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Home > Research > Genome Biology > Olivier LESPINET : Molecular Bio-informatic

Olivier LESPINET : Group Presentation

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Our group is interested in developing and validating original methods of analysis, modeling and prediction of the information contained in sequences, structures and biological networks. Our main research interests are comparative genomics and systems biology on one hand, protein and RNA structural biology on the other hand.

Presentation

Our projects are mainly focused on biological networks, at different scales, from molecular scales (molecular structures and their interactions) to “systemic” scales (protein interaction networks, gene regulatory networks, metabolic networks). The study of these biological networks, including the study of their dynamics and their evolution, is the general problem we address. We work on two different but related kinds of problems: dynamics and evolution of biological networks (metabolism, molecular interactions, regulation) on one hand and prediction and evolution of molecular structures and their interactions on the other hand.

More precisely, we work on the following topics:

1. Fungal comparative genomics and evolution of enzymatic activities. Fungi are characterized by the possession of a vast and very diverse enzyme directory depending on the species. To study and understand this diversity we have developed an ambitious program of data mining on about 200 species of fungi. In this context our work was focused on designing methods to improve the quality of data (Grossetête et al., 2010, Pereira et al., 2014) as well as on the adaptation and the development of data mining methods adapted to evolutionary comparative genomics and metabolic networks.

2. RNA structural bioinformatics. The function of an RNA molecule is very closely related to its spatial structure. We investigate several related methodological problems for studying RNA structures : structure prediction, structure and seuence-structure comparison, design of structures that is finding an RNA sequence that folds into a given secondary structure, and interactive drawing of structures (Darty et al., 2009). We notably focus on the problem of predicting the three dimensional structure of an RNA molecule, which is also called RNA structure modeling. For this purpose, we investigate new approaches based on graph algorithms and game theory (Lamiable et al., 2013).

3. Dynamics and evolution of metabolic networks. We plan to look at the evolution of the topology of metabolic graphs, that is of their properties in terms of relations between their components. We want to compare the conservation of these relations and components between related species. This will lead to a better understanding of how such networks have been able to build over time and how these changes could be responsible for characteristic life traits. At the other end of the time scale, we study the dynamics of metabolic networks during microrganism cultivations, taking into account its regulation at the gene expression level. Our aim is to build coupled models of metabolic and regulatory networks.

4. Biogenesis of protein super-complexes. The aim of our work is to develop computational approaches to model the dynamics of biogenesis of protein super-complexes. Presently, this subject is addressed mainly by genetic, biochemical and physico-chemical approaches that find their limits. Recent works have demonstrated that binary complexes can be predicted by integrating docking algorithms with evolutionary information based methods (Lopes et al. 2013) and that protein complexes components can be identified by applying clustering algorithms to protein-protein interaction networks (Glatigny et al. 2011). Our aim is to go further by developping a multiscale approach which integrates these different degree of information (systemic and molecular), notably being able to predict (1) the assembly factors of the complex which are necessary to the construction of the model; (2) the intermediate complexes involved in the cascade of events leading to the final complex.

5. Protein - Nano-surface interaction analysis. Nanoparticles are commonly used in various industrial products, leading to an increase exposure of human beings and our environment. First, this project aims to decipher at the molecular level the toxicity of these new materials. That is to characterize the structural and physical-chemical properties of the proteins that are adsorbed given some nano-surface properties. This task can lead to suggest new models of interactions (Mathé et al., 2013). Our second aim is, using the expertise acquired in the previous step, to develop dedicated classification methods to assess the probability of adsorption of a given protein with a nano-surface.

Keywords

systems biology, structural bioinformatics, biological networks, metabolic networks, RNA structure, protein assembly, graph algorithms, dynamical modelling. Nanoparticules, comparative genomics

Contact


LESPINET Olivier [Professor - UPSud]
Department of genomes biology [Deputy]
Molecular BioInformatic [Leader]
01 69 15 35 59 Orsay - Bât 400


DENISE Alain [Professor - UPSud]
Molecular BioInformatic
01 69 15 46 54 Orsay - Bât 400

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