Prospectus

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Metabolic Network Analysis (BM)

Course
2022-2023

Admission requirements

Elementary calculus, some basic knowledge of linear algebra is useful (e.g. matrix addition, matrix multiplication, Gaussian elimination method).

Description

This course is a primer in methods for mathematical modelling of metabolism, the ensemble of biochemical reactions that are essential for life.

After introducing classical kinetic models, the course will delve deeper into the concept of graph-based modelling of metabolic networks. The course will illustrate how network topology alone gives important biological insights. To this end, we will introduce the fundamentals of convex sets and how they relate back to the biology of the metabolic network being modelled. Concepts from the research literature such as extreme currents, extreme pathways, and elementary modes will be discussed. These concepts are applied to the problem of optimal metabolite production in industrial microbes, the Warburg effect in tumor cells, metabolic regulation of multicellular organisms, and the dynamics of microbial ecosystems. The lectures will include demonstrations of the software packages COPASI and COBRApy, as well as worked examples for algorithms for linear optimization and for finding generating vectors in the steady state solution space.

Course objectives

  • Learn to work with differential equation-based models and graph representation of (bio)chemical reaction networks, analysis methods and algorithms for computing flux balance analysis, elementary flux modes and extreme currents.

  • Understand the approaches and limitations of the modelling methods, read and understand current research on the topic.

  • Learn to approach biological questions on metabolism using differential equation-based modelling and flux-balance analysis.

Timetable

The schedule for the course can be found on MyTimeTable.

Mode of instruction

The class is taught primarily via lectures. Some lectures include step-by-step demonstrations of the software. Practical homework exercises ask students to use the presented software on new problems, and include also pen-and-paper exercises.
An individually written essay on a research paper will enable students to learn to read and interpret the literature hands-on.

Assessment Method

The final grade consists of two constituent examinations (30% + 30%), practical homework assignments (30%), and a practical team presentation (10%).

The constituent examinations are an individually written essay with subsequent oral presentation, and a written exam. The retakes for the essay is also written with subsequent oral presentation, and the written exam retake is written.
No minimum grade is required to take part for either exam. The homework and team presentation count as a practical and there is no retake for them.

To pass the course, the weighted average of all partial grades must be at least 5.5

Reading list

The course is based on materials collected from various sources and internal notes. Lecture slides will be made available for self-study. Various research papers will be distributed during the course.

Optional further reading:
Kremling, Andreas. Systems biology: mathematical modeling and model analysis. CRC Press, 2013. ISBN 9781466567894
Palsson, Bernhard Ø. Systems biology: properties of reconstructed networks. Cambridge university press, 2006. ISBN 9780521859035

Registration

From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.

Contact

Lecturer: Dr. E. Tsingos
Email: e.tsingos[at]math.leidenuniv.nl