|Gene Regulatory Networks||Molecular Mechanisms of Aging||Human genetics|
Gene Regulatory Networks
We seek a systems level quantitative understanding of the structure, function, and evolution of gene regulatory networks. We take an interdisciplinary approach that combines exquisitely quantitative experimentation with theoretical modeling and bioinformatic analysis to accurately reconstruct cellular regulatory networks, to study their dynamical response to environmental fluctuations, and to derive principles governing their evolution. We have developed automated flow cytometry systems to study the dynamics of gene expression with high temporal resolution and high throughput. Coupled with evolutionary analysis, we are searching for general design principles for gene regulatory networks. Currently we are focusing on the regulation of stress response and metabolic pathways, both are closely tied with aging, the second area of our research.
Molecular Mechanisms of Aging.
We study the molecular mechanisms of aging using yeast as a model system. We have developed microfluidic systems that allow us to directly observe the aging process at the molecular level in single cells throughout their lifespan. We are developing new genetic systems that enable us to perform high throughput screening of mutations that extend lifespan. We are also using various functional genomics tools to analyze the cellular state and the global regulatory networks that influence/regulate lifespan. Our goal is to build a comprehensive gene regulatory network for aging (by combining genetic analysis, functional genomics, single cell study, and bioinformatics), and to be able to predict from this network the effect of genetic/environmental perturbations on lifespan, and the molecular mechanisms through which lifespan effect is mediated.
|Microfluidic device to study molecular phenotyping of aging in single yeast cells|
Computational Human Genetics. We develop novel computational tools to delineate genetic determinants of complex human phenotypes (such as complex diseases) by combining genome-wide association study with large-scale molecular trait data (such as expression quantitative trait data).