Statistical methods for integromics discoveries

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DESCRIPTION (provided by applicant): Contemporary systems biology is shifting the paradigm of biomedical research from minimalistic studies of individual genes/proteins to integration of information at systems level. Current high throughput biotechnologies enable collection of a large amount of biological information, and the different aspects of the cellular systems are reflected with heterogeneous data, e.g., genomics, epigenomics, transcriptomics and metabolomics. However, it remains a major challenge to systematically integrate this body of information and derive biological insights at a mechanistic level. The overarching goal of this project is to develop a computational system that enables integration of various high throughput "omics" data (an "integromics" approach) to gain insights into cellular systems, in particular the signal transduction systems. The activities of the project are organized into four specific aims, which progress from approaches for capturing general information among the multiple omics data to more specific and complex models designed to decipher specific cellular signaling systems. Firstly, we will develop a general framework, based on information theory and probabilistic models, to identify information modules that convey biological information between different "omics" data at large scale. Secondly, we develop methods to further investigate if the information from the multiple omics data reflects causal relationships. Thirdly, we will develop tools to recover missing information from the system to augment the high throughput technologies. Finally, we will develop a unified model to elucidate signal transduction pathways by integrating information form multiple omics data in manner that is both biologically sensible and mathematically rigorous. We expect that the methodologies developed in the project are widely applicable to study a variety of cellular signal transduction systems.