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 interests?
We develop and apply methods for the integrative analysis of large-scale biological and clinical data. Our goals are to improve 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 Learn more. We are also interested in relatively small biological systems that can be modeled quantitatively. Here our goal is to better understand the properties of these systems that contribute their functionality such as robustness and evolvability under changing environmental conditions Learn more.
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 Micha if you have any questions or need an account.
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Assessing 75 contributed module identification methods revealed novel top-performing algorithms, and established resource of modules correspond to core disease-relevant pathways for studying human disease biology (more info is in the challenge webpage and the paper).
As a member of the PhenoMeNal (Phenome and Metabolome aNalysis) consortium we contributed to set up a system of Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardised, automated, and published analysis workflows. The paper is published in GigaScience .
G-protein coupled receptors (GPCR) constitute one of the most widespread class of receptors used by cell to get information on their external environment. However it was still debated whether the GPCR signaling system can measure more that the presence or absence of a compound binding to the receptor. Together with the team of Vladimir Katanaev of the Department of Pharmacology and Toxicology at UNIL, we quantified, using tools from information theory, the amount of information that the GPCR system can provide to a cell and showed that it can distinguish between multiple concentration levels. The paper has just been published in Nature Communications.
We invented ''Metabomatching'' as a method to identify candidate compounds from genetic associations to NMR features. In our recently published paper in Plos Computational Biology we extensively tested our approach on a large collection of real GWAS association results and saw better discovery of confirmed pathways than with other popular methods.