Advanced Systems Biology

Are you curious to understand how a biological system operates? Are you interested in knowing which elements of the system are responsible for certain characteristics that are experimentally observed? Do you want to be able to use this knowledge as a steppingstone to build model that can reproduce the behaviour of a complex living system? If your answer is yes!, you should immerse yourself in the world of Systems Biology! Systems Biology aims to understand the properties and characteristics of living systems by considering the relationships and interactions among their constituents. It does so by employing theoretical and experimental tools borrowed from different disciplines. The unifying theme in System Biology is mathematical modelling which is the use of mathematical tools to create models that (possibly) reproduce the behaviour of a biological system as observed in nature or in the laboratory. By confronting model predictions with experimental data, the model is refined to bring it closer to reality and to generate new hypothesis that could be experimentally tested.

This course will cover several (advanced) topics in Systems and Synthetic Biology and every week brings in a different application or biological problem. The topics covered are: modelling of chemical reaction networks and analysis of dynamic mathematical models (using Ordinary Differential Equations), Metabolic and Gene regulatory networks, spatio-temporal models and pattern formation (using Partial Differential Equations), stochastic models, data-fitting and model optimisation, and scientific machine learning.

The course alternates lectures on theoretical aspects of Systems Biology with exercises and computer/computational practicals where you will develop programming and analysis skills by modelling and investigating exemplary biological systems. For instance you will learn how to model the biochemical process that gives birth to the stripes on the fur of your cat, how to describe the fact that cells are similar but not identical and how this can affect biological phenomena or how to determine the unknown parameters of your mathematical model starting from experimental data.

• Describe and analyze a biological system in terms of ordinary or partial differential equations

• Apply mathematical tools to solve a biological problem

• Create a mathematical problem that can explain experimental observations

• Generate experimental hypotheses from mathematical models

• Organize, present and discuss (both written and oral) the properties of a mathematical model and the results of computer simulations

• Assignment other (30%) Self-study assignments will be provided at the end of study weeks to assess students understanding of the week’s material. These will be open questions mixing analytical and computational methods.
• Assignment report (40%) Students will use the skills taught in the course to model and analyse an example biological system. Students are provided with background information, instructions, and the grading rubric. The project takes place in Week 6 of the course.
• Assignment oral presentation (10%) Description = Students provide a 15 minute presentation describing their project for others in the class. Presentations take place in Week 7 of the course and are graded using a rubric.
• Oral test (20%) Oral exam where students are assessed on what they have been taught in the course and what they have done in their project research. Students are provided with closed book preparation time to consider answers before the oral exam begins and they must explain to the examiners how they came to their answers. This part of examination can be redone or resat during resit periods

General Biological knowledge, Introduction to Systems & Synthetic Biology (SSB50806), Molecular Systems Biology (30306), Modelling in Systems Biology (SSB30806), Modelling Biological Systems (EZO23306). Stochastic Differential Equations and Data Assimilation (MAT34306).

• Reading material (articles, tutorials, references and suggested readings) will be made available at the Brightspace site of the course. The scripting language used during the course is Python. A brief Introduction to Python scripts will be provided. However programming skills are desired.