ECTS
6 credits
Component
Faculty of Science
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Molecular and Cellular Bacteriology
Bioproduction and valorization of microbial biodiversity
ECTS
3 credits
Component
Faculty of Science
A teaching module focused on the professional world, with general introductions to pre-defined themes targeting the biotechnological valorization of microorganisms (antimicrobials, microbiota, probiotics, applied virology, etc.), followed by presentations by industrialists on their background, their company and/or the development of a project. This course covers both red biotechnologies (health applications), and the other colors of biotechnologies (green/agronomy, blue/marine, white/industrial, yellow/environmental).
Interactions and signalling
ECTS
3 credits
Component
Faculty of Science
In-depth phylogeny: methods and applications in evolution
Component
Faculty of Science
Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.
Probabilistic approaches are presented and explored in greater depth. The attraction artifact of long branches leads to an introduction to probabilistic reasoning. The maximum likelihood method is used to calculate likelihood, to estimate model parameters by optimality, to construct different character evolution models, and to compare models. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the data-driven estimation of a posteriori distributions of model parameters, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of tree and clade posterior probabilities. The importance of DNA, RNA and protein sequence evolution models and their improvement is emphasized.