Computational inference of replication and transcription activator regulator activity in herpesvirus from gene expression data

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.