BEAGLE Research team
Artificial Evolution and Computational Biology
- Leader : Guillaume Beslon
- Type : Project team
- Research center(s) : Grenoble
- Field : Digital Health, Biology and Earth
- Theme : Computational Biology
- Partner(s) : Institut national des sciences appliquées de Lyon,Université Claude Bernard (Lyon 1),CNRS
- Collaborator(s) : Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) (UMR5205)
The expanded name for the Beagle research group is "Artificial Evolution and Computational Biology". Our aim is to position our research at the interface between biology and computer science and to contribute new results in biology by modeling biological systems. In other words we are making artifacts - from the Latin artis factum (an entity made by human art rather than by Nature) - and we explore them in order to understand Nature. Our research is based on an interdisciplinary scientic strategy: We are developing computer science formalisms and software for complex system modeling in synergy with multidisciplinary cooperations in the area of living sciences. Thanks to computational approaches we study abstractions of biological systems and processes in order to unravel the organizational principles of cellular systems.
The scientic activity of the Beagle group focus on two different topics:
- Computational Cell Biology We are developing models of the spatio-temporal dynamic of cells and their molecular components. More precisely, we study the complex interplay between the reaction and the diffusion processes when the medium is not homogeneous or when the number of molecules is too low to account for a perfect mixing hypothesis. We particularly focus on the consequences on the signaling networks and on the stochasticity of transcription. In this domain, we always try to mix up modeling and \wet" experimental approaches by developing close collaborations with experimental biologists.
- In silico Models of Evolution To better understand the cellular structures (genome organization, transcription networks or signaling cascades) we propose to study their historical evolutionary origin. Individual-based evolutionary models (\in silico experimental evolution) allow to study how evolution in various conditions (e.g., large vs. small efficient population sizes, high vs. low mutation rates, stable vs. unstable environments...) leads to some specific structures shaped by the needs of robustness, variability or evolvability. The comparison with real data requires the reconstruction of the evolutionary events that have shaped the extant real genomes. To this aim, integrative models, including small substitutions as well as large reorganizations of a genome, are needed. The confrontation of what we can know of historical events and the laws we can infer from artical experiments will allow to explain some patterns of today's organisms and biodiversity.
Both topics are strongly complementary. Indeed, on the short time scales biological systems are constrained by the physical nature of their substrate but, on long time scales, they are also constrained by their evolutionary history. Thus, studying both time scales and both constraints - including their interactions - gives us a global viewpoint on the roots of biological organization.
Research teams of the same theme :
- ABS - Algorithms, Biology, Structure
- BIGS - Biology, genetics and statistics
- CAPSID - Computational Algorithms for Protein Structures and Interactions
- DYLISS - Dynamics, Logics and Inference for biological Systems and Sequences
- ERABLE - European Research team in Algorithms and Biology, formaL and Experimental
- GENSCALE - Scalable, Optimized and Parallel Algorithms for Genomics
- IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
- LIFEWARE - Computational systems biology and optimization
- MORPHEME - Morphologie et Images
- MOSAIC - MOrphogenesis Simulation and Analysis In siliCo
- PLEIADE - Patterns of diversity and networks of function
- SERPICO - Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes
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