Multiscale stochastic computational modeling of biological systems.

Viral infection


Wound healing


Tissue pattern


Mathematical and computational modeling can help to reveal control mechanisms of complex biological processes in diseases, such as viral infection and cancer. My research is focusing on both developing novel modeling methods and applying them to study realistic regulatory mechanisms of multi-scale complex biological networks.

I developed the ACME method for accurate and direct solution of chemical master equation (CME) [CL8, CLL, CTL]. It can be used to study the steady state and time-evolving probability landscapes in stochastic biological networks. It can also be used to compute the rare event probabilities and first passage time distributions. Currently, I am developing a multiscale stochastic modeling framework for simulating tissue patterns [CNL, CLN].

Current goal of my research is to reveal the stochastic control mechanisms in HIV infection by combining complex data fitting and intracellular stochastic circuit modeling. We have studied the stochastic cell fate decision in phage lambda lysogenic-lytic switch [CLL], and also studied tissue size control [CLN] with stem cell lineage and human skin wound healing problem (collaborated with experimentalists).  

Research Interest

1. Stochastic control in HIV and HCV infection.

2. Multiscale stochastic control in wound healing and cancer.

3. Developing multiscale stochastic modeling methods for complex biological processes.