Spatiotemporal Modeling and Simulation

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 particle-mesh methods for computer simulations, student project: simulation of a biological system.


Time/Place
Summer Term

Lecture: Mondays, 11:10-12:40 (3. DS.), CSBD Seminar Room 1 (Pfotenhauerstr. 108) / FIRST LECTURE: APR 9, 2018
No lecture on May 7, May 21 (Pentecost), July 9 + 16
Exercises: Fridays, 1pm-2:30pm, CSBD Room 121 (Pfotenhauerstr. 108), except:

  • 11th May: NO tutorial. There is no lecture on 7th of May either.
  • 18th May: Tutorial takes place at the Max Planck Institute of Molecular Cell Biology and Genetics in Seminar Room 2: enter the building, go up the spiral staircase to the second floor, the seminar room is directly opposite the stairs.
  • 29th May: Tutorial from 1st of June is shifted to Tuesday, 29th of May, 9:20 - 10:50 in APB-2026 (Andreas-Pfitzmann-Bau, University main campus).

Teachers

Lecture: Prof. Ivo F. Sbalzarini
Exercises: Karl Hoffmann


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

Special remarks

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: Script

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.