Systems Biology

Postgraduate course

Course description

Objectives and Content

The course provides an introduction to the methods used in computational systems biology. It is based on a quantitative approach to molecular cell biology that allows traditional interaction diagrams to be extended to dynamic mathematical models. These models serve as working hypotheses: they help to understand and predict the behavior of complex systems, which often exhibit non-intuitive behavior. The course focuses on the construction and investigation of models for chemical reaction networks, biochemical kinetics, signal transduction pathways, and gene regulatory networks. Topics to be covered include ordinary differential equation models, stochastic models and Gillespie's stochastic simulation algorithm, stability, bifurcation and sensitivity analysis, parameter fitting and dose response analysis. The course also briefly reviews the necessary basics of mathematics and molecular cell biology.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The student can

  • construct dynamic mathematical models from given interaction diagrams,
  • run simulations by choosing appropriate numerical methods for the solution of the equations
  • analyze the qualitative behavior of the systems in terms of stability of solutions and steady states
  • apply algorithms for sensitivity analysis and parameter fitting
  • recognize, exemplify and explain typical network motifs for signaling pathways, protein interaction networks, metabolic networks and gene regulatory networks


The student is able to

  • implement, simulate and analyze biology-related mathematical models using available software packages in a programming language of their choice
  • argue for the choice of specific algorithms and figure out when and why an algorithm does not work

General competence

The students can

  • work on a biological modelling task on their own and in a small group
  • communicate their modelling activities to an interdisciplinary audience



Level of Study

Required Previous Knowledge
Recommended Previous Knowledge

Be able to implement basic algorithms in a programming language of your own choice. Experiences with the use of numerical software packages, e.g. numPy.

MAT111, MAT121, STAT110, BINF100

Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.
Teaching and learning methods

The course is given as lectures and mandatory exercises

Lectures, 4 hours per week

Exercises, 2 hours per week

Compulsory Assignments and Attendance
Compulsory assignments are valid for 1 subsequent semesters
Forms of Assessment

The forms of assessment are:

  • Mandatory exercises, 30 % of total grade.
  • Written examination (3 hours), 70% of total grade).

All compulsory assignments must be approved before examination

Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Assessment Semester
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
Reading List
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.
Course Evaluation
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
Examination Support Material
Non-programmable calculator, according to the faculty regulations.
Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact
Course Administrator
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.