Latent variable models of transcriptional regulation
Seminar Room 1, Newton Institute
The expression of genes as messenger RNA (mRNA) in the cell is regulated by the activity of transcription factor proteins. The measurement of mRNA concentration for essentially all genes can be routinely carried out using high-throughput experimental techniques such as microarrays. It is much less straightforward to measure the concentration of activated transcription factor proteins. Latent variable models have therefore been developed which treat transcription factors as unobserved chemical species who's active concentration and effect can be inferred indirectly from the expression levels of their target genes. We are developing two classes of latent variable models. For small sub-systems, e.g. genes controlled by a single transcription factor, we model the process of transcription using ordinary differential equations with the transcription factor's concentration modeled using a Gaussian process prior distribution over functions. For larger systems, with hundreds of transcription factors controlling thousands of genes, we use simple discrete-time or non-temporal linear models. Bayesian methods provide a natural means for inference of transcription factor concentrations and other model parameters of interest.
Joint work with Neil Lawrence.
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