Course Information

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Taught in person.

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Troják, Ph.D. (assistant)

Guaranteed by

doc. RNDr. David Šafránek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 12:00–13:50 B411

Prerequisites

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 62 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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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. In the case of online teaching lectures are implemented synchronously through a suitable videoconferencing software.

Assessment methods

Written final examination (50%), semester project (50%). In case of online teaching the exam is oral.

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Troják, Ph.D. (assistant)

Guaranteed by

doc. RNDr. David Šafránek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 10:00–11:50 A319

Prerequisites

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 62 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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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. In the case of online teaching lectures are implemented synchronously through a suitable videoconferencing software.

Assessment methods

Written final examination (50%), semester project (50%). In case of online teaching the exam is oral.

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Troják, Ph.D. (assistant)

Guaranteed by

doc. RNDr. David Šafránek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 15. 9. to Wed 8. 12. Wed 10:00–11:50 C525

Prerequisites

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 62 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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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. In the case of online teaching lectures are implemented synchronously through a suitable videoconferencing software.

Assessment methods

Written final examination (50%), semester project (50%). In case of online teaching the exam is oral.

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online.

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Troják, Ph.D. (assistant)

Guaranteed by

doc. RNDr. David Šafránek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 16:00–17:50 C511

Prerequisites

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 62 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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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. In the case of online teaching lectures are implemented synchronously through a suitable videoconferencing software.

Assessment methods

Written final examination (50%), semester project (50%). In case of online teaching the exam is oral.

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Troják, Ph.D. (assistant)

Guaranteed by

doc. RNDr. David Šafránek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 12:00–13:50 B411

Prerequisites

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 62 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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Hajnal (assistant)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 10:00–11:50 B411

Prerequisites

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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Hajnal (assistant)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 14:00–15:50 B411

Prerequisites

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.

Learning outcomes

At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract 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;
use state-of-the-art modeling and analysis software tools.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)
RNDr. Matej Hajnal (assistant)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 10:00–11:50 A218

Prerequisites

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.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 8:00–9:50 C511

Prerequisites

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.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. 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

Timetable

Thu 12:00–13:50 B410

Prerequisites

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.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. 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

Timetable

Tue 8:00–9:50 B411

Prerequisites

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.

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. 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

Timetable

Thu 18:00–19:50 B411

Prerequisites

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 free modeling and analysis software tools;
model and analyze dynamic properties of interaction networks

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 18:00–19:50 C525

Prerequisites

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 free modeling and analysis software tools;
model and analyze dynamic properties of interaction networks

Syllabus

• 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.

Literature

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. URL 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

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~xsafran1/PB050/

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 18:00–19:50 B411

Prerequisites

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 24 fields of study the course is directly associated with, display

Course objectives

At the end of the course, students will be able to:
understand complex models of biological processes;
apply abstract computer-scientific thinking to modeling and analysis of complex systems with special focus to biological systems;
practically use free modeling and analysis software tools;
perform static analysis of large-scale interaction networks;
model and analyze dynamic properties of interaction networks

Syllabus

• 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.
• Sources of biological data: databases of biological knowledge, databases of biological models.
• Specification of a biological model: biological networks and pathways, languages SBML and SBGN.
• Static analysis of biological systems: analysis of biological networks and pathways, network motifs and biological circuits.
• Modeling and simulation of biological systems dynamics: hypotheses prediction.
• Modeling of Escherichia coli bacteria: genetic regulatory network, models of loccomotion organ synthesis and chemotaxis, nutritional stress response models.
• Model parameters, robustness and parameter sensitivity.

Literature

recommended literature
• ALON, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/Crc, 2006. info

not specified
• 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. URL info
• 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. Group projects.

Assessment methods

Written final examination (50%), semester project (50%).

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology
• PA183 Project in Systems Biology
• PB051 Computational methods in Bioinformatics and Systems Biology

Further Comments

Study Materials
The course is taught annually.

PB050 Modelling and Prediction in Systems Biology

Extent and Intensity

1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Šafránek, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 16:00–17:50 B410

Prerequisites

This is an interdisciplinary course that extends the knowledge of bachelor students. 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 22 fields of study the course is directly associated with, display

Course objectives

At the end of the course, students will be able to apply abstract computer-scientific thinking to in silico modeling and analysis of living organisms. Students will become familiar with a variety of tools devoted to modeling and simulation in systems biology. Relevant practical skills will be obtained by working on a semestral project.

Syllabus

• 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.
• Sources of biological data: databases of biological knowledge.
• Specification of a biological model: biological networks and pathways, SBML language.
• Static analysis of biological systems: analysis of biological networks and pathways, network motifs and biological circuits.
• Modeling and simulation of biological systems dynamics: hypotheses prediction.
• Modeling of Escherichia coli bacteria: genetic regulatory network, models of loccomotion organ synthesis and chemotaxis, nutritional stress response models.
• Robustness and parameter sensitivity.

Literature

• ALON, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/Crc, 2006. 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. Group projects.

Assessment methods

Written final examination (50%), semester project (50%).

Language of instruction

Czech

Follow-Up Courses

• PA054 Formal Methods in Systems Biology

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (recent)