System Biology

• 26 hrs lectures
• 26 hrs pc labs

Students will be able to:
- mathematically describe the main components of gene expression
- mathematically describe the main components of signal transduction pathways
- mathematically describe the main components of neuronal pathways
- analyze network graphs using network motifs
- name the main network motifs of transcription, signal-transduction and neuronal-system networks
- explain principles of the main network motifs of transcription, signal-transduction and neuronal-system networks
- describe experimental mathods in systems biology

The aim of the subject is to provide students with basic knowledge of computational models in cellular biology and way of their use, knowledge of analysis methods applied to models in systems biology.

Students enrolled in this subject should be able to describe cellular systems, its main components regarding structure and function; analyze systems of ordinary differential equations and apply basic knowledge of probability distribution and combinatorics. In general, knowledge on the Bachelor's degree level is requested.

• Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., Herwig, R. Systems Biology: A Textbook. Wiley, 2009. ISBN: 978-3-527-31874-2

• Konopka, A.K. Systems Biology: Principles, Methods, and Concepts. CRC, 2006, ISBN: 978-0824725204
• Alon, U: An Introduction to Systems Biology, Design Principles of Biological Circuits. CRC, 2007, ISBN: 1-58488-642-0

1. Introduction to systems biology - from molecular biology of cell to computational models
2. Modeling of biochemical systems - mathematical and computational models to describe processes in living organisms
3. Specific biochemical systems - mathematical modelling of biological and chemical processes in examples
4. Model fitting - design and verification of correct models, comparison to real living systems
5. Analysis of high-throughput data - recent methods used in bioinfnormatics and their implications to systems biology
6. Gene expression models - mathematical modelling of gene expression
7. Stochastic systems and variability - from deterministic to stochastic description of nearly chaotic biochemical processes
8. Network structures, dynamics, and function - networks of models and their use
9. Optimality and evolution - extended dynamic and adaptive models for evolving processes
10. Experimental techniques in molecular biology
11. Linear control systems in modelling
12. Computer modeling tools in practice
13. Systems biology for future

1. Specific biochemical systems - mathematical modelling of biological and chemical processes in examples
2. Gene expression models - mathematical modelling of gene expression
3. Stochastic systems and variability - from deterministic to stochastic description of nearly chaotic biochemical processes
4. Optimality and evolution - extended dynamic and adaptive models for evolving processes
5. Selected computer modeling tools
6. Individual projects

upto 30 points from laboratories
upto 70 points from examination.
Examination has an on-line form.

Techning methods include lectures and computer laboratories. Course is taking advantage of e-learning (Moodle) system.

Laboratory tutorials are compulsory, properly justified absence can be compensated based on agreement of the tutor (usually in the last semester week).