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Neil Lawrence
School of Computer Science, University of Manchester, UK
Wednesday 13 June 2007, 16:00
Seminar Room B10 (Basement)
Alexandra House, 17 Queen Square, London, WC1N 3AR
Probabilistic Inference for Modelling of Transcription Factor Activity
Accurate modelling of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. In practice many of them are difficult to measure in vivo. For example, it is very hard to measure the active concentration levels of the transcription factor proteins that drive the process.
In this talk we will show how, by making use of structural information about the interaction network (e.g. arising form ChIP-chip data), transcription factor activities can estimated using probabilistic inference. We propose two different probabilistic models: a simple linear model with Kalman filter based dynamics for genome/transcriptome wide studies and a differential equation based Gaussian process model with a more physically realistic parameterisation for smaller interaction networks.
Related papers:
N. D. Lawrence, G. Sanguinetti and M. Rattray. (2007) "Modelling transcriptional regulation using Gaussian processes" in B. Schölkopf, J. C. Platt and T. Hofmann (eds) Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA.
G. Sanguinetti, N. D. Lawrence and M. Rattray. (2006) "Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities" in Bioinformatics 22 (22), pp 2275--2281
G. Sanguinetti, M. Rattray and N. D. Lawrence. (2006) "A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription" in Bioinformatics 22 (14), pp 1753--1759