Inferring regulatory networks from multiple sources of genomic data

Abstract

(cont.) algorithm to identify the regulatory models from protein-DNA binding and gene expression data. These models to a large extent agree with the knowledge of gene regulation pertaining to the corresponding regulators. The three works in this thesis provide a framework of modeling gene regulatory networks.

This thesis addresses the problems of modeling the gene regulatory system from multiple sources of large-scale datasets. In the first part, we develop a computational framework of building and validating simple, mechanistic models of gene regulation from multiple sources of data. These models, which we call physical network models, annotate the network of molecular interactions with several types of attributes (variables). We associate model attributes with physical interaction and knock-out gene expression data according to the confidence measures of data and the hypothesis that gene regulation is achieved via molecular interaction cascades. By applying standard model inference algorithms, we are able to obtain the configurations of model attributes which optimally fit the data. Because existing datasets do not provide sufficient constraints to the models, there are many optimal configurations which fit the data equally well. In the second part, we develop an information theoretic score to measure the expected capacity of new knock-out experiments in terms of reducing the model uncertainty. We collaborate with biologists to perform suggested knock-out experiments and analyze the data. The results indicate that we can reduce model uncertainty by incorporating new data. The first two parts focus on the regulatory effects along single pathways. In the third part, we consider the combinatorial effects of multiple transcription factors on transcription control. We simplify the problem by characterizing a combinatorial function of multiple regulators in terms of the properties of single regulators: the function of a regulator and its direction of effectiveness. With this characterization, we develop an incremental