Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

636-0706-00L  Spatio-Temporal Modelling in Biology

SemesterAutumn Semester 2016
LecturersD. Iber
Periodicityyearly recurring course
Language of instructionEnglish


AbstractThis course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. A wide range of mathematical techniques will be presented as part of the course, including concepts from non-linear dynamics (ODE and PDE models), stochastic techniques (SDE, Master equations, Monte Carlo simulations), and thermodynamic descriptions.
ObjectiveThe aim of the course is to introduce students to state-of-the-art mathematical modelling of spatio-temporal problems in biology. Students will learn how to chose from a wide range of modelling techniques and how to apply these to further our understanding of biological mechanisms. The course aims at equipping students with the tools and concepts to conduct successful research in this area; both classical as well as recent research work will be discussed.
Content1. Introduction to Modelling in Biology
2. Morphogen Gradients
3. Turing Pattern
4. Travelling Waves & Wave Pinning
5. Application Example 1: Dorso-ventral axis formation
6. Chemotaxis, Cell Adhesion & Migration
7. Introduction to Numerical Methods
8. Simulations on Growing Domains
9. Image-Based Modelling
10. Branching Processes
11. Cell-based Simulation Frameworks
12. Application Example 2: Limb Development
13. Summary
Lecture notesAll lecture material will be made available online
Link
LiteratureMurray, Mathematical Biology, Springer
Forgacs and Newman, Biological Physics of the Developing Embryo, CUP
Keener and Sneyd, Mathematical Physiology, Springer
Fall et al, Computational Cell Biology, Springer
Szallasi et al, System Modeling in Cellular Biology, MIT Press
Wolkenhauer, Systems Biology
Kreyszig, Engineering Mathematics, Wiley
Prerequisites / NoticeThe course builds on introductory courses in Computational Biology. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques.