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Research Interest

Fundamental regulatory mechanisms of cell fates

Stochastic control of cell fates in HIV

Integrative multi-scale modeling for biological networks

 

Mathematical and computational modeling play critical roles in understanding complex biological processes in diseases and tissue development. My goal is to understand the molecular- and cellular-level regulatory mechanisms in important biological networks, such as in HIV infection, using mathematical and computational modeling.

I have developed the Accurate Chemical Master Equation (ACME) method to directly solve the Chemical Master Equation (CME) with quantified state space truncation error. With ACME, we can dramatically reduce the state space of CME, so many unsolvable realistic problems using other methods can be tackled using the ACME. For example, the reduction in state spaces can be 9 orders of magnitude in some complex network. With the ACME, we can compute the detailed probability landscape of the biological network at the steady state and during time evolution, so we can study the full stochastic behavior of the network. We can also compute the rare event probabilities and first passage time distributions (FPTD) from one state to another. With these advantages, the ACME method has great potential to be applied to study broad issues in systems biology.

Currently, I am applying the ACME to understand the stochastic cell fate control in HIV infected cells. This research will help to reveal the control mechanism in HIV latency, and help to identify better potential drug targets from the complex gene regulatory network.