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.
Contents
dimensionality analysis, causality diagrams, vector fields, particle methods, governing equations for diffusion and flow, hybrid particlemesh methods for computer simulations, student project: simulation of a biological system.
Time/Place
Summer Term
Lecture: Tuesdays, 13:0014:30 (4. DS.), CSBD Seminar Room 1 (Pfotenhauerstr. 108) / FIRST LECTURE: APR 4, 2017
Exercises: Tuesdays, 14:5016:20 (5. DS.), CSBD Seminar Room 1 (Pfotenhauerstr. 108) / FIRST TUTORIAL: APR 11, 2017
Teachers
Lecture: Prof. Ivo F. Sbalzarini
Exercises: Dr. Vojtech Kaiser
Learning goals

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 selfcheck questions, and the exercises:
 Lecture 1  Administration and Introduction (Slides PDF, Handouts PDF, Slides Intro PDF, Handouts Intro PDF, Selftest questions PDF)
 Lecture 2  Dimensional Analysis (Slides PDF, Handouts PDF, Selftest questions PDF, Exercise PDF, Solution PDF)
 Lecture 3  Modeling Dynamics: Reservoirs and Flows (Slides PDF, Handouts PDF, Selftest 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  ReactionDiffusion
 Lecture 9  AdvectionDiffusion
 Lecture 10  Flow
 Lecture 11  PDEs
Full lecture notes can be found here: Script (PDF).
Project
The student project will aim at implementing the Quorum Sensing model proposed by J. Müller et al. as described in this publicly available preprint.