The immune system is a fascinating complex system taking decisions on how to respond to a wide variety of stimuli, varying from lethal pathogens to harmless proteins in the food. Decisions are remembered for life in the form of immunological memory. Most of the research in immunology is of a qualitative nature, describing novel cell types, molecules, and genes.

The proper understanding of such a complex systems also requires a more quantitative approach describing the various population sizes, the turnover rates of the cells within each population, their migration rates, and the rates at which cells form contacts with other cells. A major part of our work is to develop a more quantitative immunology by describing the population dynamics of its major populations using a variety of labeling techniques and mathematical modeling to analyze the data.

Pathogens are also fascinating complex systems that by their faster evolution manage to exploit properties of the immune system to elicit inappropriate immune reactions. We study host-pathogen evolution by bioinformatic methods predicting which parts of a pathogen trigger the dominant immune responses in any particular host (using peptide MHC prediction tools). One of our model pathogens is HIV-1, which is modifying the normal population dynamics in the immune system in such a way that the system slowly collapses, and for which an enormous amount of bioinformatic data is available, that we use to study the evolution of this pathogen in its new human host.