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MSc Mathematical Modelling in Biology


Course Description

Course aims
This program aims to provide a comprehensive perspective of existing modelling approaches and contemporary methods of data analysis used in biology and ecology.

The focus will be on design, analysis and implementation of mathematical models for problems arising in life sciences, with special emphasis on ecological applications.

By the end of the course, you will be able to formulate problems in mathematical terms, build relevant mathematical models of biological phenomena, apply an array of analytical tools and/or select and develop an appropriate numerical method, write a computer program for numerical simulations, and present and interpret the results for a potential client from the agriculture and nature conservation sector.

Course modules
Autumn term:
Scientific Computing
Data Mining
Applied Dynamical Systems
Patterns and Processes in Ecosystems

Spring term:
Topics in Mathematical Biology
Computational Methods for Partial Differential Equations

Plus two option modules from the following:
• Introductory Mathematical Neuroscience
• Research Presentation
• Algorithms for Bioinformatics
• Java and Databases for Bioinformatics
Option modules are subject to availability in each particular year. Course titles and content are subject to change.

After term two, you will be given a choice of research projects. A project is undertaken full-time, leading to an oral presentation and the submission of a dissertation by the middle of September. The projects are supervised by a member of the Department of Mathematics either alone or jointly with a member of the School of Bioscience or from a relevant industrial partner.

Teaching and assessments methods
The course will be taught by lectures, seminars, problem classes and workshops. Each module will be assessed by a mixture of continuous assessments and examinations.

Visit the MSc Mathematical Modelling in Biology page on the University of Leicester website for more details!

Entry Requirements

Normally a good second class degree in mathematics, engineering, coNormally a good second class degree in Mathematics, Engineering, Computer Science, Physics or its equivalents.

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