CIF: Small: Sparse Signal Processing Methods for Inference of Differential Gene Regulatory Networks

University Of Miami, Coral Gables FL

Investigators

Abstract

Genes in living cells interact with each other and form a complex network to regulate molecular functions and biological processes. Recent studies have shown that gene regulatory networks (GRNs) can undergo substantial rewiring when cells respond to different environmental signals. More generally, differential changes of GRNs can occur depending on environment, tissue type, disease state, development and speciation. Identification of such changes in GRNs is thus an important step to fully understand how GRNs work, and may have profound impact on the understanding of various biological processes and on biomedical research. However, current computational methods are focused on the inference of GRNs under a single condition or attempt to identify differential gene co-expression. No method currently exists that is capable of systematically identify structural changes in GRNs. The objectives of this project are to design systematic methods that can exploit gene expression and other relevant data to infer structural changes of GRNs, and to develop software that implements such inference methods. Intellectual merit: The proposed inference methods seek to leverage an important attribute of GRNs, the sparsity in both GRNs and their structural changes, and they are built on the innovative ideas of sparse signal processing. They employ novel inference techniques to integrate various types of data into a unified framework. This systematic approach is expected to significantly improve inference accuracy over existing ad hoc methods. The proposed methods and the resulting software will provide a valuable tool for the analysis of differential GRNs and help to uncover differential gene-gene interactions under different conditions. Although mathematical formulations are framed for the problem of inference of differential GRNs, the underlying mathematical ideas and the associated inference methods may also find applications in more general domain of sparse signal processing and sparse model learning. Broader impact: Successful completion of the proposed project will have a broad impact on the fields of signal processing, systems biology, and medicine. It will contribute a novel set of inference methods to the field of sparse signal processing. It will also help to understand the dynamic changes of GRNs that occur in various disease states, during cell differentiation, or in cellular response to environmental changes. In particular, understanding differential gene-gene interactions in genetic diseases such as cancer will open the door to new diagnostics and therapeutics development. Overall, the proposed research will help to discover new knowledge and potentially find applications in medical research, thereby benefiting society as a whole. The proposed project will also positively impact interdisciplinary education at both graduate and undergraduate levels, and attract minority students to be involved in cutting edge research.