DESCRIPTION (provided by applicant): The emergence of high-throughput technologies has made it feasible to measure the activities of thousands of genes simultaneously, which provides scientists a major opportunity to infer global gene regulatory networks (GRNs). Accurate inference of GRNs is very important, which allows people to gain a systematic understanding of the molecular mechanism, to shed light on the mechanism of diseases that occur when cellular processed are dysregulated, and furthermore to identify potential therapeutic targets for diseases. Given the high dimensionality and high complexity of high-throughput data, inference of global GRNs largely relies on the advance of computational methods. However, the current computational methods for inference of global GRNs are either inaccurate or computationally infeasible. How to infer global GRNs has put a great challenge on the current statistical methodology. The investigators propose an equivalent measure of partial correlation coefficients, and based on which develop an innovative computational framework for inference of global GRNs. The new measure of partial correlation coefficients can be evaluated with a reduced conditional set and thus feasible for high dimensional problems. Under the new framework, the investigators develop a series of algorithms which are able to provide a comprehensive inference for global GRNs by integrating various types of high-throughput molecular profiling data, e.g., mRNA expression, copy number variation, methylation, microRNA, and protein expression, and adjusting with various clinical covariates, e.g., age, gender, and disease stage. The proposed algorithms are applied to infer the global GRNs for various types of cancer with a special focus on lung cancer, while they are applicable to all other types of diseases. Statistically, this project proposes an innovative framework for inference of global GRNs, and the algorithms developed under which are not only computationally efficient, but also very flexible in data integration, covariate adjustment, prior knowledge integration, and network comparison. Biomedically, this is the first study to integrate such comprehensive and complementary information using rigorous statistical methods to study the global GRNs for various types of cancer.

PUBLIC HEALTH RELEVANCE: This project will develop a novel computational framework to reconstruct global gene regulatory networks using comprehensive molecular profiling data. Context-specific global gene regulatory networks could provide people a systematic understanding of the molecular mechanisms underlying diseases and facilitate the identification of therapeutic targets, which can subsequently improve patient care.