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Technological advances in the life sciences are producing vast amounts of data describing organisms at all levels of organisation. The impact of this on Informatics and the Computational Sciences has been enormous: the new disciplines of computational biology and bioinformatics were born to organise and model these data, and are now some of the fastest growing and most exciting areas in computer science. In the proposed Summer School, we will concentrate on the statistical and machine learning approach to biological data modelling, particularly Bayesian Statistics. The ability to formally combine prior knowledge with experimental data and properly distinguish competing hypotheses based on the evidence present makes Bayesian Statistical Inference (SI) ideal for the type of problems found in experimental Biology. The school will consist of six 4 hour modules, each delivered by an expert of international standing over five days. The first two sessions will serve as an introduction to multi-variate and Bayesian statistics respectively with a leaning towards the tools required in Computational Biology. The remaining sessions will cover four of the main inference tasks in Computational Biology - network reconstruction, inference within models of biological processes, inference in phylogenetics and phenotype-genotype associations to explain genetic diseases.

A skeleton outline of the proposed session reads as follows:

• An introduction to multi-variate statistics
  Terry Speed - UC Berkeley
• An introduction to Bayesian Inference
  (to be arranged)
• Network reconstruction and modelling from data
  Dirk Husmeier - BIOSS
• Inference in mechanistic models of biological processes
  Manfred Opper - TU Berlin (to be confirmed)
• Statistical Inference in Phylogenetics
  Michael Stumpf - Imperial College London
• Genotype-phenotype associations for complex traits
  Chris Holmes - University of Oxford

Target audience

The primary target audience of the school are PhD students from non life-sciences background who are now working within Computational Biology, Bioinformatics and Systems Biology. Another important target audience are PhD students and postdocs working in quantitative biology, who may want to learn more about computational and statistical modelling approaches. Scotland has several world-leading centres of excellence in the area: to mention but a few, the Informatics Life Sciences Institute and the Centre for Systems Biology at Edinburgh (University of Edinburgh and Heriot-Watt University), the department of Computing Science, doctoral training centre in the Sir HenryWellcome functional genomics utility and the Beatson Institute for Cancer Research at the University of Glasgow, or the Systems Biology centre at the University of Aberdeen.

Location and dates

The school will be held in Edinburgh from June, 14th to June, 18th. The workshop will be hosted at the National eScience Centre (NeSC), Edinburgh with accommodation provided in the nearby Pollock Halls.

Organisers

• Guido Sanguinetti
  Sanguinetti has recently been appointed as a SICSA lecturer in Machine Learning in the School of Informatics, University of Edinburgh. He’s previously been a lecturer in Systems Biology and Machine Learning in the University of Sheffield (2006-09). He has a strong interest in the application of Machine Learning approaches to problems in systems biology, with a track record of approx 20 papers in leading international journals. He has co-organised (with N.D. Lawrence, M. Rattray and M. Girolami) a Thematic Programme on learning in computational and systems biology within the PASCAL Network of Excellence (2007), and has organised the PMNP workshop in 2007, as well as the fourth IAPR international conference on Pattern Recognition in Bioinformatics (PRIB) in 2009
• Simon Rogers
  Rogers is a recently appointed SICSA lecturer in the Inference Group, Department of Computing Science, University of Glasgow. He has an active research profile in both Statistical Inference and Computational Biology with over 15 papers in leading international journals. Whilst a PDRA in the Inference Group at the University of Glasgow he was instrumental in the organisation of three successful workshops - Practical Inference Methods for Mechanistic Systems Modelling (PIMMS) 2007, Mathematical and Statistical Aspects of Molecular Biology (MASAMB) 2008, and Learning in Computational and Systems Biology (LICSB) 2008.

For more information, email sicb@dcs.gla.ac.uk