The course introduces computat. methods for systems biology under ‘real-world’ conditions of limiting biological knowledge, uncertain model scopes and predictions, and spatial effects. Focus is on systems identification for mechanistic, deterministic models and the corresponding numerical approaches. Topics include uncertainty evaluation, experim. design, and numerical methods for spatial models
Objective
The aim of the course is to provide students with mathematical and computational methods for the analysis of biological systems in a ‘real world’ setting. This implies (i) incomplete knowledge of components, interactions, and their quantitative features in cellular networks, (ii) resulting uncertainties in model predictions and iterations between models and experiments, and (iii) spatial effects. All these factors make direct representations of biological mechanisms in mechanistic, deterministic mathematical models challenging. Based on general concepts of systems identification and on corresponding numerical methods, the course aims at providing an in-depth understanding of computational approaches that enable the analysis of mechanisms of biological network operation in detail, using iterations between experimental and theoretical systems analysis.
Content
Lecture topics: (1) Mechanistic mathematical models and systems identification challenges; (2-4) Structural models and data integration; (5-8) Identification and experimental design for ODE models; (9-10) Uncertainty quantification; (11-13) Numerical methods for partial differential equation (PDE) models to describe spatial effects.
Background literature will be available on-line at the start of the course.
Prerequisites / Notice
For this advanced course, participants are expected to have a solid background in the mathematical modelling of biological systems, as conveyed by the combination of the following two courses in the MSc Computational Biology and Bioinformatics: ‘Computational systems biology’ and ‘Spatio-temporal modeling in biology’.
