Systems analysis by Quantitative Cellular Assays, Network Modeling and Integration of Metadata

We will develop a strategy to elucidate complex biological networks by deriving computational models from quantitative cellular assays. Our project combines technology and expertise from three fields:
genomics, which evaluates the inventory and organization of all genes, forward genetics, which illuminates the members and mechanisms of cellular pathways, and computational biology, which facilitates statistical data analysis and the integration and modeling of complex metadata.
Using quantitative functional analysis, we will measure genome-wide cellular phenotypes in response to RNAi perturbation of each gene function. We will identify and compare the genetic pathways required for cell death upon various inductions such as cytotoxic stress and hormone treatment, revealing how different conditions lead to the same cellular response. We will also determine the pathways induced by hormone treatment in different cell types, revealing how the same conditions can result in different cellular response. Of importance in employing large-scale experimental data into networks is software and methodology capable of handling the complex data structures. We will develop statistically rigorous visualization and inference methods. From our theoretical network predictions, we will design directed experiments to test our models of cellular regulation.