Our goal is to advance understanding of complex biological processes using theoretical modeling and computer simulation. This work lies at the intersection of many disciplines, including engineering, mathematics, chemistry, physics, and systems biology.

Underlying all biological processes are complex networks of biochemical reactions. A growing wealth of data revealing where, when, and how these reactions occur is presenting new opportunities to gain quantitative understanding of biological function. With these opportunities comes the challenge of interpreting increasingly detailed, complex molecular data.

To address this challenge, we investigate dynamics of biochemical networks, particularly in the immune system—a highly regulated network of diverse cell-types and molecular effectors. Current areas include:

Stochastic methods

  1. Rare-event simulations of state-transitions in molecular networks. How can we understand and predict the molecular events driving cell-state changes?

  1. Reduction and analysis of complex biological networks. How can we understand global dynamics of multi-stable biological systems? How can we connect dynamic model predictions to experimental data?

Immune cell decision-making

  1. Macrophage polarization. How do cells of the immune system respond to complex biochemical signals? Can we understand immune cell population distributions in terms of sub-cellular and inter-cellular networks?

  1. Regulation of T cell immunity. How can T cell dysfunction in chronic infections and cancer be reversed?

Selection of targets by the immune system. How can we control the development of immunodominance?

Immune escape. How can we model and predict stochastic evolutionary dynamics of viruses and tumors under immune pressure?