Computational Methods for Functional Genomic Discovery from Gene Knockout Studies

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DESCRIPTION (provided by applicant): The overall Phase I objective is to prototype a new computational tool to assess the feasibility of identifying and predicting gene functional and interactive relationships from gene expression data obtained from multi- comparative gene knockout studies. NIH's Knockout Mouse Project is an initiative to generate a public resource of mouse embryonic stem (ES) cells containing a null mutation in every gene in the mouse genome, important for the study of diseases and for deciphering the complexity of biological systems of mice and ultimately in man. It is anticipated that a new generation of comparative knockout studies with a biological will emerge in all areas of biomedical research. Having new computational methods for identifying and deciphering genetically regulated response (e.g. signaling cascades) will significantly aid in advancing our understanding of the molecular targets and mechanisms of many diseases and will be extremely important for making medical breakthroughs, especially for the safe and effective development of drugs and diagnostics. Today, researchers are hindered by the tremendous volumes of data generated from knockout investigations. Seralogix plans to build upon the capabilities of our existing Biosystem Analysis Framework (BAF) that is comprised of a suite of integrated analysis and mathematical modeling tools for gene and protein analysis. Our core tools are based on the statistical power of Dynamic Bayesian Networks (DBNs) that is built on sound statistical methods that allow us to combine prior knowledge with empirical time-course data for modeling, pattern recognition and genetic network inference. Our innovation, proposed herein, is to integrate the }multi- perturbation Shapley Value Analysis (MSA) method with our DBNs for improved genetic network discovery. MSA is based on concepts from game theory that we will utilized to identify the relationships/importance of each element (genes) in a system with respect to all other elements. We hypothesize that MSA integrated with our DBN inference engine in conjunction with time-course gene expression data from multiple gene KO experiments will result in improved genetic interactive discovery leading to a more robust mathematical/functional model system. If successful, the inclusion of MSA into Seralogix's suite of analysis and modeling tools will provide an important new tool for genetic functional and interactive relationship discovery. Seralogix believes that such a new tool will have significant commercial potential and will make a major contribution to the scientific community at large. The Phase I goals for our proposed tool will be to: 1) design and implement our DBN/MSA multi-conditional KO comparative methodology for discovery of underlying genetic networks; and 2) demonstrate proof-of-feasibility of the DBN/MSA methodology on mice gene KO expression data for folate receptor and related signaling pathways provided to us from the Texas Institute of Genomic Medicine (TIGM) as part of their ongoing birth defect research. It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that control health and disease, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. Having new computational methods (software tools) for identifying and deciphering genetically regulated response (e.g. signaling cascades) will significantly aid in advancing our understanding of the molecular targets and mechanisms of many diseases of high public health concern. The discovery of underlying genetic function and relationships will be extremely important for making medical breakthroughs, especially for the safe and effective development of drugs and diagnostics. Today, researchers are hindered by the tremendous volumes of gene/protein expression data generated from knockout investigations. Computational tools that transform these volumes of raw genomic/proteomic data to actionable knowledge via mathematical modeling will help guide and accelerate researchers' investigations of genetic disorder and identifying targets of intervention and treatment.