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This subject introduces mathematical and computational modelling, simulation and analysis of biological systems. The emphasis is on developing models, with examples, using MATLAB.
Modelling biochemical reactions. Law of mass action. Enzymes and regulation of enzyme reactions. Thermodynamics of reversible biochemical reactions. Cellular homeostasis. Application of ordinary differential equations to these problems.
Modelling large reaction networks. Flux balance analysis and constraint-based methods. Genome-scale models. Regulation of gene expression. Gene regulatory networks in systems and synthetic biology. Network inference and statistical modelling of –omic data. Knowledge-based modelling in systems biology.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILOs)
Having completed this unit the student should be able to:
1 - describe the role for mathematical and computational modelling of biological systems;
2 - use the law of mass action to develop ODE models for biochemical reactions;
3 - develop and analyse models for enzyme catalysed reactions in cellular bioengineering and synthetic biology;
4 - develop and analyse ODE and PDE models in molecular and cellular physiology;
5 - describe the premise of systems biology;
6 - develop and analyse large-scale network models for biosystems and synthetic biology;
7- describe the role of knowledge-based modelling in systems biology;
8 - describe the measurement technologies and sources of data underlying systems biology, data repositories and modelling approaches.
On completion of this subject students should have developed the following generic skills:
- Ability to apply knowledge of science and engineering fundamentals.
- Ability to communicate effectively, with the engineering team and with the community at large.
- Capacity for lifelong learning and professional development.
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship.
Last updated: 6 December 2019