Welcome to the Computational Biology Group!

Who are we?

The Computational Biology Group (CBG) is a research group embedded in the Department of Computational Biology at the University of Lausanne. The group consists of PhD students and postdocs and is led by Prof. Sven Bergmann.

What are our goals?

Our goals are to improve our fundamental understanding of how genetic variability affects phenotypes, to learn about underlying molecular mechanisms, and to make use of our insights to improve the diagnosis, prevention and treatment of disease whenever possible. We are also interested in relatively small biological systems that can be modelled quantitatively, our goal being to understand better the properties that contribute to their robustness and evolvability under changing environmental conditions.

What are our research interests?

The group works on a broad spectrum of biological topics, from systems biology of development and signal transduction over genomics to statistical genetics. In recent years our primary focus has been on genome-wide association studies (GWAS) that link genetic variants, such as single nucleotide polymorphisms (SNP), to organismal traits. The group develops and applies innovative tools for linking association signals to genes and pathways for single (Pascal) and multiple traits (PascalX). Most analyses use data from the Cohorte Lausannoise (CoLaus), SKIPOGH and the UK Biobank with a particular interest in high-dimensional phenotypes, such as gene expression, metabolomics, and, most recently, features derived from images.

Specifically, we lead the interdisciplinary VascX SNF Sinergia project. Together with three clinical research groups, our goal is to extract vascular parameters from a unique set of cohorts providing several types of retinal and non-retinal vasculature imaging, history of vascular diseases, risk factors and genotypes. Our group is developing state-of-the-art methodologies (including Deep Learning) to quantify how these parameters vary across the subjects of a given cohort and study the similarity between these parameters across the retina, brain and limbs. To this end we are also integrating genetic information to identify genes and biochemical pathways that modulate vessels’ shape and contribute to vascular disease. Finally we develop models that can predict cardiovascular and cerebrovascular disease risk based on the morphology of retinal vessels. This highly interdisciplinary project will advance our knowledge about the genetic and molecular mechanisms for shaping vasculature. Since retinal imaging is fast and inexpensive, our aim to predict individuals’ risks and detect disease onset early presents a real prospect of a tangible impact on the societal burden caused by common vascular diseases.

How do we work?

Most of our work is computational, which means we use computer algorithms to process and analyse data. Our analyses often have a statistical component to evaluate the significance of results. Whenever possible we describe our data using mathematical models. Sometimes these models can be solved analytically, but often we rely on numerical solutions and simulations. Some of our methods have a heuristic component, but we try to evaluate them rigorously and make them as practical as possible. We strongly believe in sharing our analysis tools and Open Science in general.

Our group seeks an interdisciplinary approach, bridging the traditional gaps between physics, mathematics and biology. Our lab collaborates with experimental and medical research groups.

General info on this wiki

This wiki is the main instrument to centralize and archive information on and generated by the CBG. Ask Michael if you have any questions or need an account.