Ioannis Xenarios



Dr. Ioannis Xenarios has a PhD in immunology from the Ludwig Institute of Cancer Research and the Institute of Biochemistry of the University of Lausanne, and is since 2010 full professor at the University of Lausanne (UNIL). Dr. Xenarios’ expertise has been built around viruses and host interactions. During his postdoctoral fellowship he created under the guidance of Professor David Eisenberg at the University of California Los Angeles (UCLA) the first protein-protein interactions database (DIP), one of the three major interaction databases in the world. Dr. Xenarios led several groups in the Merck-Serono Pharmaceutical Research Institute, centered on computational methodologies development in the fields of proteomics, genomics and genetics. His research activities are at the interface of gene regulatory network modeling and computational biology. He is one of the 15 members of the Scientific Committee for the Innovative Medicine Initiative, and involved  in the Proteomics Standard Initiative (PSI-MI). Dr. Xenarios leads the Vital-IT group at the SIB Swiss Institute of Bioinformatics in Lausanne, as well as the Swiss-Prot group at SIB in Geneva.

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Biocurations for model predictions: example of logical-based models

There is an inherent tension between generating large amount of data and being able to analyse, interpret and predict from those data. The challenge is to find a common ground between high throughput data generation and small scale experiments. With the groups that I am coordinating this challenge does exists very much on one side the Swiss-Prot competence center is a largest academically funded biocurators group and on the other side the Vital-IT competence center is leaning toward high performance and high throughput analytics. Bridging the gap between these two is essential to enable predictive biology, beyond the mere data collections (stamp collection) or the mapping exercice on pathways. An attractive approach is the use of logical-based models, as these type of models try to capture the flow of information within a regulatory network. I will describe how these models are used in different research projects ranging from fundamental yeast to cancer biology and elaborate on their ease of use and also their limitations. Finally the interesting part of these models is that they are easily understandable by experimental biologists and could be used as a training mechanism towards systems biology modeling.

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