RESEARCH SUMMARY

Our group is interested in different aspects of Bioinformatics, Computational Biology and Systems Biology. Our goal is to obtain new biological knowledge with an in-silico approach which complements the in-vivo and in-vitro methodologies of Biology. This mainly involves mining the massive amounts of information stored in biological databases.

Besides our lines of scientific research, we also collaborate with experimental groups providing them with bioinformatics support for their specific needs, and participate in different teaching projects.

We have developed evolutionary-based method for predicting sites with some functional importance in protein sequences and structures. Experimental determination of functional/active sites cannot cope with the massive stream of new sequences coming from genome sequencing projects. Hence, computational methods are highly demanded for this task. The methods we develop in this area are based on the fact that functional sites are subject to certain evolutionary constraints whose landmarks can be detected on multiple sequence alignments.

The biological functions of many proteins can only be explained in the context of their relationshipts with others. Experimental techniques for the determination of interaction partners are still far from perfect and computational methods for predicting pairs of proteins which interact or are functionally associated have emerged. We have developed evolutionary-based methods for predicting interaction partners which have been accepted and followed by the community. These methods are mainly based on the hypothesis that interacting or functionally related proteins adapt to each other during the evolutionary process (co-evolution). We try to detect the landmarks that this co-evolutionary process left in the sequences and structures of the proteins.

The study of living systems from a network perspective is providing new biological knowledge which could have never been obtained from the study of the individual components (genes, proteins, ...) no matter how detailed it was. As a prototype of complex systems, in biological systems many times "the whole is more than the sum of the parts".

The biological knowledge obtained with this "top-down" approach is still modest compared with the wheal of information the "bottom-up" approach (exemplified by the Molecular Biology) has produced. Nevertheless, it is clear that this new approach is required to complement the intrinsic limitations of the reductionist approach due to the complexity of living systems. We are studying metabolic networks (central metabolism and biodegradation) and protein interaction networks from this new "top-down" approach. Of special interest for us is the study of the complex phenomenon of "protein function" from a systemic perspective, trying to understand how complex functions arise by combining the molecular functions of proteins when these interact in intricate networks.