Recent advances in molecular technologies have revolutionized the field of biology, and scientists now can collect data on the transcriptomes, proteomes, and metabolomes, at multiple scales, e.g. the whole organism level and the single cell level. Such unprecedented amount of information poses great challenges in data processing, access, modeling, analysis, and interpretation. Although the potential and importance of integrating various types of genomics and proteomics data to dissect biological pathways is well recognized, the valuable information offered in different types of data may not be fully realized without a sound and comprehensive modeling framework to integrate these data. In addition, close collaboration among researchers from different disciplines is essential to ensure that biologically sensible models are developed and the validity of these methods should be rigorously tested through biological experiments. In this project, computational and experimental scientists will join force to develop an integrated approach to modeling and inferring dynamic transcriptional regulatory networks. A better understanding of these networks may offer clues on disease diagnosis, prognosis, and treatment. Our approach provides a comprehensive framework to integrate diverse data types, such as gene expression data, protein-DNA interaction data, mRNA decay data, nucleosome occupancy data, and other data for regulatory network inference. In the applications of the developed methods, we will focus on the yeast cell cycle to statistically infer and experimentally validate regulatory networks. The computer programs and network information generated from this project will be disseminated to the scientific community in a timely manner. ? ? Narrative: Recent years have seen the development of many exciting technologies that allow researchers to study biology at the systems level. For example, the expression levels of all the genes in an organism can be monitors simultaneously. This project will develop comprehensive and powerful statistical methods to systematically model and integrate these data to better understand how genes are regulated in the cell. These powerful computational methods will be implemented and applied to study gene regulations throughout the cell cycle. The computational predictions will be tested by biological experiments, and the computational tools developed will be made available to the general research community. ? ? ?
| Ma, Haisu; Zhao, Hongyu (2013) Drug target inference through pathway analysis of genomics data. Adv Drug Deliv Rev 65:966-72 |
| Ma, Haisu; Zhao, Hongyu (2012) FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment. Bioinformatics 28:2662-70 |
| Ma, Haisu; Zhao, Hongyu (2012) iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics 28:1911-8 |
| Sun, Ning; Zhao, Hongyu (2009) Reconstructing transcriptional regulatory networks through genomics data. Stat Methods Med Res 18:595-617 |
