Research
Research in the Computational Biology Group aims to advance the rational design of medical interventions in complex and evolving biological systems, such as cancerous tumors and virus populations. We employ an array of methods and approaches: developing models and algorithms for the statistical analysis of high-throughput molecular data; reconstructing and analyzing biological networks; predicting the effect of perturbations; and designing evolutionary models of rapidly adapting cells and pathogens. Our methodologies are applied in several AI-driven personalized medicine initiatives, with a particular emphasis on oncology and virology.
Computational Oncology
The goal of our cancer-related research is to support the diagnosis and treatment of cancer. Our research topics include the analysis, integration, and interpretation of molecular profiles of cancer cell populations, and across patient cohorts. For example, we have developed methods for mutation calling, for reconstructing the evolutionary history of a tumor, for discriminating driver from passenger mutations, and for identifying mutually exclusive cancer mutations which are likely to be critical for tumor growth and hence candidates for medical interventions. We are part of the Tumor Profiler Center, where we develop methods for single-cell genomics and transcriptomics data from cancerous tumor cells and for data integration across various single-cell technologies. These methods can improve patient outcomes by enabling early detection and diagnosis and by facilitating the development of personalized treatment plans tailored to individual genetic and clinical profiles.
Computational Virology
Our research in computational virology aims at identifying optimal personalized antiviral drug combinations tailored to the genetic composition of each patient´s virus population. We predict the probability that the virus will develop genetic escape mutations, which could lead to drug resistance and eventually result in treatment failure. In order to estimate the genetic makeup of a virus population within a host, we have developed computational approaches for the assembly of sequencing reads into viral haplotype sequences. The other major focus of our computational virology research involves analyzing wastewater samples to detect the presence of pathogens, such as SARS-CoV-2, RSV, and Influenza, to obtain population-level epidemiological information. Our computational virology tools are integrated into V-pipe, our bioinformatics pipeline designed for the reproducible analysis of mixed virus samples.
