Our group is responsible for the experimental component of a NIH-funded project led by John Tyson (Dpt of Biological Sciences, Virginia Tech) to develop a stochastic model of the cell cycle. We develop time-lapse microscopy methods to quantify with a single cell resolution, the abundance of the mRNA and proteins involved in the cell cycle machinery along with protein-protein interactions. We also develop data reduction algorithms to extract statistical models that can be compared to the stochastic model of the cell cycle control.

We also participate in another NIH-funded project led by T.M. Murali (Dpt of Computer Sciences, Virginia Tech). The goal of this project is to develop a framework for generating hypotheses from top-down models, test these hypotheses by integrating them into bottom-up models, and validating the hypotheses using experiments. We are using the control of the cell cycle in yeast as a test case. With an improved understanding of cell cycle regulation in budding yeast, we should be able to suggest novel experiments that provide a better understanding of molecular control systems. We are expecting that the methods developed in this project should be relevant to the study of any complex cellular system, including the development of cancer and the spread of infectious diseases. If we are successful, our project will result in significant advances in computationally driven experimental biology.