Single-Course English 5 ECTS

Computational Molecular Evolution

Overall Course Objectives

To provide the student with broad knowledge in the field of molecular evolution (i.e. the evolution of DNA, RNA, and proteins). It is in particular the aim to provide the student with experience and in-depth knowledge of model-based methods for phylogenetic tree reconstruction and hypothesis testing in an evolutionary context. Although the study of molecular evolution does require a certain level of mathematical understanding, this course has been designed to attract a diverse range of students.

Learning Objectives

  • account for natural selection and the neutral theory of molecular evolution.
  • solve simple population genetic problems.
  • account for important properties of phylogenetic trees.
  • construct phylogenetic trees under the parsimony, distance, and maximum likelihood criteria (using the PAUP* program); construct Bayesian phylogenetic trees (using the MrBayes and BEAST programs).
  • use the Fitch algorithm to manually compute the length of a tree given an alignment; use this as the basis for selecting the most parsimonious tree(s).
  • account for substitution models based on Markov chains.
  • manually compute the likelihood for a phylogenetic model given a set of parameter values and an alignment.
  • use model selection criteria (e.g. likelihood ratio testing and AIC) for selecting the best of several alternative phylogenetic models.
  • use the PAML program package to detect positively selected sites in a protein-encoding gene.
  • use R (eg. RStudio) for manipulating, plotting, and analysing phylogenetic trees

Course Content

Brief introduction to evolutionary theory and population genetics.
Mechanisms of molecular evolution. Models of DNA and protein substitution. Reconstruction of phylogenetic trees using distance based methods, parsimony, maximum likelihood, and Bayesian techniques. Advanced models of nucleotide substitution (gamma-distributed mutation rates, molecular clock models, codon models and analysis of selective pressure). Statistical analysis of biological hypotheses (likelihood ratio tests, bootstrapping, AIC, Bayesian statistics).

The student will acquire practical experience in the use of computational methods by analyzing sequences from the scientific literature.

Recommended prerequisites


Teaching Method

Online lectures, computer exercises, handout exercises.
NOTE: The course is run as a “flipped classroom”, i.e., lectures are viewed online before class.


Limited number of seats

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.

See course in the course database.





13 weeks


Health Tech


DTU Lyngby Campus

Course code 22115
Course type Candidate
Semester start Week 5
Semester end Week 19
Days Wed 13-17

7.500,00 DKK