Our lab is interested in quantitative analysis of biological processes. We use experiment and/or computational approaches to understand biological systems. In particular, we focus on following areas:
Physiological function of double-stranded RNAs and innate immune response proteins
Cellular dsRNAs are emerging as a new class of signaling molecules that regulate multiple signaling pathways. dsRNAs were originally considered as signature of viral RNAs and hence, the amount and type of cellular dsRNAs were believed to be highly limited. However, single-stranded RNAs can locally adapt secondary structure and can form intramolecular dsRNAs. Studies on cellular dsRNAs are necessary to understand and unravel the extent to which this new class of biomolecules plays a role in cell fate determination. As dsRNAs are often recognized by immune response proteins, investigating regulation of these RNAs in cells will also provide an important step toward better understanding of host immune response during infection or autoimmune disease.
Quantitative imaging analysis of cell cycle
Cell cycle is a highly orchestrated process where gene expression is controlled through multiple regulatory layers to ensure proper DNA replication and chromosome segregation. Immunofluorescence and live-imaging techniques have allowed us to observe expression of key regulatory proteins and their dynamics during cell cycle progression. Our goal here is to develop computational tools to quantitatively assess the protein expression, localization, and kinetics to formulate mathematical models and elucidate processes that governs the cell cycle.
Physicochemical models of transcriptional networks during embryo development
We are interested in investigating a transcriptional network where multiple transcription factors interact to give rise to complex gene expression patterns. For example, in fly embryo development, interactions among different maternal morphogens give rise to striped patterns of the gap genes, which are involved in specifying segmentation of the embryo. These genes are first induced by maternal factors and later their expression boundaries are refined through gap gene cross-regulation network. To better understand the transcriptional networks during embryo development, we combine computational modeling and genetic experiments to develop dynamic reaction-diffusion models. The models will be validated using gap gene expression patterns in mutant and transgenic backgrounds with varying levels of the maternal inputs. As a virtually one-dimensional system, the expression of gap genes provides an attractive system to analyze how different signaling systems interact at regulatory regions of DNA to control gene expressions.