Course detail

Systems Biology

FEKT-MPA-SYSAcad. year: 2023/2024

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.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Offered to foreign students

Of all faculties

Entry knowledge

Students enrolled in this course 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.

Rules for evaluation and completion of the course

up to 16 points for reports from computer exercises
up to 14 points for completion and presentation of the semestral project
up to 70 points for exam.
Examination has an oral form.
Laboratory tutorials are compulsory, properly justified absence can be compensated based on agreement of the tutor (usually in the last semester week).

Aims

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 will be able to:
- mathematically describe the main components of gene expression
- mathematically describe the main components of signal transduction pathways
- analyze network graphs using network motifs
- name the main network motifs of transcriptional networks and signal-transduction pathways
- explain function of the main motifs of transcriptional networks and signal-transduction pathways
- describe experimental mathods in systems biology

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

Not applicable.

Elearning

Classification of course in study plans

  • Programme MPC-BTB Master's 1 year of study, summer semester, compulsory

  • Programme IT-MSC-2 Master's

    branch MBI , 0 year of study, summer semester, elective

  • Programme MITAI Master's

    specialization NISY , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, elective
    specialization NBIO , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, elective
    specialization NGRI , 0 year of study, summer semester, elective
    specialization NADE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NMAT , 0 year of study, summer semester, elective
    specialization NSEC , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, elective
    specialization NVER , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NEMB up to 2021/22 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Syllabus

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

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Syllabus

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

Elearning