Statistical Genomics and Biological Physics
Computational Biology
Advances in sequencing and other high-throughput experimental technologies have fueled the genomic revolution over the last 10-15 years, providing an unprecedented amount of large-scale biological data. To extract information from such data, and to reach a deeper understanding of the corresponding biological system, requires the solution of hard inference problems, which are intractable by conventional computational tools.
Our group, newly established in Sept. 2011, draws inspiration from the statistical physics of disordered systems to develop novel algorithmic tools for solving large-scale optimization and inference tasks, to bring such computational methods to the full benefit of biological research.
Specific questions of our interest are:
- Statistical-physics inspired approaches to large-scale network inference
- Protein co-evolution and structural inference of proteins, their complexes, and their interaction specificity
- Inference of signal transduction networks from multiple-drug perturbation experiments
- Robust clustering of noisy data
Group Domain: Computational
Members
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Christoph Feinauer
joined 2 years ago -
Guido Uguzzoni
joined 2 years ago -
Matteo Figliuzzi
joined 2 years ago -
Edwin Rodriguez
joined 2 years ago -
Kai Shimagaki
joined 2 years ago -
Giancarlo Croce
joined 2 years ago -
Nika Abdollahi
joined 2 years ago -
Pierre Barrat-Charlaix
joined 2 years ago -
Eleonora Deleonardis
joined 2 years ago -
Alice Coucke
joined 2 years ago -
Frédéric Chaux
joined 2 years ago -
Barthelemy Meynard-Piganeau
joined 2 years, 4 months ago -
Alya Zeinaty
joined 2 years, 4 months ago -
Lorenzo Rosset
joined 2 years, 4 months ago -
Roberto Netti
joined 2 years, 4 months ago -
Francesco calvanese
joined 2 years, 4 months ago -
Jeanne Trinquier
joined 2 years, 4 months ago -
Martin Weigt
joined 2 years, 4 months ago