Genome scale metabolic modelling

Single course PhD.

The course is offered every other year.

Recommended prerequisites are good biochemical knowledge especially with respect to metabolism. Basic knowledge in chemistry. Ideally basic knowledge in python programming. (Suggested to take Bio-3027/8027 before this course).

In time of growing amounts of omics datasets, modelling of large-scale biological networks and integration of multi-omics datasets can help to improve our understanding of mechanistic relationships in biosciences, especially with respect to metabolic networks. Modelling approaches can be used to help experimental design, to identify drug targets and in the prediction of disease related changes. In this course, we will focus on the modelling of large-scale metabolic pathways as well as current experimental approaches used to measure metabolic changes. We will discuss potential applications and limitations of modelling. In the practical part students will be introduced to different genome scale metabolic modeling approaches and metabolic flux analysis.

Who can apply as a singular course student:

• PhD student enrolled at another institution than UiT. PhD students must upload a document from their university stating that they are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.
• Holders of a master´s degree of five years or 3+2 years (or equivalent) may be admitted. These applicants must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you, see the following list: Proficiency in English must be documented - list of countries. For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document: Proficiency in english - PhD level studies

Knowledge

Upon completing the course, the student will:

• Demonstrate knowledge of current measurement techniques in metabolomics, including their applications and limitations.
• Exhibit understanding of genome-scale metabolic modeling methodologies and their role in systems biology.
• Understand stable isotope labeling techniques and their use in tracing metabolic pathways.
• Develop an understanding of metabolic flux analysis, including its theoretical foundations and practical applications.
• Evaluate the limitations, assumptions, and challenges of different modeling approaches in metabolomics.

Skills

Upon completing the course, the student will be able to:

• Perform genome-scale metabolic modeling using e.g. RNA-Seq data
• Use metabolic flux analysis to investigate and quantify metabolic pathways.
• Integrate and analyze multi-omics datasets to derive meaningful biological insights.
• Be able to perform computational analysis with tools for genome-scale modeling (e.g., COBRA toolbox, RAVEN)
• Use different data integration techniques for multi-omics (e.g., transcriptomics, proteomics, metabolomics)

General Competences

Upon completing the course, the student will:

• Demonstrate the ability to critically analyze and discuss concepts in metabolomics and metabolic modeling.
• Integrate knowledge from multiple disciplines (e.g., biology, chemistry, computational science) to address research questions in systems biology.
• Communicate findings and methodologies effectively to both specialist and non-specialist audiences.
• Evaluate research literature in metabolomics and systems biology

Recommended prerequisites

BIO-3027 Scientific Programming with Python in the life sciences , BIO-8027 Scientific Programming with Python in the life sciences

Language of instruction and examination

Schedule

More info about the coursework requirements

Active participation in the course with minimum 80% attendance.

Approved work requirements are valid for three years.