Course - Metabolic Pathway Analysis - BT8118
BT8118 - Metabolic Pathway Analysis
Lessons are not given in the academic year 2020/2021
This course will provide a 1-week intensive introduction to the computational analysis and reconstruction of both small and genome-scale metabolic networks. The goal is to make students familiar with state-of-the-art computational tools and databases, as well as providing the students with hands-on experience. Furthermore, we will cover the mathematical basis of constraint-based analysis of genome-scale metabolic models and provide a foundation for stability and control analysis of dynamic models.
The first part of the course (2.5 days) will focus on the organization of metabolic networks; their reconstruction and the mathematical basis of optimization approaches used in constraint-based modeling. We will use the COBRA toolbox in Matlab and/or COBRApy for python. We will also discuss available methods for data integration.
The second part of the course (2.5 days) will focus on the reconstruction and analysis of small, mechanistic models of metabolic pathways that allow the simulation of dynamic properties of the system. In this context, we will provide an introduction to stability and control analysis.
Explain the organization and structure of metabolic models
Knowledge about relevant databases
Capable of reconstructing metabolic models
Explain the underlying principles of constraint based modeling approaches
Explain stability and control analysis
Proficiency in use of COBRA toolbox for genome-scale metabolic modeling
Explain limits of small and genome-scale metabolic analysis
Knowledge about tools for data integration
Learning methods and activities
Lectures, presentation and computer exercises. The course will be given in English and will take place during 1 week of the semester.
Further on evaluation
Project report (10 pages) that will be evaluated and needs to be approved. Grades: pass/fail
Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.
Recommended previous knowledge
It is recommended that the participants have basic knowledge in bioinformatics, and mathematical knowledge of linear algebra and differential equations.
Course materiel will be announced to registered participants before the start of the course.
Credits: 7.5 SP
Study level: Doctoral degree level
Language of instruction: English, Norwegian
- Technological subjects
Department with academic responsibility
Department of Biotechnology and Food Science
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"