Courses: BINF305 Systems Biology - Spring 2019
ECTS Credits
10
Level of Study
Master
Full-time/Part-time
Full-time
Language of Instruction
English
Teaching semester
Autumn
Place of Instruction
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, pharmacokinetics, metabolic networks, 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, flux balance analysis, dose response analysis, reconstruction of networks from observational data using ODE-based, information theoretic and statistical methods, and topological analysis of complex networks. 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:
Knowledge
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
- perform flux balance analysis on metabolic networks
- reconstruct gene regulatory networks from time series and perturbational gene expression data
- analyze large-scale networks using graph-theoretical pattern matching and clustering algorithms
- recognize, exemplify and explain typical network motifs for signaling pathways, protein interaction networks, metabolic networks and gene regulatory networks
Skills
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 detect causes of failure
General competence
The student can
- work on a biological modelling task on their own and in a small group
- communicate their modelling activities to an interdisciplinary audience
Required Previous Knowledge
INF100
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
Credit Reduction due to Course Overlap
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
Examination Support Material
Non-programmable calculator, according to the faculty regulations.
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.
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 mailto:studieveileder@ii.uib.noStudent adviser
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.
Contact Information
Student adviser:
mailto:studieveileder@ii.uib.noStudent adviser
T: 55 58 42 00