Recent advances in sequencing, mass spectrometry, micro-array technology, and other high-throughput methods have transformed biological research. With modern methods, biological experiments increasingly produce such volumes of data that advanced computational and statistical methods are required to analyze them. Research in the Wilke lab, in the broadest sense, aims to make sense of this wealth of data. We do computational biology. We use bioinformatical and statistical methods to analyze biological data sets, in particular whole-genome and high-throughput data sets; we also develop mathematical models and computer simulations of biological systems. While the lab is purely computational, we frequently collaborate with experimental groups. To see what we are currently working on, check out our recent publications. You can also read about some of our major results.
Our 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 our research is evolution; modern biomedical research is deeply connected to evolutionary biology. For example, evolutionary methods are used to track and study infectious diseases such as influenza or HIV/AIDS. Many vaccines are developed through experimental evolution. Cancer progression is governed by evolutionary dynamics. Patterns of genome evolution can reveal costs and constraints under which cells operate.
Biophysical mechanisms of molecular evolution
One of our 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. Our 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. The 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.
Microbial adaptation and experimental evolution
We are developing 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. The most exciting current development in experimental evolution is the trend to full-length sequencing and functional-genomics characterization of evolved isolates. These high-throughput data sets will allow testing of evolutionary models at an unprecendented level of accuracy and detail.
Our research interests in molecular evolution and microbial adaptation have important applications for infectious diseases. We have done extensive research on HIV/AIDS. HIV forms long-lived reservoirs in patients' bodies, and we have carried out several studies aimed at understanding the source and maintenance of these reservoirs. Currently, we are working on mathematical models linking antiviral drug efficiency to the stage in the viral life cycle at which the drug acts. We are also studying broader questions, not specifically linked to a particular disease, such as how viral sequence data relate to dynamical aspects of an epidemic such as disease prevalence or incidence.