|Ph.D Thesis||Department of Computer Science|
|Supervisors:||Prof. Pinter Ron|
|Assoc. Prof. Hanah Margalit|
Cellular processes are regulated by interactions between various types of molecules, such as proteins, genes, and metabolites. Among these, the interactions between proteins and the interactions between transcription factors and their target genes play a prominent role, controlling the activity of proteins and the expression levels of genes. A significant number of such interactions has been recently revealed via high throughput technologies. These data can be represented as a network of interactions describing the circuitry responsible for the regulation of a variety of cellular processes. Analysis of this cellular circuitry is one of the major research goals in the post genomic era.
Previous studies have analyzed aspects of this circuitry, concentrating on either transcription regulation or protein-protein interactions. We systematically analyze the integrated cellular circuitry comprising both types of interactions, focusing on the detection of functional mechanisms via topological analyses. We developed a scheme for the detection of simple and complex mixed feedback regulation using a novel application of classical graph algorithms. To study mechanistic modules composing the cellular circuitry we developed a new algorithm for detecting composite motifs in networks comprising two or more types of connections. By applying our schemes to data of the yeast Saccharomyces cerevisiae we were able to identify known and novel mechanisms which provide new insight into the processes that take place in the cell.
Our study presents a general framework which can be utilized to analyze any network with more than one type of connection.