Book Description
High-throughput genomics has been increasingly generating the massive amount of genome-wide data. With proper modeling methodologies, we can expect to archive a more comprehensive understanding of the regulatory mechanisms of biological systems. This work presents integrative approaches for the modeling and analysis of gene regulatory systems. In mammals, gene expression regulation is combinatorial in nature, with diverse roles of regulators on target genes. Microarrays (such as Exon Arrays) and RNA-Seq can be used to quantify the whole spectrum of RNA transcripts. ChIP-Seq is being used for the identification of transcription factor (TF) binding sites and histone modification marks. RNA interference (RNAi), coupled with gene expression profiles, allow perturbations of gene regulatory systems. Our approaches extract useful information from those genome-wide measurements for effectively modeling the logic of gene expression regulation. We present a predictive model for the prediction of gene expression from ChIP-Seq signals, based on quantitative modeling of regulator-gene association strength, principal component analysis, and regression-based model selection. We demonstrate the combinatorial regulation of TFs, and their power for explaining genome-wide gene expression variation. We also illustrate the roles of covalent histone modification marks on predicting gene expression and their regulation by TFs. We present a dynamical model of gene expression profiling, and derive the perturbed behaviors of the ordinary differential equation (ODE) system. Based on that, we present a regularized multivariate regression method for inferring the gene regulatory network of a stable cell type. We model the sparsity and stability of the network by a regularization approach. We applied the approaches to both a simulation data set and the RNAi perturbation data in mouse embryonic stem cells.
