The main theme of the Quantitative and Predictive Modelling course is how to describe the dynamic behaviour of biological systems and integrate experimental data. Concepts of modelling are introduced via a great variety of examples taken from diverse practices. The emphasis is on providing an overview of modelling approaches rather than an in-depth treatment of a few techniques. A short introduction will be given of top-down versus bottom-up modelling, together with a classification of biological systems (metabolic, regulatory, signalling, population, multi-scale, statistical models). The course includes an overview of and acquaintance with available software tools, among others Matlab will be used.
Date: June 22-26, 2015
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 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.
Program: This five-day course is structured according to five main topics:
- Differential equation modelling of metabolic, regulatory, signalling, population and multi-scale biological processes
- The link of differential equation modelling with other modelling approaches, including Boolean modelling
- Parameter estimation to integrate experimental data in modelling and fit models to data
- Uncertainty analysis to quantify the effect of errors and noise in the input data on model predictions, and how model-based experimental design can improve the predictive quality of models
- Show-case examples of applications
Learning Objectives: The students will be provided with a theoretical basis, methods and computational hands-on experience on differential equation modelling, parameter estimation and uncertainty analysis.