We use systems biology approaches to uncover the underlying principles governing the operation of genetic networks. Specifically, we integrate computational modeling and data analysis to elucidate the relationship among robustness of network dynamics, stochasticity in gene expression and heterogeneity in a cell population. Our studies will contribute to a systems-level understanding of biological processes determining cellular state transitions.
New advances in genomics provide new opportunities for Systems Biology modeling
With the new advances in technologies, researchers can not only measure multiple types of genomics data at the same time, but also at single-cell resolution, with temporal and spatial information. Most importantly, these data are made publicly available almost immediately.
We first integrate both literature and genomics data to construct a large gene network with bioinformatics. Then using mathematical modeling, we aim to identify a core gene regulatory circuit. Mathematical modeling can also simulate circuit dynamics, from which we propose new predictions that can be tested experimentally. This is an iterative process to improve circuit models.