Introductory description

This module focuses on how complex biological systems can make decisions. For example, the body must respond to changes in the local environment quickly and reliably. The approach taken here is to use mathematical modelling to understand how networks and feedback can generate precise responses, such as switching and pulse generation. Relatedly, we will develop the students ability to perform estimation and quick calculations relevant to biological systems.

The module will be compulsory for second year Integrated Natural Science (INS) students. The module builds on the ethos of INS, with a clear emphasis on solving problems by drawing freely from the methods and mindsets of more than one discipline. The module will be open to SLS students who want to expand their quantitative and mathematical skills. No prerequisites are required, though students without knowledge of calculus will need to do some prescribed pre-module reading and exercises.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Block 1: Numbers in biology – learning to estimate
Back-of-envelope calculations
Scales in biology
Number patterns in biology

Block 2: Law of mass action and application to simple gene networks
Michaelis-Menten simple derivation
Thresholds for generating gene response
Concept of steady-state
Non-dimensionalisation

Block 3: Generating simple circuits with genes
Simple gene network – feedforward
Positive feedback switch
Effects of noise on switching

Block 4: Timing in gene networks
Multiple thresholds for generating timing sequence
Pulse generation
Basic intro to generating biological oscillators

Block 5: Interpretation of spatial information by gene networks
Morphogen gradients and positional information
Interpreting morphogens through gene networks

Project: Extracting and analysing quantitative imaging data
Students given imaging data, e.g. butterfly wing images. In groups of around four, implement segmentation via available software packages, e.g. ilastik. Generate figures from quantitative data. Individually write short reports on imaging data, emphasising statistical analysis.

Learning outcomes

By the end of the module, students should be able to:

• Utilise software packages for analysing imaging data (written report)
• Understand fundamental concepts underlying biological networks, including feedback, hysteresis, and robustness
• Comprehend concepts underlying networks and their representation
• Apply model fitting approaches to experimental data (written report)
• Apply statistical knowledge to estimate uncertainties in analysis (written report)
• Apply statistical techniques to a variety of biological data
• Generate and interpret graphs of biological data

Indicative reading list

There is no necessary book, though we recommend
An Introduction to Systems Biology by Uri Alon
Numbers in Biology by Rob Philips

Interdisciplinary

Utilises concepts from mathematics, physics and computer science to tackle problems related to biology.

Subject specific skills

Knowledge of key methods in network analysis, simple dynamic modelling, data handling and analysis. Knowledge of approaches to model linear and multidimensional gradients resulting in cellular decision making and robust pattern formation. Knowledge how to use tools to extract quantitative data from images and analyse the data.

Transferable skills

Students will be able to demonstrate integrated thinking across the Sciences. They will learn to work as a team to tackle a problem. They will learn how to structure a longer project over 10 weeks.