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This subject introduces the concepts of mathematical and computational modelling of biological systems, and how they are applied to data in order to study the underlying drivers of observed behaviour. The subject emphasises the role of abstraction and simplification of biological systems and requires an understanding of the underlying biological mechanisms. Combined with an introduction to sampling-based methods for statistical inference, students will learn how to identify common patterns in the rich and diverse nature of biological phenomena and appreciate how the modelling process leads to new insight into biological phenomena.
- Modelling: Deterministic and stochastic population-level dynamic models; agent-based computational models; and geospatial statistical models will be introduced and studied. Indicative examples will be drawn from health (e.g. infectious diseases, cell tumour growth, developmental biology), ecology (e.g. predator-prey systems, sustainable harvesting, environmental decision making) and biotechnology (e.g. biochemical and metabolic models).
- Simulation: Sampling based methods (e.g Monte Carlo simulation, Approximate Bayesian Computation) for parameter estimation and hypothesis testing will be introduced, and their importance in modern computational biology discussed.
Intended learning outcomes
On completion of this subject, students should:
- Appreciate how abstraction and simplification of biological systems through modelling can provide new insight into biological phenomena
- Be able to distinguish between different approaches to modelling (deterministic, stochastic, agent-based, statistical) and critically evaluate the suitability of these alternative approaches for particular biological problems
- Be able to develop computer programs that implement and solve simple models of biological phenomena
- Be familiar with the concept of statistical simulation and its role in testing hypotheses and understanding model behaviour
- Use models and their application to data to formally evaluate biological hypotheses
Understand how to interpret and critique the biological modelling literature
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular
- modelling skills: the ability to abstract and generalise from observations of a complex system, providing an alternative perspective on the problem
- numerical and computer simulation skills: the ability to design simple computer programs to solve models and test hypotheses
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 29 October 2019