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Probabilistic Modeling in Molecular Evolution
Last Updated: 2026-02-05 15:29:34
Abstract
The course is designed to provide an up-to-date overview of the fundamental statistical and computational techniques in the field of molecular evolution, and to outline the directions for new methodological developments. Although the focus is on probabilistic methods such as maximum likelihood and Bayesian inference, a range of alternatives is also discussed.
Content
The course is designed to provide an up-to-date overview of the fundamental statistical and computational techniques in the field of molecular evolution, and to outline the directions for new methodological developments. Although the focus is on probabilistic methods such as maximum likelihood and Bayesian inference, a range of alternatives is also discussed. For each topic the methods' mathematical and statistical aspects are rigorously presented, followed by examples of the best known applications to real molecular data. The practicals are intended to deepen the understanding of the theory presented during lectures through analytical and empirical exercises ranging from formulae derivations to hands-on experience of downloading, filtering and analyzing the real molecular data. The topics to be covered: Markovian models of character substitution (nucleotide, amino acid and codon models). Maximum likelihood inference: estimation of evolutionary rates, phylogenetic reconstruction, ancestral states reconstruction, modeling non-heterogeneous evolution. Phylogenetic inference with maximum likelihood. Model selection using likelihood ratio tests, AIC and BIC. Bayesian phylogenetic inference using Monte Carlo integration, MCMC algorithms. Molecular clock and estimation of divergence times: likelihood ratio tests, likelihood estimation, Bayesian approach. Neutral theory of evolution and the tests for neutrality. Detecting adaptive evolution; detecting co-evolving positions. Approximations to likelihood. Simulating evolution. Modeling evolution of sequences with indels.
Resources
Literature
(1) Li, H-W. 1997. Molecular Evolution (2) Yang, Z. 2006. Computational Molecular Evolution (3) Nielsen, R. 2005. Statistical Methods in Molecular Evolution (this one is available online with the ETHZ library)
General Information
- Language
- English
- Levels
- BSC , DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 15 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Probabilistic Modeling in Molecular Evolution |
|
2 h weekly |
| exercise | Probabilistic Modeling in Molecular Evolution |
|
1 h weekly |