Time: February 19th to March 1st, 2024. Please note that the course is two weeks.
Place: University of Bergen, Department of biomedicine
Course responsible: Ines Heiland, Mathias Ziegler, and Suraj Sharma
Invited lecturers: Sascha Schäuble, HKI, Jena Germany; Marcel Kwiatkowski, University of Innsbruck; Marie Migaud, University of South Alabama
Suggested amount of ECTS: 10
Number of participants: max 20
Registration form: HERE
Registration deadline: February 5th
Course description
This course will provide an introduction to the computational analysis and reconstruction of both small and genome-scale metabolic networks. The main goal is to familiarize students with relevant state-of-the-art computational tools and databases, as well as to provide the students with hands-on experience in metabolic modelling approaches. 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.
In the first part of the course (6 days), we will focus on experimental approaches to metabolomics analyses and on the organization of genome-scale metabolic networks; their reconstruction and the mathematical basis of optimization approaches used in constraint-based modeling. We will use COBRApy, a Python based toolbox, for the analysis and manipulation of genome-scale metabolic models.
The second part of the course (4 days) will focus on the reconstruction and analysis of small-scale, mechanistic models of metabolic pathways to simulate the dynamic properties of the system. In this context, we will provide an introduction to stability and control analyses. We will also discuss available methods for data integration.
During the course, a poster session will be organized. The students are expected to present their research projects during the poster session thereby getting the opportunity to discuss potential applications of modelling approaches to their own research.
The students will develop an individual modelling project that will be the basis of the homework assignment. The aim is to apply the methods taught during the course to individual research projects ideally to be continued during their academic training.
Course program
The course has a duration of two weeks (10 days, Monday through Friday), each day from 09-16 with a 1-hour lunch break. The course is designed such that lectures and practical exercises will be balanced by allocating about 50% of time for each of them. The students will receive a reading list before the course and will develop a course project during the course. They are furthermore expected to bring a poster about their own research projects to be presented in a poster session during the course. The homework project will require the students to apply methods taught during the course, and the students will have to prepare and return a project report 4 weeks after the end of the course teaching.
Prerequisites
Basic competence in a programming language (preferably Python) is expected. Basic knowledge in biochemistry, linear algebra and statistics are required.
Learning outcomes and competence
– Familiarity with current methods of experimental metabolomics
– Explain the organization and structure of metabolic models
– Knowledge about relevant databases
– Capability to reconstruct 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
Evaluation
Poster, Exercises and project report (10 pages) that will be evaluated.
Grades: pass/fail