Response of cells to their changing environment is governed by intricate regulations of gene expression by regulating molecules in cells including, most importantly, transcription factors (TFs) and microRNAs (miRNAs). Understanding how TF and miRNA regulations define cellular states such as cell survival, cell proliferation, and cell death, and eventually phenotypes including various diseases is a major challenge facing computational systems biologists.
Intellectual Merit This EAGER project will develop and validate a novel computational model called Semi-parametric Bayesian FActor Regulatory Model (SB-FARM) for TF and miRNA regulation of gene expression. The advantages of SB-FARM over other existing models are that it integrates existing knowledge about gene regulations in the model and enables the discovery of specific regulations by TFs and miRNAs under unmeasured conditions or contexts. The investigators will examine the modeling details as well as algorithms for reconstructing the model from a set of gene expression data. They will apply SB-FARM to study the context-specific regulations in E-coli and human cancer.
The long term goal of the investigators is to develop signal processing and statistical learning methods for the system-level understanding of gene regulatory networks underlying different biological processes and apply them to better understand the genomic basis for diversity in organisms and development of diseases. The SB-FARM has a general structure that permits integration of additional aspects of gene regulation. The success of the SB-FARM will have long lasting impact on gene regulation research and is expected to also significantly advance statistical signal processing and Bayesian learning.
Broader Impact This research is highly interdisciplinary, cross-cutting science and engineering. It will provide an environment for advanced interdisciplinary learning and education in the area of genomics signal processing and computational biology. The PIs will also actively involve graduate and undergraduate students in research activities. Particularly, they will utilize the minority institution status of UTSA and UTHSCSA to recruit and involve minority students in this research. The developed computational methods will be implemented into publically available software to aid computational biology researchers to investigate context specific gene regulations. The computational methods and tools will enhance the signal processing and machine learning research and ultimately lead to development of new theory and methods.
