What we do

The Computational Biology Group is involved in research and teaching in the areas of computational biology, biostatistics, and systems biology. We aim at supporting the rational design of medical interventions in complex and rapidly evolving systems. To achieve this goal, we develop models and algorithms for the statistical analysis of high-throughput molecular data, we analyze biological networks and predict the effect of perturbations, and we design evolutionary models of rapidly adapting disease-causing agents. We are engaged in several personalized medicine efforts, particularly in oncology and virology.

The group develops the V-pipe tool: read the interview to know more about V-pipe's latest applications and developments.

Find out more about the Group’s activities

Main publications 2020

  • Lang B M et al.
    Predicting colorectal cancer risk from adenoma detection via a two-type branching process model
    PLoS Comput Biol, 10.1371/journal.pcbi.1007552
  • Borgsmüller N et al.
    BnpC: Bayesian non-parametric clustering of single-cell mutation profiles
    Bioinformatics, 10.1093/bioinformatics/btaa599
  • Morita K et al.
    Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
    Nat Commun, 10.1038/s41467-020-19119-8