Introductory description

This module focuses on how complex biological systems can be described and analysed using mathematical and computational approaches. For example, (1) new sequencing data is revealing insight into the connectivity in gene regulation; and (2) mathematical models underly key concepts in population and community ecology. Such information is critical as we look to understand the effects of the Anthropocene on species’ genetic diversity and extinction.
The module will be compulsory for second year Integrated Natural Sciences (INS) students. It is a continuation of MSci Integrated Natural Sciences Year 1 laboratory modules that focus on molecules, cells and organisms to further increase scale to population level. The programme extends the first year of the degree using more advanced methods and analysis, but retaining the Integrated Natural Sciences ethos, solving problems by drawing freely from the methods and mindsets of more than one discipline.

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.

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 (weeks 1 - 4): Genes and gene network analysis of high-throughput sequencing data. How to handle data, detect and correct biases, and assure data quality. How to extract differential expression and gene network information.

Block 2 (weeks 5 - 7): Ecology and evolution in populations, communities, and ecosystems. Using the sequencing and data analysis tools developed in Block 1 to characterise real-world communities. Investigate feedback(s) across scales, how species interactions scale-up to influence ecosystem properties and vice versa how ecosystems drive or constrain the evolution of species
The module ends with a field trip to collect data and perform statistical analysis.

Learning outcomes

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

• Utilise software packages for analysing sequencing data (Block 1)
• Comprehend concepts underlying transcriptomics data and their representation (Block 1)
• interpret and apply data to understand network interactions in ecology and evolution of biological systems (Blocks 1-2)
• Apply statistical knowledge to estimate uncertainties in analysis (Blocks 1-2)
• Apply concepts such as species interactions and feedback to ecological systems (Block 2)
• Present concisely, appropriately and effectively on topics discussed in class (Blocks 1-2)
• Synthesise biological concepts related to network interaction (Blocks 1-2)
• Understand how to use the literature to deepen understanding (Block 1-2)
• Evaluate biological data using different techniques and able to critique the appropriateness of each methodology

Indicative reading list

Michael Begon & Colin R. Townsend. Ecology: From Individuals to Ecosystems. 5th Ed.

Interdisciplinary

The students will apply computational and mathematical methods to understand and describe complex biological phenomena.

Subject specific skills

Knowledge of key methods in bioinformatics, data handling and analysis. Knowledge how to apply analysis tools to extract quantitative data from nucleotide reads of biological samples. Knowledge about how to interpret data in the context of ecological and evolutionary theory.

Transferable skills

Students will be able to demonstrate integrated thinking across the Sciences. Oral presentations on quantitative methods applied to biological problems.