Research Summary:

I am a computational biologist. I use bioinformatical and statistical methods to analyze biological data sets, in particular whole-genome and high-throughput data sets; I also develop mathematical models and computer simulations of biological systems. While my lab does not perform any experiments, I have extensive collaborations with experimental groups and I frequently co-advise students whose research has an experimental component.

My current research covers three broad but interconnected areas: 1. biophysical mechanisms of molecular evolution; 2. microbial adaptation and experimental evolution; 3. disease dynamics. A recurring theme in my research is evolution; modern biomedical research is deeply connected to evolutionary biology.

1. One of my main research goals is to develop mechanistic, biophysical explanations for patterns of molecular evolution observable in extant genomes. Many of the patterns that we detect reflect fundamental biophysical mechanisms operating in all cellular life forms. My research in this area has led to the hypothesis that selection against protein misfolding is a major factor shaping coding-sequence evolution; we continue to test and elaborate on this hypothesis. My group has also found a universal trend of selection for efficient translation initiation in a broad survey of over 300 species, including bacteria, archaea, and eukaryotes.

2. I develop mathematical or simulation models that predict aspects of microbial adaptation, such as the expected increase in fitness over time. These models provide valuable insight to the growing number of experimentalists who carry out laboratory evolution experiments with microbes.

3. My research interests of molecular evolution and microbial adaptation have important applications for infectious diseases. My group is studying both specific disease systems, such as HIV, and broader questions, such as how viral sequence data relate to epidemiological quantities such as disease prevalence or incidence.