and the Rta transcription factor. The kinetic method is developed within a Bayesian inferential framework using Markov chain Monte Carlo. The profile of the Rta transcriptional regulator, other post- transcriptional regulatory genes and gene-specific kinetic parameters are inferred from the gene expression data of the target genes. The method described here provides an example of a principled approach to handle a wide range of transcriptional network architectures and regulatory activation mechanisms to reconstruct the activity of several transcription factors and activation kinetic parameters in a single regulatory network.
Date of Publication: November 2008