BioNEt Event

Monday-Tuesday 2-3 April 2007 , Room 2.21, Research Beehive, Old Library Building, Newcastle University. Attendance, refreshments and lunch are free of charge. Register for event by emailing Natasha Taylor
There will be an opportunity to display posters and time for a small number of contributed oral presentations. Click here for further information.

Statistical Bioinformatics & Stochastic Systems Biology
Programme

Day 1: Monday 2 April

12.30 Lunch and registration

13.20 Welcome
Prof Trevor Page, Pro-Vice-Chancellor, Newcastle University

Chair: Prof Richard Boys, Mathematics & Statistics, Newcastle University
13.30 Stochasticity in biological systems: signals in the 'noise'
Prof Tom Kirkwood, Institute for Ageing & Health, Newcastle University
14.15 Probabilistic modelling of data on undirected graphs
Prof Lorenz Wernisch, Crystallography, Birkbeck College, University of London
15.00 Bayesian partitioning of sequence data and identification of regulatory regions
Yussanne Ma, Centre for Bioinformatics, Imperial College, London

15.30 Tea and coffee

Chair: Dr Malcolm Farrow, Mathematics & Statistics, Newcastle University
16.00 Colouring and breaking sticks, pairwise coincidence losses and clustering expression profiles
Prof Peter Green, Mathematics, University of Bristol
16.45 Assessing the exceptionality of network motifs
Prof Stéphane Robin, AgroParisTech / INRA, Paris, France
17.30 Mass spectrometry feature extraction for expression proteomic applications using a levy random fields model
Dr Leanna House, Mathematical Sciences, Durham University

18.00 Poster Session

19.00 Close

Day 2: Tuesday 3 April

Chair: Prof Richard Boys, Mathematics & Statistics, Newcastle University
09.30 Objective Bayesian Hidden Markov models for detecting regions of copy number variation in the human genome using SNP genotyping data
Dr Chris Holmes, Oxford Centre for Gene Function, University of Oxford
10.15 The optimal discovery procedure and bayesian decision rules
Prof Peter Müller, Biostatistics, University of Texas, USA

11.00 Coffee

Chair: Prof Heather Cordell, Institute of Human Genetics, Newcastle University
11.30 Statistical inferences from noisy and incomplete protein interaction network data
Dr Michael Stumpf, Centre for Bioinformatics, Imperial College, London
12.15 Modelling transcription activation using microarray data
Prof Ernst Wit, Medical Statistics Unit, Lancaster University

13.00 Lunch

Chair: Dr Peter Avery, Mathematics & Statistics, Newcastle University
14.00 Covariate-modulated false discovery rates
Dr Egil Ferkingstad, Biostatistics, Oslo University, Norway
14.30 Bayesian inference for systems biological models via a diffusion approximation
Dr Andrew Golightly, Mathematics & Statistics, Newcastle University

15.00 Tea and coffee

Chair: Dr Peter Avery, Mathematics & Statistics, Newcastle University
15.30 Title to be confirmed
Dr Wally Gilks, Statistics, University of Leeds

16.15 Close

Supported by CELS Ltd, European Regional Development Fund and One NorthEast

Abstract

In bioinformatics, biology, statistics and computer science meet. Bioinformatics deals with the handling and interpretation of data which arise in biology, especially in modern molecular and post-genomic biology. Examples include problems connected with DNA sequences, protein structure or microarray experiments. Statistical bioinformatics deals particularly with questions of modelling and inference in the extraction of useful information from such data.

Stochastic systems biology is concerned with modelling processes which take place at the molecular level within cells. In more traditional chemical kinetic modelling, the numbers of molecules involved are usually considered to be so large that the randomness of individual interactions of molecules can be ignored and a deterministic model is satisfactory. However, in the case of, for example, protein expression and genetic regulatory networks within cells, this simplification is no longer appropriate and models involving random, i.e. stochastic, processes are needed to give an adequate description of the behaviour. Given the limitations in the degree to which these processes can be observed experimentally, this leads to difficult problems of inference when we attempt to learn about the values of model parameters or compare alternative models.

This meeting will bring together leading researchers working on the development of new methods in statistical bioinformatics and stochastic systems biology. It will be of interest to statisticians and computer scientists working in this field and also to biologists and others who are interested in the latest developments in inferential and modelling methods for biological data. The emphasis will be on developments in statistical methodology but this is, by its very nature, an interdisciplinary area which depends on collaboration between those with statistical or computational expertise and those with biological expertise. Scientists who would not regard themselves as primarily statisticians but who have an interest in the development of appropriate statistical methodology will be welcome at the meeting.

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