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.

This subject will introduce current topics in computational biology, focusing on case studies in a number of different biological areas, and applying a range of different mathematical and computational data handling approaches to solve or interrogate biological problems. Each topic will be developed through a series of lectures introducing the biological topic (relying on a fundamental knowledge of the molecular basis of life gained in second year level genetics and biochemistry subjects), the types and sources of biological data, and the relevant computational approaches, based around case studies. A series of assignments in each of these topic areas, supported by tutorial classes, will illustrate the computational methodologies as they are applied to specific biological data.

Indicative biological topics include applications of computational biology in:

• Phylogenetics, population genetics and evolution
• Ecological and environmental modeling (including geospatial and environmental decision making)
• Bio-imaging and cell tracking in cell biology
• Pathogenesis and immunology
• Structural biology
• Metabolic engineering and biotechnology

This subject introduces the biology and technology underlying modern genomics data, features of the resulting data types including the frequency and patterns of error and missingness, and the statistical methods used to analyse them. It will include hands-on data analysis using R software. The material covered will evolve as genomics technology and practice change, and will span the following four areas: introduction to genomics technology and the resulting data, population genetics, association analysis including tests of association and major sources of confounding, heritability and prediction both in human genetics and for animal and plant breeding, and analysis of expression quantitative trait loci.