# MS202-15 Cellular Decision Making

##### 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

The module will focus primarily on mathematical methods to analyse data, and model interactions and spatial patterns. The students will be equipped to extract quantitative information from images, derive network information and mathematical models to describe multidimensional patterns and complex multi-species interactions. The module provides an important view on understanding biological systems that is quite distinct from most other approaches covered in UG modules in SLS and WMS.

##### 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.

## Teaching split

Provider | Weighting |
---|---|

Warwick Medical School | 80% |

Life Sciences | 20% |

## Study time

Type | Required |
---|---|

Lectures | 5 sessions of 1 hour (3%) |

Project supervision | 5 sessions of 1 hour (3%) |

Practical classes | 10 sessions of 2 hours (13%) |

Private study | 92 hours (61%) |

Assessment | 28 hours (19%) |

Total | 150 hours |

##### Private study description

Reviewing lecture material prior to practical classes.

Reading literature, particularly Systems Biology by U Alon.

Going through practical class material afterwards to embed learning and to cover any questions missed.

Working on project in groups initially and then individually to prepare data for the report.

## Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

##### Assessment group A

Weighting | Study time | |
---|---|---|

Block 2 Problem Set | 10% | 3 hours |

Using plotting to understand different scenarios of gene behaviour |
||

Block 3 Problem Set | 10% | 3 hours |

Detailed analysis of switching behaviour |
||

Block 4 Problem Set | 10% | 3 hours |

Exploring behaviour of an oscillating circuit |
||

Block 5 Problem Set | 10% | 3 hours |

Using non-dimensionalisation to understand general behaviour of mathematical models of biology |
||

Quantitative approaches to biological imaging data | 35% | 15 hours |

Students given imaging data, e.g. butterfly wing images |
||

Mathematical approaches to understanding biological networks | 25% | 1 hour |

The one-hour in-class test will take place at the end of the last Practical Class. It will consist of short maths problems related to understanding biological networks. |

##### Feedback on assessment

Students will receive back their marked Problem Sets (except no. 5) prior to the in-class test. Worked solutions will be provided where appropriate. The project will have 5 1-hour classes available where students can come for feedback on their progress.

##### Pre-requisites

Students without calculus knowledge should do precourse reading as prescribed by the lecturer

## Courses

This module is Core for:

- Year 2 of UMDA-CF10 Undergraduate Integrated Natural Sciences (MSci)