Modelling Course in Population and Evolutionary Biology
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This course provides a "hands-on" introduction into mathematical/computational modelling of biological processes with particular emphasis on problems drawn from evolutionary and population biology. Working in small teams, the participants develop two modelling exercises with different levels of difficulty from a list of projects. They complete the whole life cycle of a modelling (mini-) project: from the identification of a biological problem, through its "translation" to a mathematical/computational model, to the evaluation, interpretation and presentation of the results. The students learn about some interesting problems of evolutionary or population biology, acquire practical skills in the modelling of biological problems, and experience team work. The models are developed in the open-source software R by modifying and adding to existing example programs. Previous experience with R is helpful, but not required for this course.
Practical information
In 2025 the course is going to take place between 2-13 June. The typical hours are between 9am-6pm, but this can be shifted half an hour earlier, based on a vote during the first class. The first class starts 2 June, 9am. The place is LFW C4.
You are encouraged to bring your own laptop to the course. However, ETH laptops can also be provided, if needed (please, give us advance notice).
The limited places of the course tend to fill up. However, some of the registered students may cancel their registration before the course starts, which would free up their slots. Therefore, if you did not make it in the first round early February, you are recommended to check back in March or later, if you are still interested in taking the course.
There is a possibility for people from outside the ETH to register for the course. If you study at the UZH, you can register for free as a 'special student': click here for more information. If you study at another Swiss university, please, check it out on this page, whether you are entitled to register as a special student. All others can register as 'external auditors' for a fee; for details, click here. (If you are interested, please, check the deadlines for registration early on).
The course starts with a general introduction to modelling, an introduction to the available modules (modelling exercises), and (as needed) a short tutorial to R. Teams of 3-4 students are formed and choose a first and a second level module. The majority of the time involves supervised group work: the student teams work out the solutions to their chosen modules, while instructors are present in the classroom to help when needed and to monitor progress. A few days are devoted to the completion of a Level 1 module, and about a week to the completion of a Level 2 module. The course concludes with the last half day devoted to presentations, in which each team present their results on their Level 2 module.
Evaluation
The final mark is composed as the average of four marks awarded for the following tasks: 1) The implementation of the first module, 2) The technical implementation of the second module, 3) Biological analysis and interpretation of the second module and 4) the final presentation. The instructors are constantly monitoring progress during the course, and judge understanding of concepts, creativity, the functionality of implemented code and, ultimately, the scientific answers to the questions in the modules. To enable the evaluation of individuals, each team member should be responsible for well-defined tasks within the student projects, and also take part in the presentation. Importantly, criteria are defined such that students with no prior knowledge of R should also be able to achieve the highest mark.
Level 1 modules
Level 2 modules
- chevron_right Discrete vs. continuous time models of malaria infections
- chevron_right Evolution of the sex ratio
- chevron_right Network models of epidemics
- chevron_right Rock-paper-scissors dynamics in space
- chevron_right Spatial cooperation games
- chevron_right Stability and complexity of model ecosystems: Are large ecosystems more stable than small ones?
- chevron_right Stochastic simulation of epidemics
- chevron_right Unstable oscillations and spatial structure: The Nicholson-Bailey model of host-parasitoid dynamics
Additional background information (biology and modelling techniques) can be found for several modules in the Download reader of the earlier course "Ecology and Evolution: Populations" (PDF, 2.5 MB) (701-1415-00L) by Prof. Sebastian Bonhoeffer.
Further literature reference and background information can be found on the individual webpages of the modules.
Powerpoint file of the Download introductory presentation (PPTX, 4.4 MB).
R sample files:
Download sample.r (R, 9 KB) and accompanying Download Tutorial (PDF, 567 KB).
Download distance.r (R, 130 Bytes)
Download reaction.r (R, 3 KB)