The advent of high throughput technologies including DNA sequencing and microarray experiments has reshaped the landscape of molecular and cellular biology; A new field known as computational systems biology (CSB) has emerged to be a central discipline. This research investigates Bayesian signal processing solutions that enable uncovering gene regulatory networks (GRNs), the regulatory systems linking proteins and targets. Uncovering GRNs will help elucidate biological system structure, system dynamics, control mechanisms, and the design principles, thereby providing clues for new therapeutics strategies.
At the heart of the GRNs research are system modeling, inference, and data integration, all of which are the key issues of Bayesian signal processing. This research aims to: (1) investigate a novel graphical modeling framework for accurate and robust modeling of gene regulation; (2) investigate a novel Bayesian framework for systematic and efficient data integration; (3) investigate systematic procedures for effective results validation and error analysis; (4) apply the developed solutions to uncover the cell cycle GRNs of yeast and malaria parasite. This research is motivated by the inadequacy of current research in modeling, data integration, and results validation, and the shortfall in the practices that are inharmonious with the iterative nature of CSB. This research is anticipated to culminate in the formulation of powerful modeling and data integration frameworks that will significantly improve the accuracy and robustness of the uncovered GRNs, and under which further improvements and extensions to systems beyond genes can be systematically carried out. The research is also integrated with signal processing curriculum advance research training, and education of students at all levels. It promotes diversity and outreach in the area of CSB.
