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636-0007-00L 6 Credits BSC , DR , MSC D-CHAB , D-INFK , D-BSSE , D-BIOL , D-MATH , D-MAVT , D-ITET

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Computational Systems Biology

Lecturers & Examiners: Prof. Dr. Jörg Stelling

VVZ CR 4.8

Last Updated: 2026-02-05 16:16:49

Abstract

Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).

Objective

The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.

Content

Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.

Resources

Lecture Notes

http://www.csb.ethz.ch/education/lectures.html

Literature

U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010. B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013

Learning Materials (Links)

Main link
Lecture Material

General Information

Language: English
Levels: BSC , DR , MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: keine

Course Components

Type	Title	Time & Place	Hours
lecture	Computational Systems Biology	Wed 14:15-17:00 (HG D 3.2) 17.01 Date 15:15-17:00 (HG D 3.2)	3 h weekly
exercise	Computational Systems Biology	Fri 10:15-12:00 (HG D 1.2)	2 h weekly

Offered In

Computational Science and Engineering Bachelor
- Fields of Specialization
  - Biology
Computer Science Bachelor
- Minor Courses
  - Minor Courses
Computational Science and Engineering Master
- Fields of Specialization
  - Biology
Chemical and Bioengineering Master
- Electives
  - Biochemical Engineering
Mathematics Master
- Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. Credits from other application areas cannot be recognised for further application areas.)
  - Biology
Biology Master
- Master Studies (Programme Regulations 2023)
  - Majors
    - Major in Biochemistry
      - Elective Compulsory Master Courses
    - Major in Systems Biology
      - Elective Compulsory Master Courses I: Computation
Computational Biology and Bioinformatics Master (More information at: )
- Core Courses (Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category.)
  - Biosystems
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 2018)
  - Track: Systems and Control (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Specialisation Courses (These specialisation courses are particularly recommended for the area of "Systems and Control", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.)
- Master Studies (Programme Regulations 2008)
  - Major Courses (A total of 42 CP must be achieved during the Master Programme. The individual study plan is subject to the tutor's approval.)
    - Systems and Control
      - Recommended Subjects (These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.)
Robotics, Systems and Control Master
- Core Courses
Doctorate Biosystems Science and Engineering (More Information at: )
- Subject Specialisation