During the course, the emphasis will be on composing and analysing exact computational models based on specific biological hypotheses. These models are used to gain insight into the dynamics of biological systems across various fields. Topics include multi-level evolution, such as pre-biotic evolution, eco-evolutionary dynamics, spatial pattern formation, and genome evolution—for instance, the interaction between gene regulation and evolutionary processes. Developmental dynamics are also addressed, focusing on pattern formation, morphogenesis, mechanical interactions between cells, and the evolutionary basis of morphogenetic processes. The course further explores immune system dynamics, including self/non-self discrimination and host-pathogen co-evolution, as well as behavioural dynamics, such as self-structuring through local interactions and the interface between learning and evolution.
To analyse these systems, students will use a range of modelling formalisms. These include (non-linear) differential and difference equations (ODE and PDE), cellular automata, individually oriented (particle-based) models, and evolutionary computation. Through these tools, students will learn how computational models of dynamical systems can be formulated, analysed, and interpreted to investigate biological processes. The course also prepares students to critically read and interpret current scientific literature that uses modelling, helping them to extract key findings, assess underlying assumptions, and connect insights to the theoretical frameworks discussed during the course.