Equipe BCM

Biologie Computationnelle et Mathématique

Welcome to the BCM web page (Computational and Mathematical Biology Team)

Research in BCM is done along three main axes : genomics, systems biology, and mathematical modeling of complex biological systems.
Methodology in computational biology is one of the main objective
of BCM. Our research team develops new methods and algorithms
allowing us to validate or to choose computer or mathematical
models that can realistically account for the complexity of
biological data. Here, computational methods are inference
methods that make use of algorithmic principles or simulation
techniques. Applications to evolutionary genetics, genomics and
epidemiology are important aspects of our activity, that inspire most of the theoretical approaches developed in the team.

A non-limiting list of projects developed at BCM includes :

PNG - 25.5 ko
Approximate Bayesian Computation (ABC) for population genetics data (Csilléry & al.)

- Genomics and evolutionary genetics
Population genetic structure
Inference of natural selection in human genomes.
Bayesian methods and machine learning.
Molecular epidemiology.
Interactome bioinformatics.
Smart pooling of protein-protein interactions.

JPEG - 133.6 ko
Bayesien clustering using tessellations and Markov models in spatial population genetics (TESS Software)

- Systems biology
Dynamics of genetic networks
Formal methods for network modeling
Cancer bioinformatics

- Complex systems
Mathematics of interacting particle systems
Gene and species tree models
Computer algorithms and stochastic simulation

- Biostatistics

Laboratoire TIMC-IMAG, Domaine de la Merci, 38706 La Tronche Cedex

Grenoble INP
Mentions Légales