Modelling Biosystems

Full course description

The complexity of biological systems manifests itself in many features. For example, biological systems consist of a very large number of components (e.g. thousands of genes in one cell, billions of neurons in the human brain). Moreover, these components are linked by a multitude of processes, which are often nonlinear. As a consequence, we cannot make reliable predictions about these complex biosystems with intuition alone. Interestingly, mathematics naturally deals with thousands of variables and non-linearities, providing us with an excellent tool to explore complex biosystems.

The aim of this course is to provide an overview over various “mechanistic” or “white box” modeling approaches. The course will introduce the generic modeling process, important modeling concepts and terminology, and focus on equation-based modeling (and in particular ordinary and partial differential equations), agent-based modeling and constraint-based modeling. We will also touch upon the challenges of multiphysics and multiscale modeling and the inspiring avenues of in silico clinical trials and computational modeling for regulatory approval. To illustrate the various modeling approaches we cover different biological scales (intracellular, cellular/tissue and organ/patient) as well as different applications.

The course will not only provide theoretical knowledge, but will apply the various modeling approaches in hands-on practicals, thereby equipping students with Matlab skills needed in the curriculum later on. The course is thus complementary to the parallel MSB1005 Experimental Design and Data Management course which focuses on data management and analysis and introduces R. It is furthermore complementary to the preceding MSB1001 Systems Biology, in that it focuses specifically on mechanistic modeling approaches (in contrast to –omics approaches) and provides both a theoretical comprehension as well as practical training in these mechanistic approaches.