Leaders: Thomas Rolland & Daniele Raimondi
The Advanced Computational Biology axis gathers individuals with interdisciplinary knowledge from diverse labs, who are investigating biological questions using computational models and tools. In the big data era, computational biology has emerged as a necessary mean to address the complex relationships between the (epi)genome, the transcriptome, the proteome and the phenotypes, at the level of the cell, tissue or entire organism.
Our group hence includes researchers, engineers, postdocs and students in Machine Learning and Computational Biology who investigate the fundamental mechanisms underlying healthy and pathological conditions in model organisms (viruses, yeast, mouse, plants) as well as in human individuals. We are developing innovative, tailor-made methods to investigate a wide range of biological questions including transcriptional regulation, structural modelling, chromatin remodelling and genome sequencing-based prediction of quantitative or disease traits.
Teams involved: Jean-Christophe Andrau, Anne Debant, Raphael Gaudin, Dom Helmlinger, Andrey Kajava, Domenico Libri & Odil Porrua, Delphine Muriaux & Cyril Favard, Daniele Raimondi, Eric Soler, Albert Tsai.