Course description

Biological systems, and specifically ecological systems, contain multiple entities that interconnect to form networks. These include gene interaction networks, food webs, plant-pollinator networks, metapopulations and networks of disease transmission. How these networks are structured is crucial for the dynamics and functioning of biological systems. This course will introduce the theory and methods for working with biological networks. Though primarily motivated by ecology, the theory and methodology we will cover is also relevant for other fields in biology, such as neuroscience, systems biology and epidemiology. Students will be actively involved in classes via paper discussions. Therefore, there is a limit of 12 students. The course, class discussions and presentations will be given in English. For the final project, students are encouraged to use their own research data.

Course goals

Provide an understanding of the (ecological) theory underlying the structure and dynamics of complex systems.
Provide tools to perform network analysis on a variety of network types.
Skills: programming and analysis of network data, advanced paper reading, presenting research, scientific writing.

Requirements and grading:

Required attendance to lectures
Required attendance to exercises
Grade components: Project description (5%), final report (in English) 45%, presentation of project 20%, paper discussion 20%, active participation 10%.

Academic and general requirements:

The exercises are computational and students are required to bring a laptop (talk to me if you do not have one!)
Exercises are not for submission but are recommended for hands-on experience. Exercises are written in R. Students who would like to program in another language are welcomed to do so independently.
Recommended (not necessary) prerequisite courses: Introduction to Ecology, linear algebra.
The course and all the assignments are in English .

Course Topics (may change)

Introduction to networks and complex systems
Network structure
Community detection
Nestedness
Robustness
Multilayer networks
Network dynamics

Note: Course material and weekly meeting topics may vary from what is currently written in the syllabus at the instructor’s discretion during the semester.