Statistical Modelling of Biological Networks
New experimental techniques in molecular biology make it possible to probe cellular processes in unprecedented detail. In particular, information on the activity profiles of genes is available from large scale microarray or reporter assay experiments. To infer detailed models for cellular processes poses formidable inference problems, if models beyond simple clustering or association graphs are envisaged.
I will discuss some ideas and examples for the inference of static as well as dynamic models for gene regulation using a Bayesian model selection framework. The fact that most data sets are high dimensional but comparatively small suggests exploiting the simplicity and flexibility of Gaussian processes for modelling nonlinear relationships in dynamical models.
Speaker Details
After his PhD on combinatorial algorithms in mathematics in 1994 and a two-year postdoc in Berlin, Germany, Lorenz Wernisch joined the European Bioinformatics Institute in Cambridge, where he worked on the computational analysis of protein structures. In 1999 he became lecturer in bioinformatics at Birkbeck College where he developed an interest in the statistical analysis of sequence and microarray data. Lorenz Wernisch is currently professor of computational biology. He and his group are working on topics such as microarray analysis, comparative genomics, and the application of mathematical modelling, statistical inference, and machine learning to gene regulation.
- Date:
- Speakers:
- Lorenz Wernisch
- Affiliation:
- Birkbeck College, University of London
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Jeff Running
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