Introduction to spatiotemporal modeling and simulation
This course teaches modeling techniques for spatially resolved systems. You will learn to account for the geometry of a system and for transport in space. After repetition of the basics from mathematics and physics, you will model processes such as diffusion and flow, and simulate them in the computer.
dimensionality analysis, causality diagrams, vector fields, particle methods, governing equations for diffusion and flow, hybrid particle-mesh methods for computer simulations, student project: simulation of a biological system.
Lecture: Tuesdays, 13:00-14:30 (4. DS.), CSBD Seminar Room 1 (Pfotenhauerstr. 108) / FIRST LECTURE: APR 4, 2017
Exercises: Tuesdays, 14:50-16:20 (5. DS.), CSBD Seminar Room 1 (Pfotenhauerstr. 108) / FIRST TUTORIAL: APR 11, 2017
Lecture: Prof. Ivo F. Sbalzarini
Exercises: Dr. Vojtech Kaiser
Analysis of the dynamic behavior of biological or physical systems with spatial structure
Formulation of a model of the system behavior
Computer simulation of the model using numerical methods
We focus on biological systems. The taught methods and concepts are, however, applicable in a much broader sense.
Lecture language: ENGLISH
Please find below the lecture syllabus, the slides, the self-check questions, and the exercises:
- Lecture 1 - Administration and Introduction (Slides PDF, Handouts PDF, Slides Intro PDF, Handouts Intro PDF, Self-test questions PDF)
- Lecture 2 - Dimensional Analysis (Slides PDF, Handouts PDF, Self-test questions PDF, Exercise PDF, Solution PDF)
- Lecture 3 - Modeling Dynamics: Reservoirs and Flows (Slides PDF, Handouts PDF, Self-test questions PDF, Exercise PDF
- Lecture 4 - Recap on Vector Analysis
- Lecture 5 - Conservation Laws and Control Volume Methods
- Lecture 6 - Particle Methods
- Lecture 7 - Diffusion
- Lecture 8 - Reaction-Diffusion
- Lecture 9 - Advection-Diffusion
- Lecture 10 - Flow
- Lecture 11 - PDEs
Full lecture notes can be found here: Script (PDF).
The student project will aim at implementing the Quorum Sensing model proposed by J. Müller et al. as described in this publicly available preprint.