Award details

MRes in Computational Biology

Reference	BB/H020616/1
Principal Investigator / Supervisor	- Emma Rand
Co-Investigators / Co-Supervisors	Professor Fred Anston, Dr David Ashford, Dr Peter Ashton, Dr Naveed Aziz, Mr Shane Booth, Dr Adrian Bors, Professor Pierre Broun, Professor Neil Bruce, Professor Andrzej Brzozowski, Ms Louise Byass, Dr Leo Caves, Professor James Chong, Mr Edward Clark, Professor Mark Coles, Professor Matthew Collins, Professor Kevin Cowtan, Dr James Cussens, Dr Christopher Elliott, Dr Simon Hickinbotham, Professor Jane Hill, Professor Roderick Eliot Hubbard, Dr Harry Isaacs, Dr Daniel Kudenko, Professor Ottoline Leyser, Professor Peter Lillford, Professor Frans Maathuis, Professor Simon McQueen-Mason, Dr Garib Murshudov, Dr Simon O'Keefe, Professor Steven Penfield, Dr John Pillmoor, Dr Jonathan Pitchford, Dr Kelly Redeker, Dr Seishi Shimizu, Ms Janice Simpson, Professor Deborah Smith, Professor Jennifer Southgate, Professor John Sparrow, Professor Susan Stepney, Professor Gavin Thomas, Dr Jerry Thomas, Professor Jon Timmis, Professor Reidun Twarock, Professor Daniel Ungar, Dr Marjan van der Woude, Dr Richard Waites, Professor Julie Wilson, Professor Peter Young
Institution	University of York
Department	Biology
Funding type	Skills
Value (£)	223,204
Status	Completed
Type	Training Grants
Start date	01/10/2010
End date	30/09/2013
Duration	36 months

Abstract

unavailable

Summary

The Masters in Research (MRes) in Computational Biology programme trains graduates to meet the computational research demands of modern interdisciplinary bioscience (e.g. postgenomic, systems and synthetic biology) in universities, research institutes and industry. Delivered by staff from across the Departments of Biology, Chemistry and Computer Science, the course provides interdisciplinary research-led training in data analysis, informatics and the organisation and dynamics of complex biosystems. The programme builds on over 30 years of experience in training mathematically, statistically and computationally literate bioscientists. From the long running MSc in Biological Computation to the Masters of Research programmes in Bioinformatics, Maths in the Living Environment and Computational Biology we have continued to develop and evolve programmes to meet the current, and future, research demands of interdisciplinary bioscience. Our focus on core training in the essentials of data analysis, statistics, computational methods and modelling and the provision of opportunities to practise such methods in a research context equip graduates to meet new challenges emerging in biological research. The programme is principally aimed at graduates of the biological and molecular sciences but also accepts graduates of computer science, mathematics and statistics who can demonstrate an interest in the biosciences. SPECIFIC AIMS OF THE PROGRAMME: * to provide knowledge of the concepts and methods underpinning research in bioinformatics and computational biology; * to provide training in bioinformatics research skills, principally in the areas of: - sequence to structure to function, and evolutionary relationships in biomolecules - analysis of large biological data sets - modelling and simulation of biological systems; * to provide training in skills that are widely transferable, such as mathematical and programming skills, personal effectiveness, team working, communication and technology transfer; * to apply and develop these skills through three research projects including an external placement in academia, research institutes or industry worldwide. PROGRAMME STRUCTURE (51 WEEKS) Also see attached 'CBoutline2010.doc' * Autumn term 11 weeks ( -1 to 10) The focus is on providing teaching in the more fundamental areas with the following modules: Sequence, Structure & Genomics; Data Analysis I - Concepts and Skills; Introduction to Programming (Python); and Transferable skills. * Spring term 15 weeks (1 to 15) Taught modules advance the knowledge and skills acquired during the Autumn term and comprise: Data Analysis II - Applied Biological Data Analysis; Complex Dynamical Biosystems; Biocomputing and Web Applications; and Introduction to Machine Learning. There is a greater emphasis on research work during this term with a 12-13 week individual project. * Summer term 18 weeks (1 to 18) This term is devoted almost entirely to a 17-18 week research project normally carried out on placement in industry, a research institute or academic institute.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	Training Grant - Masters Training Account

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