636-0006-00L  Computational Systems Biology: Deterministic Approaches

SemesterSpring Semester 2019
LecturersJ. Stelling, D. Iber
Periodicityyearly recurring course
Language of instructionEnglish


636-0006-00 GComputational Systems Biology: Deterministic Approaches Special students and auditors need a special permission from the lecturers.
Students are expected to have completed the courses Computational systems biology’ and ‘Spatio-temporal modeling in biology’ (MSc Computational biology and bioinformatics), which provide the foundational knowledge for the course.
3 hrs
Tue13:15-16:00BSB E 4 »
J. Stelling, D. Iber

Catalogue data

AbstractThe course introduces computat. methods for systems biology under ‘real-world’ conditions of limiting biological knowledge, uncertain model scopes and predictions, and spatial effects. Focus is on systems identification for mechanistic, deterministic models and the corresponding numerical approaches. Topics include uncertainty evaluation, experim. design, and numerical methods for spatial models
ObjectiveThe aim of the course is to provide students with mathematical and computational methods for the analysis of biological systems in a ‘real world’ setting. This implies (i) incomplete knowledge of components, interactions, and their quantitative features in cellular networks, (ii) resulting uncertainties in model predictions and iterations between models and experiments, and (iii) spatial effects. All these factors make direct representations of biological mechanisms in mechanistic, deterministic mathematical models challenging. Based on general concepts of systems identification and on corresponding numerical methods, the course aims at providing an in-depth understanding of computational approaches that enable the analysis of mechanisms of biological network operation in detail, using iterations between experimental and theoretical systems analysis.
ContentLecture topics: (1) Mechanistic mathematical models and systems identification challenges; (2-4) Structural models and data integration; (5-8) Identification and experimental design for ODE models; (9-10) Uncertainty quantification; (11-13) Numerical methods for partial differential equation (PDE) models to describe spatial effects.
Lecture notesCourse material will be made available at: Link
LiteratureBackground literature will be available on-line at the start of the course.
Prerequisites / NoticeFor this advanced course, participants are expected to have a solid background in the mathematical modelling of biological systems, as conveyed by the combination of the following two courses in the MSc Computational Biology and Bioinformatics: ‘Computational systems biology’ and ‘Spatio-temporal modeling in biology’.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersJ. Stelling, D. Iber
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 20 minutes
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


General : Special students and auditors need a special permission from the lecturers

Offered in

Biotechnology MasterElectivesWInformation
Computational Biology and Bioinformatics MasterBiosystemsWInformation
Computational Science and Engineering BachelorElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation