BIGS Research team
Biology, genetics and statistics
The BIGS (Biology, Genetics and Statistics) team is mainly focused on stochastic modeling and statistics for a methodological
purpose but also aiming at a better understanding of biological systems and health phenomena. Its attention is directed on (1) stochastic modeling, (2) estimation and control for stochastic processes, (3) algorithms and estimation for graph data and (4) regression and machine learning. The main objective ofBIGS is to exploit these skills in applied mathematics to provide a better understanding of some issues arising in life sciences, with a special focus on (1) tumor growth, (2) photodynamic therapy, (3) genomic data and micro-organisms population study, (4) epidemiology and e-health and (5) dynamics of telomeres.
Online data analysis, local regression, estimation of Piecewise deterministic Markov Processes, Inference for tree data, inference of netwok, estimation for complex biological systems, Markovian models for tumor growth, SDEs for bacteriophage systems, MCMC methods for light transport in tissues.
International and industrial relations
Collaboration with Purdue University, CHU Nancy, Cybernano (Nancy) and Transgene (Strasbourg).
Research teams of the same theme :
- ABS - Algorithms, Biology, Structure
- BEAGLE - Artificial Evolution and Computational Biology
- 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 - from patterns to models in computational biodiversity and biotechnology
- SERPICO - Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes