Course: Quantitative and Predictive Modelling

Course coordinator

Jaap Molenaar (WUR)

Study load

3 ECTS points


In this Quantitative and Predictive Modelling course the participants learn how to describe the dynamic behaviour of biological systems and to integrate experimental data. Concepts of modelling in terms of differential equations are introduced via a great variety of case studies taken from diverse practices. The course offers a math refresher to help those participants who are not (yet) involved in modelling on a daily basis. The emphasis is on providing an introduction into modelling approaches rather than an in-depth treatment of a few techniques and aims as such at a broad audience. The course is a mixture of theory sessions and computer practicals. During the practicals most of the time Matlab will be used. Participants not acquainted with Matlab will get an introduction. The course has to be completed with assignments in the form of practical exercises as homework afterwards.

Target Audience

This course is primarily targeted at academic researchers such as PhD students and Postdocs in Life Sciences, Bioinformatics, Systems Biology or Biomedical Engineering. Participants from the private sector are also welcome.
Participants are expected to have some experience in modelling with differential equations or to have followed the Introductory courses E-course modelling and E-course calculus, and Discovering Systems Biology Principles or Applications for Systems Biology and Bioinformatics in the Medical Sciences. The course will start with a session to refresh the basic elements of modelling with differential equations.

Learning Objectives

The students will be provided with a theoretical basis, a variety of methods and a computational hands-on experience to handle differential equation modelling, parameter estimation and uncertainty analysis.

In the course the students will learn:

  • How to set-up a dynamic model to represent biological networks using different interaction mechanisms
  • To implement, simulate and analyse dynamic network models in different software tools
  • To understand the common ground and the differences for applications in metabolic, regulatory, signalling, population and multi-scale biological processes
  • To integrate experimental data in modelling, estimate model parameters and assess the accuracy of parameter estimates and model predictions.

Previously organised courses can be found on the course archive page.