Course details

System Biology

SYSa FEEC BUT MPA-SYS Acad. year 2021/2022 Summer semester 5 credits

The course is oriented to gain knowledge of methods used in systems biology, creating models of cellular organisms and possibilities of their usage. It aims on computational methods used to describe behavior of living organisms on molecular level that are utilizable in cellular biology, biochemistry, and biotechnology.
Studied models are represented by extensive network graphs. Special attention is paid to both methodologies of model analysis, static as well as dynamic, especially using quantitative ODE models. The concept of hierarchy is followed and all functional layers, from gene regulatory network to signaling pathways and metabolic networks, are presented. Examples of models are given on systems of particular, especially unicellular, organisms.

Guarantor

Language of instruction

English

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 26 hrs pc labs

Assessment points

78 exam, 22 exercises

Department

Lecturer

Instructor

Subject specific learning outcomes and competences

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.

Learning objectives

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.

Prerequisite kwnowledge and skills

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.

Fundamental literature

  • Konopka, A.K. Systems Biology: Principles, Methods, and Concepts. CRC, 2006, ISBN: 978-0824725204 (EN)
  • Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., Herwig, R. Systems Biology: A Textbook. Wiley, 2009. ISBN: 978-3-527-31874-2 (EN)
  • Alon, U: An Introduction to Systems Biology, Design Principles of Biological Circuits. CRC, 2007, ISBN: 1-58488-642-0 (EN)
  • Maly, Ivan V. Systems biology. Humana Press, New York 2009. ISBN 978-1-934115-64-0. (CS)
  • Rosypal, S. Nový přehled biologie. Scientia, Praha 2003. ISBN 80-7183-268-5 (CS)
  • Dubitzky, W., Wolkenhauer, O., Cho, K.-H., Yokota, H., Encyclopedia of systems biology. Springer, New York 2013. ISBN 978-144-1998-644. (CS)

Syllabus of lectures

  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

Syllabus of computer exercises

  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

Progress assessment

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).

Controlled instruction

Upto 22 points from laboratories.
Upto 78 points from examination.
Examination has a written form.

Course inclusion in study plans

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