The development of high-throughput techniques for genomics and post-genomics has considerably changed the way that many biologists do their research. Knowledge of complete genomes and, more recently, metabolic, regulatory, and interaction networks has made it possible to consider a living cell not as a loose collection of individual components but as a system. These global approaches in biology contribute to deeper understanding of living systems, but produce an accompanying volume of information that only computer science methods can master. Global answers to biological questions are more and more dependent of pluridisciplinary approaches that link biology and bioinformatics. The ultimate goals of computational biology are to extract knowledge from large scale data sets; to build complete representations of cells, organisms, and populations; and to predict computationally complex systems from bodies of less complex data . The inference of the behavior of a living organism at a systems level, based on the knowledge of other living organisms, will be very valuable in medicine and biotechnology. Indeed, a large number of living organisms are out of reach for thorough experimental investigation, either for technical or financial reasons. As the acquisition of genomic sequences is becoming easier and more cost effective, computational biology must fill the gap between the genome and the understanding of a living organism as a system.
One of the key challenges in the study of biological systems is understanding how the static information recorded in the genome is interpreted to become dynamic systems of cooperating and competing biomolecules. Magnome addresses this challenge through the development of computer science techniques for multi-scale modeling and large-scale comparative genomics:
- stochastic hierarchical models for behavior of complex systems, formal methods
- logical and object models for knowledge representation
- algorithms for sequence analysis, and
- data mining and classification.
We use genome-scale comparisons of eukaryotic organisms to build modular and hierarchical hybrid models of cell behavior that are studied using multi-scale stochastic simulation and formal methods. Our research program builds on our experience in comparative genomics, modeling of protein interaction networks, and formal methods for multi-scale modeling of complex systems.
Biological systems are complex systems in the sense that their behavior cannot be completely described by the behavior of their individual components. Interaction between simple components leads to complex system behavior.
Magnome uses genome-scale comparisons of eukaryotic organisms to build modular and hierarchical hybrid models of cell behavior that are studied using multi-scale stochastic simulation and formal methods. Rather than study individual components of these genomes or individual biochemical reactions, we build views of these organisms as systems of cooperating and competing biological processes.
Our research program develops novel applications in comparative genomics of eukaryotic microorganisms, predictive construction of biological networks such as protein-interaction networks and biochemical pathways, and practical modeling and simulation of biological systems using the BioRica framework. This activity has produced a wide variety of software tools designed for the biological user, developed in through international collaboration with partner laboratories in France and in Europe.
- IFP-Energies Nouvelles
- AgroParisTech, Grignon
- CEA, Saclay
- European Bioinformatics Institute, Hinxton
- Génoscope – Centre National de Séquençage, Evry
- Institut Pasteur, Paris
- Institut des Sciences de la Vigne et du Vin, Bordeaux
- Universities : Cambridge, Chalmers (Göteborg), Claude Bernard (Lyon), Marseille, Humboldt (Berlin), Strasbourg