Undergraduate Programme and Module Handbook 2026-2027
Module COMP3831: Bioinformatics & Natural Computing Algorithms
Department: Computer Science
COMP3831: Bioinformatics & Natural Computing Algorithms
| Type | Open | Level | 3 | Credits | 20 | Availability | Available in 2026/2027 | Module Cap | Location | Durham |
|---|
Prerequisites
- COMP2271 Data Science
- COMP2261 Artificial Intelligence
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce students to applications of Computer Science in Biology.
- To give students an understanding of how processes and phenomena that occur in the natural world can inspire the development of new computational algorithms and models relevant to modern-day computing.
- To equip students with a range of nature-inspired and metaheuristic techniques that can be widely applied in real-world problem solving.
Content
- Key topics in bioinformatics and algorithms that are used to solve them.
- Metaheuristic algorithms for data classification and the solution of optimization problems drawn from, e.g., immunocomputing, nature-inspired algorithms.
- Models and methodologies in programmable matter, e.g., DNA computing.
Learning Outcomes
Subject-specific Knowledge:
- On completion of the module, students will be able to demonstrate:
- An understanding of some key computational problems in Biology.
- An understanding of how systems and phenomena from the natural world inspire new computational algorithms
Subject-specific Skills:
- On completion of the module, students will be able to demonstrate:
- An ability to implement key algorithms within bioinformatics.
- An ability to identify what methods are applicable to given Biological data.
- An ability to implement appropriate natural computing algorithms and apply them to given data
Key Skills:
- On completion of the module, students will be able to demonstrate:
- An ability to abstract out a computational problem from a real-world one.
- An ability to appreciate the synergy between computer science and the natural world
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable the students to learn new material relevant to applications of Computer Science in Biology and natural computing algorithms and their implementations.
- Summative Assignment 1 assesses the application of bioinformatics methods and techniques learned and consists of a poster submission supported by code and data.
- Summative Assignment 2 assess the understanding of natural computing algorithms and their practical implementation and consists of a written assignment supported by code.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 20 | 1 per week | 1 hour | 20 | |
| Workshops | 20 | 1 per week | 1 hour | 20 | |
| Preparation and Reading | 160 | ||||
| Total | 200 |
Summative Assessment
| Component: Coursework | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Assignment | 50% | ||
| Assignment | 50% | ||
Formative Assessment:
Example formative exercises are given during the course.
■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.