MSc Computational Methods in Ecology and Evolution
Over the past 10-20 years, biology has become increasingly quantitative, and mathematical sciences have in turn been increasingly influenced by biology. It has been said that “mathematics is biology’s next microscope, only better” because mathematical, statistical, and computational sciences will continue to reveal unsuspected and entirely new worlds within biology as the microscope once did. It has also been said that “biology is mathematics’ next physics, only better” because biology will in turn continue to spur major new developments in computation, mathematics and statistics as physics has done in the past several hundred years.
By offering a course that specifically targets the quantitative skills that biologists are going to be increasingly reliant upon we aim to give our graduates a head start in the race to shape how mathematics and biology shape their combined development during the 21st century.
The MSc in Computational Methods in Ecology and Evolution provides students of life sciences with the quantitative skills they will need to thrive in the modern discipline of biology, and provides students from a more quantitative background with the biological insight they need to apply their technical skills. The course is unique in integrating important current research questions in biology with data from ecosystems down to cells and state-of-the-art quantitative methods. Graduates will be highly trained scientists prepared for employment in any of several settings.
Mathematical topics in the taught component of the course include the applied concepts of calculus, linear algebra, probability theory, and dynamical systems. In terms of statistical methods, the fundamental concepts of probability theory, distributions, likelihoods, linear and nonlinear regressions, bootstrapping, and computational statistics are explored in depth.
Students also study a number of current biological topics and case studies including:
- The evolution of genetic and ecological systems – population models dealing with evolution typically either focus on ecological interactions at the expense of genetic detail or consider detailed genetics while assuming constant population size. In this module, we present the two disparate approaches and discuss modern ways of bringing them together.
- A quantitative approach to co-evolutionary host-pathogen systems, introduces students to modelling genetic mutations, numerical diversity measures, mass action laws and chemostat models of microbial growth and evolution.
- Models of cooperation – classical modelling approaches to studying cooperation are explored in order to demonstrate that these, while successful at capturing general trends, cannot be used to make quantitative predictions.
- Confronting mechanistic biological models with data – global changes increase the urgency of transforming ecology into a predictive discipline so that we can develop a more rational basis for mitigation policy. Myriad new and existing methods of fitting competing, nonlinear and often complex models with available data are finding increased application in population ecology and epidemiology. We introduce these methods and apply them to models and data from several systems to draw concrete biological conclusions.
A research project lasting about six months is a compulsory component of the course. The project can be supervised by staff members from the Departments of Life Sciences and/or Mathematics, or at a number of outside institutes with which we have collaborative links (including The Royal Botanic Gardens, Kew, the Centre for the Environment, Fisheries and Aquaculture Science, and the Institute of Zoology).
Students who graduate form this MSc will be highly trained scientists prepared for employment in any of several settings, including as PhD students in universities and institutes worldwide; in the research departments of multinational industries concerned with the environment (e.g., pharmaceuticals, biotechnology); in conservation, management and agricultural agencies; and in local and national governments.
The minimum expected requirement is an Upper Second Class Honours degree in a biological, ecological, or life sciences-based subject from a UK academic institution, or an equivalent overseas qualification, and an A level in mathematics or equivalent.
Candidates with different backgrounds (e.g. Geography) or qualifications below the minimum level of the College academic regulations, i.e. 2:1, may be given the opportunity to impress at the interview stage, and further consideration will be given for admission.
Candidates with mathematical, statistical or computational backgrounds will also be considered if they show evidence of an abiding interest in biology.
Imperial College London
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