CS904 Computational Biology
CS904-15 Computational Biology
Introductory description
The module will cover topics on the acquisition of and analysis of large-scale data generated in biomedical sciences, particularly DNA/RNA sequences, live cell microscopy and multi-gigapixel pathology images. Students will be introduced to how these data are acquired, modern machine learning methods to process the data, and computational modelling approaches to help us better understand the complex phenomena underpinning biological processes. The module will be taught following an "algorithmic approach," demonstrating that addressing problems in computational biology requires a diverse range of theoretical concepts and algorithms, making it an exciting and rapidly evolving field for computer scientists. The students will learn using deep learning models for analysis of multi-gigapixel images. Therefore, basic understanding of image processing concepts will be required.
Module aims
The module is designed to develop student research skills in the broad area of computational biology.
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.
- Molecular biology fundamentals;
- Sequence alignments;
- Phylogenetic Trees;
- Dimensionality reduction and clustering;
- Live cell microscopy fundamentals;
- Image based modelling of single cell dynamics;
- Overview of mathematical modelling techniques and their application: Ordinary differential equations, stochastic differential equations, Monte Carlo methods
- Introduction to tissue imaging and computational pathology;
- Whole-slide image (WSI) handling
- WSI processing using machine learning and deep learning algorithms;
- Recognising various kinds of cells in cancerous WSIs;
- Advanced research topics in computational biology;
Learning outcomes
By the end of the module, students should be able to:
- Have an understanding of how tissue slides are imaged with high throughput
- Have a basic grasp of fundamental molecular biology concepts as relevant to this module
- Understand some basic and commonly used algorithms in bioinformatics
- Know algorithms to compute sequence alignments and how these are applied in current research
- Understand basic problems in the processing and analysis of tissue images and some standard solutions to those
- Be able to apply image analysis and machine learning to real-world computational pathology problems
- Understand principles of fluorescence microscopy and image-based modelling of single cell dynamics
- Know how to apply different mathematical techniques for modelling cellular dynamics
Indicative reading list
Zvelebil, M., and Baum, J.O., Understanding Bioinformatics. Garland Science, 2008;
Kremling, A., Systems Biology. CRC Press, 2014;
Pantanowitz, L., and Parwani, A., Digital Pathology. ASCP, 2017;
Alberts, B., et al., Essential cell biology: an introduction to the molecular biology of the cell (5/e). Garland, 2008.
Research element
Students need to develop and implement algorithms to address questions typically asked in current research projects. For example: Perform sequence analysis of homologous genes and construct phylogenetic trees, generate simulation data of gene regulatory networks and perform dimensionality reduction and clustering, develop models for cellular dynamics, analyze digital pathology images taken from real-world data.
Interdisciplinary
The module will cover a broad range of techniques used in biology, mathematics, and computer science.
Subject specific skills
By the end of the module, students will have acquired skills in:
- Implementing fundamental bioinformatics algorithms for computing alignments and phylogenetic trees
- Formulating and solving mathematical models of gene regulatory networks
- Applying methods of dimensionality reduction and clustering
- Formulating mathematical models of cellular dynamics
- Handling and processing of whole-slide images (WSI) data
- Recognising various kinds of cells in cancerous WSIs
- Recognising various kinds of regions in WSIs
- Performing survival analysis using features collated from WSIs
Transferable skills
Technical - Technological competence and staying current with knowledge
Communication - Verbal, listening, writing, technical communication skills, using different medium for communicating
Critical Thinking - Problem-solving, analysis of possible solutions etc
Creativity - Ability to harnass creative ideas and turn them into tangible and strategic products/solutions
Study time
Type | Required |
---|---|
Lectures | 20 sessions of 1 hour (13%) |
Supervised practical classes | 10 sessions of 1 hour (7%) |
Private study | 120 hours (80%) |
Total | 150 hours |
Private study description
Background reading of research papers, working through additional examples and improving coding skills, revision.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group C5
Weighting | Study time | Eligible for self-certification | |
---|---|---|---|
Assignment 1 | 16% | Yes (extension) | |
This assignment covers the first section of the module, Bioinformatics. |
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Assignment 2 | 17% | Yes (extension) | |
This assignment covers the first section of the module, Mathematical Modelling of Cellular Dynamics. |
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Assignment 3 | 17% | Yes (extension) | |
This assignment covers the first section of the module, Computational Pathology. |
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In-person Examination | 50% | No | |
CS904 examination
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Assessment group R2
Weighting | Study time | Eligible for self-certification | |
---|---|---|---|
In-person Examination - Resit | 100% | No | |
CS904 resit examination. Standard calculator allowed.
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Feedback on assessment
Written comments on coursework
Courses
This module is Core optional for:
- Year 1 of TBSA-C1P9 Postgraduate Taught Systems Biology
This module is Optional for:
-
TCSA-G5PD Postgraduate Taught Computer Science
- Year 1 of G5PD Computer Science
- Year 1 of G5PD Computer Science
- Year 1 of TMAA-G1PF Postgraduate Taught Mathematics of Systems
- Year 1 of TBSA-C1P9 Postgraduate Taught Systems Biology
This module is Option list A for:
- Year 1 of RMAA-G1PG Postgraduate Research Mathematics of Systems
- Year 1 of TMAA-G1PF Postgraduate Taught Mathematics of Systems
This module is Option list B for:
- Year 5 of UCSA-G504 MEng Computer Science (with intercalated year)
- Year 4 of UCSA-G503 Undergraduate Computer Science MEng
Further Information
Term 2
15 CATS
Module Organisers:
Till Bretschneider
Amina Asif
Shan Raza