Aims

By the end of this module students will have gained, at an introductory level, experience in, and a knowledge of (i) the basic concepts underpinning novel forms of computation that are inspired by Nature and use biological or 'natural' (i.e., non-silicon based) computing substrates. (ii) the applcation of computing in the design and engineering of biological systems. The module will prepare interested students for a dissertation project in the research area, and potential further study or early career in the field of bio-computing.

This module aims to provide a basic and wide-ranging overview of this field, while also being adequately rigorous in its treatment and grounded in real-world applications. The material covered is complementary to the stage 3 module (CSC3432 Biomedical Data Analytics).

Outline Of Syllabus

Students will be introduced to how biology, ecology, and chemistry have inspired novel computational paradigms.
Students will be introduced to the concept of probabilistic (random) algorithms as an approach to solve computationally hard problems
Students will be introduced to the principles of biological engineering and computational biodesign
Students will be introduced to the concepts of evolutionary algorithms, neural networks, molecular & DNA computing, and/or quantum computing
Students will be introduced to the synthetic biology design-build-test cycle and the role of engineering
Students will be introduced to the basic paradigms of biological engineering and the role of mathematical modelling in model-based design
Students will be introduced to the role of standards in computational biodesign

Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Rationale And Relationship

This module focuses on a very practical subject and hence an assessment based on coursework is the best option to evaluate the student’s knowledge. The coursework will assess the student's ability to apply the module's concepts in a practical setting and will be assessed as practical reports, which is a suitable methods for assessing the creation and application of biologically-inspired algorithms and computational bio-design tools.