Introduction to Systems Biology

Course Objective

Introduction to Systems Biology is the starting course of the Bioinformatics and Systems Biology master (together with Fundamentals of Bioinformatics). To emphasise the importance of systems thinking biology, this course introduces the principles of systems biology, with a primary focus on the analysis of metabolic networks using stoichiometric modelling.

The course explores how genomic information can be translated into computational models to understand and predict cellular behaviour, linking genotype and phenotype. The course will see the application of these models in biotechnology and medicine, utilizing mathematical concepts (particularly linear algebra) and computational tools.

After completing the course successfully [Bloom taxonomy]:

• You understand the challenge of linking genomic information (genotype) to cellular behaviour (phenotype). [understand]
• You can write short code to solve simple problems and complete skeleton scripts for computational modelling of biological processes [apply]
• You can solve mathematical problems using algebra. [apply]
• You are able to represent biological information, specifically metabolic networks, in a computable format using mathematical concepts. [evaluate]
• You are able to use computational tools and algorithms to analyse metabolic networks. [apply]
• You appreciate the importance of experimental data and design in systems biology. [analyse]

This will enable you to understand and perform computational analysis related to:

• Systems Biology Research: Using metabolic network models to understand cellular growth under different conditions.
• Biotechnological Applications: Identifying genes to modify in microbial genomes for the production of valuable products like biofuels.
• Medical Applications: Identifying potential drug targets within the metabolism of pathogens or cancer cells for therapeutic intervention.

Fundamental knowledge:

Students will have the opportunity to level up their knowledge of molecular biology, maths (linear algebra and calculus) and programming (Python), according to their needs.

Course Content

The course will cover the following key topics:

• Genome Annotation and Network Reconstruction: Decoding genomic information to build metabolic networks.
• Stoichiometric Principles: Understanding chemical element and charge conservation in biochemical reactions, stoichiometric coefficients, and balancing reactions.
• Reaction Networks and Stoichiometric Matrix: Representing networks mathematically using the stoichiometric matrix and formulating mass balance equations.
• Steady-State Analysis: Concepts of steady-state, null space of the stoichiometric matrix, steady-state flux distributions, and the rank theorem.
• Linear Programming and Flux Balance Analysis (FBA): Introduction to FBA as a core computational method for analysing metabolism.

Conversion classes:

Depending on their needs, students can choose to attend conversion classes where they will be working to fill gaps in their knowledge in mathematics (linear algebra and differential equations), programming or biology.

Additional Information Teaching Methods

• Lectures
• Workgroups
• Conversion classes in biology or mathematics or programming

Method of Assessment

A written exam. To pass the exam, the grade must be 5.5 or higher. Retakes of the exam take place once a year. The last obtained pass grade will count as the final grade for the course.

Literature

Additional Information Target Audience

Students with a bachelor degree in Biology, Bioinformatics, Mathematics, Physics or similar background

Additional Information

Generative AI Policy:

In this course, generative AI (GenAI), such as ChatGPT or similar tools, may be used in a limited way, for example for editorial support, rephrasing, or structure. Substantive contributions from GenAI may only be used within the limits set by the lecturer. Any use of GenAI must always be explicitly disclosed, in accordance with the lecturer's instructions. The student remains fully responsible for the content, quality, and accuracy of the submitted work. Incorrect use of GenAI is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.

Recommended background knowledge

Bachelor in any science discipline (including medicine), with an interest in applying mathematical techniques in biology. Mathematics at high-school level is necessary. When in doubt please contact the coordinator.