Lecture: Stochastic Dynamics in Systems Biology – WS13/14

Instructor: Prof. Dr. Verena Wolf
Assistant: Alexander Andreychenko

  • Grades for final exam. Inspection: 20 February, 14:30-16:30 (other dates/times will be announced later)
  • We offer a follow up seminar in the summer term (link).
  • Since we had twice as many lectures before the midterm compared to the number of lectures after the midterm, we will compute the final grade as 2/3*grade of midterm + 1/3*grade of final.
  • An oral re-exam about the whole material is possible (but the grade of the written exams then become obsolete).

Schedule: Tuesday 12:15 – 13.45, Building E2 1 – Room 007

Tutorial slots:
Thursday 12:15-13:45 (BioInf, SR001)
Credits: 6 ECTS points

Course Material:

The course aims at giving the participants knowledge of methods, techniques, and concepts in the study of dynamical models that arise in the area of systems biology. The main focus of the course is on the mathematical analysis of intrinsically stochastic processes in the cell (e.g. gene expression). This course is a flipped classroom course which means that exercises are solved/presented during the two slots and the homework consists in watching videos which will be uploaded at the course homepage.

The course is open to students from computer science or bioinformatics interested in stochastic modelling and systems biology. The course does not require previous experience in biology. Mathematical skills as well as basic programming skills are of advantage but not mandatory.

Certification Conditions:
There will be two written exams. Their grades contribute with 2/3 (midterm) and 1/3 (final) to the final grade (“Schein”). The assignments are optional but bonus points may be obtained during the tutorial. An oral re-exam is possible.

Exam Schedule: Midterm exam: Dec 17, 12:15 – 13:25 Building E1 3 – HS  003 (results)
final exam: Feb 11, 12:15 – 13:15 Building E1 3 – HS  001

Using Matlab:
As a part of the assignments the participants will have to program in Matlab. Instructions on how to get access to Matlab.


    • Reaction Rate Equations
    • Why Stochastic Models?
    • Basic concepts of probability
    • Stochastic chemical kinetics
    • Simulation algorithms
    • Basic concepts of statistics
    • Statistical output analysis
    • Direct Approaches
    • Moment Closure Approaches
    • Hybrid Approaches
    • Maximum Likelihood Estimation
    • Bayesian Inference

Text Books:

  • Stochastic Modelling for Systems Biology. Darren J. Wilkinson, Crc. Pr. Inc, 2006.
  • Simulation Modelling and Analysis. Averill M. Law, Mcgraw-Hill, 2006.
  • Introduction to the Numerical Solution of Markov Chains. William J. Stewart, Princeton Univ. Pr., 1994.
  • Systems Biology: Dynamic Pathway Modelling. O. Wolkenhauer.
  • INTRODUCTION TO PROBABILITY. C. Grinstead and L. Snell