Course content

This class will give an introduction to systems biology methods in modeling and analysis of cellular networks. This course has a special emphasis on (1) biological interaction networks, and (2) genome-level cellular metabolism. Students will learn through lectures and applied projects, and gain hands-on experience using Python programming. An interdisciplinary presentation of the topics will be emphasized, making the class accessible to students with a background in computer science, biology, chemistry, and physics.

Learning outcome

At the completion of this course, the students are expected to be able to:

• Explain basic concepts, models, and statistical measures to characterize the properties of general networks, as well as using Python programming tools to analyze empirical networks.
• Explain basic concepts, principles and methods of metabolic engineering.
• Explain the organization and construction process of genome-scale metabolic networks, explain the principles behind constraint-based analysis (especially Flux Balance Analysis), as well as being proficient in the use of Python programming tools for the numerical analysis of empirical models.

Learning methods and activities

Lectures will be given in English.

Compulsory assignments

• Exercises

Recommended previous knowledge

Basic knowledge in molecular biology similar to TBT4145/TBT4146 Molecular Genetics, statistics similar to ST0103 Statistics with Applications.

Required previous knowledge

Basic programming in python, equivalent to TDT4110: Introduction to Information Technology.

Course materials

• B.O.Palsson "Systems Biology: Constraint-based Reconstruction and Analysis" 2nd edition (2015).
• A.-L. Barabási "Network Science" (2016).
• Compendium.