PB050 Modelling and Prediction in Systems Biology

Faculty of Informatics
Autumn 2014
Extent and Intensity
1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
RNDr. David Šafránek, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing - Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
Thu 12:00–13:50 B410
This is an interdisciplinary course that extends the knowledge of bachelor students of all study branches. The course is especially recommended for students of Bioinformatics.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 25 fields of study the course is directly associated with, display
Course objectives
At the end of the course, students will be able to:
understand basic principles of quantitative modeling,
understand dynamic computational models of complex systems in the domain of biological processes;
apply abstract computer-scientific thinking to modeling and analysis of complex systems with special focus to biological systems;
practically use state-of-the-art modeling and analysis software tools;
model and analyze dynamic properties of complex interaction networks.
  • History and scope of systems biology.
  • Basic notions: living organism as a system with precisely given structure and functionality, in silico model, abstraction, simulation and prediction, model validation.
  • Specification of a biological model: biological networks and pathways, languages SBML and SBGN.
  • Emergent properties of systems dynamics, their specification and encoding.
  • Modeling and simulation of biological systems dynamics: hypotheses prediction.
  • Modeling of Escherichia coli bacteria: genetic regulatory network, models of locomotion organ synthesis and chemotaxis, nutritional stress response models.
  • Notion of stochasticity in biological dynamics, basic principles of stochastic models, chemical master equation, Monte Carlo simulation.
  • Model parameters, robustness and parameter sensitivity.
    recommended literature
  • VRIES, Gerda de. A course in mathematical biology : quantitative modeling with mathematical and computational methods. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 2006. xii, 309. ISBN 0898716128. info
  • ALON, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. : Chapman & Hall/Crc, 2006. info
  • WILKINSON, Darren James. Stochastic modelling for systems biology. Boca Raton: Chapman & Hall/CRC, 2006. 254 s. ISBN 1584885408. info
    not specified
  • NOBLE, Denis. Music of life : biology beyond the genome. Oxford: Oxford University Press, 2006. xiii, 153. ISBN 9780199295739. info
  • System modeling in cell biology : from concepts to nuts and bolts. Edited by Zoltan Szallasi - Jorg Stelling - Vipul Periwal. Cambridge, Mass.: MIT Press, 2006. xiv, 448. ISBN 0262195488. info
  • Computational modeling of genetic and biochemical networks. Edited by James M. Bower - Hamid Bolouri. Cambridge: Bradford Book, 2001. xx, 336. ISBN 0262524236. info
Teaching methods
Lectures and optional homeworks. Semestral projects.
Assessment methods
Written final examination (50%), semester project (50%).
Language of instruction
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Teacher's information
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019.
  • Enrolment Statistics (Autumn 2014, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2014/PB050