636-0017-00L  Molecular Evolution, Phylogenetics and Phylodynamics

SemesterAutumn Semester 2015
LecturersT. Stadler
Periodicityyearly recurring course
Language of instructionEnglish


AbstractThe aim of the course is to provide up-to-date knowledge on how we can obtain an understanding of the evolution and population dynamics of organisms based on their genetic sequencing data, employing key concepts from molecular evolution, phylogenetics and phylodynamics. Throughout the course, we tie the models and methods closely with applications, mainly in the field of epidemiology and evolution
ObjectiveAttendees will learn what information is contained in genetic sequencing data and how this information is extracted from the sequencing data. The main concepts introduced are:
* models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
* stochastic processes
Attendees will apply these concepts to a number of applications yielding biological insight into:
* epidemiology
* pathogen evolution
* macroevolution of species
ContentThe course consists of three parts. We first introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Second, we employ these evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. We finally introduce the field of phylodynamics. The aim of that field is to understand and quantify the population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades.
Lecture notesSlides of the lecture will be available online.
LiteratureThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
Prerequisites / NoticeBasic knowledge in linear algebra, analysis, and statistics.