The interest of statistical physics for combinatorial optimization is not new, it suffices to think of a famous tool as
simulated annealing. Recently, it has also resorted to statistical inference to address some "hard" optimization problems, developing a new class of message passing algorithms. Three applications to computational biology are presented in this thesis, namely:
1) Boolean networks, a model for gene regulatory networks;
2) haplotype inference, to study the genetic information present in a population;
3) clustering, a general machine learning tool.
