Welcome to the website of Project-Team BEAGLE.
BEAGLE is a joint research group between:
- INRIA: the French National Institute for Research in Computer Science and Control
- INSA Lyon: Lyon Polytechnical University for Science and Technology
- University Claude Bernard Lyon 1: the largest university in Rhone-Alpes’ region
We are located in Lyon, France (see our contact page for directions).
In collaboration with team Morpheme (Inria Sophia-Antipolis) and the Fleischmann lab (Brown University), we are looking for postdoctoral researchers that are passionate about image analysis in neurodevelopment. Roughly speaking, our goal is to develop a scalable pipeline for the quantification of neuronal nuclei, stained for specific proteins, in large volumes of spatiotemporal imaging data. If you are interested, please check out the MSCA Postdoctoral Fellowships website (https://euraxess.ec.europa.eu/jobs/hosting/inria-msca-pf-2022-hosting-offer-grenoble-lyon-5) and contact us (email: email@example.com or firstname.lastname@example.org).
The expanded name for Beagle is “Artificial Evolution and Computational Biology”. Our research is at the interface between biology and computer science and aims at contributing 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. Using computational approaches, we study abstractions of cellular systems and processes in order to unravel their organizational principles.
Project-team Beagle is funded by the following laboratories / institutes / agencies
Two main topics:
- Computational Cell Biology: We develop models of the spatio-temporal dynamic of cells and their molecular components, in particular the complex interplay between the reaction and the diffusion processes when the medium is not homogeneous or 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 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. In silico experimental evolution allows the study of 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.
Both topics are strongly complementary: 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. Therefore, studying both time scales and both constraints – including their interactions – gives us a global viewpoint on the roots of biological organization.