Hosted by: Dr. Ian Blaby

The rapidly increasing quantity of biological data offers novel and diverse resources to study biological functions at the system level. Integrating and mining these various large-scale datasets is both a central priority and a great challenge for the field of systems biology and necessitates the development of specialized computational approaches. In this talk, I will present several novel computational systems approaches in a multi-scale modeling framework to study gene expression and regulation: 1) an algorithm to simultaneously cluster multi-layer networks such as gene co-expression networks across multiple species, which discovered novel human developmental genomic functions and behaviors; 2) a logic-circuit based method to identify the genome-wide cooperative logics among gene regulatory factors and pathways for the first time in cancers such as acute myeloid leukemia, which provided unprecedented insights into the gene regulatory logics in complex biological systems; 3) an integrated method using the state-space model and dimensionality reduction to identify principal temporal expression patterns driven by internal and external gene regulatory networks, which established an entirely new analytical platform to identify systematic and robust dynamic patterns from high dimensional, complex and noisy biomedical data. In addition, I will introduce some ongoing research projects and discuss the future directions where multi-scale approaches can make a significant impact in systems biology.