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

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 objectives of BCM. Our research team develops new methods and algorithms allowing us to validate or to choose computational 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 :

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.

Bayesien clustering using tessellations and Markov models in spatial population genetics (TESS Software)

 Systems biology
Dynamics of genetic networks.
Formal methods for network modeling.
Interactome bioinformatics.
Smart pooling.

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

 Biostatistics