SYSTEM BIOLOGY APPROACHES TO THE IDENTIFICATION OF COMPLEX REGULATORY NETWORKS

The regulation and function of disease-associated genes is of crucial importance towards understanding molecular mechanisms underlying the disease. Mechanisms of pathogenesis have proven to be difficult to explain because of an inability to understand the complex biological mechanisms involved in the regulation and function of disease genes. Genes, proteins and metabolites are organized into extensive networks that enable a cell to respond, adapt and communicate with its environment. The extent and complexity of such networks can hinder attempts to elucidate their structure and function. In this project we plan to develop: (1) a software toolbox to simulate the behaviour of known complex regulatory network in different experimental conditions in silico; (2) a rapid and scalable method that enables construction of a first-order quantitative model of a gene and protein regulatory network using no prior information on the network structure or function. The model can be used to identify the regulatory role of individual genes in an unknown network; in particular, it can identify those genes with major regulatory functions. In addition, the model can be applied to the RNA expression measurements obtained from pharmacological perturbations to identify the genes that directly mediate a compound’s bioactivity in the cell. Thanks to our proposed approach, it will be possible to identify for a gene of interest (i.e. a disease gene) the regulatory network in which the gene is embedded, and therefore understand how it is regulated and what other genes it regulates, both in physiological and pathological conditions. This will be very important to elucidate the molecular mechanisms responsible for the disease.