Introductory description

MD2A2-30 - Integrated Science: Patterns and Populations

The module is a continuation of MSci Integrated Science 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 Science 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 given an indication of the sort of topics that may be covered. Actual sessions held may differ:

Week 1: Introduction to term-long data analysis project, e.g. segmentation of butterfly wing patterns and extraction of size, shape, colour and texture information

Block 1 (weeks 2 - 4): Genes and gene networks
Analysis of new generation sequencing data. How to determine binding sites of gene regulators. How to handle data, bias correction of data. How to extract differential expression and gene network information. Introduction to spatial transcriptomics

Block 2 (weeks 5 - 7): Developmental patterning
Feedback loops, Morphogen gradients and how to model them as one-dimensional patterns. How to set positions in a developing organism and how are gene networks built to support this. Robustness, i.e. how to interpret noisy expression patterns to discreet phenotypic outputs. How do you get the right number of extremities, on example of polydactyly. Large scale and multidimensional patterns - evolution and environmental constraints, e.g. environmental impact on butterfly spot size and how this is regulated.

Block 3 (weeks 8 -10): Ecology and evolution in populations, communities, and ecosystems
Using the sequencing and data analysis tools developed in weeks 2-4 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

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 spatial transcriptomics and their representation (Block 1)
• Generate data (Block 1), interpret (Block 2), and apply (Block 3) network interactions to study the ecology and evolution of biological systems
• Apply model fitting approaches to experimental data (Blocks 2 and 3)
• Apply statistical knowledge to estimate uncertainties in analysis (Blocks 1, 2 and 3)
• Apply concepts such as species interactions and feedbacks to ecological systems (Block 3)
• Use sequencing data to infer information about species evolution (Block 3)
• Present concisely, appropriately and effectively on topics discussed in class (Blocks 1, 2 and 3)
• Synthesise biological concepts related to network interaction. In particular, students are expected to being together different elements of the course to show how the concepts are integrated (Blocks 1, 2 and 3)
• Understand how to use the literature to deepen understanding (Blocks 1, 2 and 3)
• Understand fundamental concepts underlying biological networks, including feedback, hysteresis, and robustness
• Apply statistical techniques to a variety of biological data (Block 1: sequencing; Block 2: dynamic; Block 3: applications to ecology and evolution)
• Evaluate biological data using different techniques and able to critique the appropriateness of each methodology

Interdisciplinary

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

Subject specific skills

Knowledge of key methods in bioinformatics, data handling and analysis.
Knowledge of approaches to model linear and multidimensional gradients resulting in robust pattern formation.
Knowledge of how genetic variance impacts the spread of infectious disease within same species and different species.
Knowledge how to develop analysis tools to extract quantitative data from images and nucleotide reads of biological samples.

Transferable skills

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