rhvdm–afastanduser-friendlyrpackageto-videolectures.ppt

Dynamic modelling of microarray data. Martino Barenco Institute of Child Health / UCL Goal: predict targets of a known transcription factor in a complex response using dynamic models and time course microarray data. HVDM: Hidden Variable Dynamic Modelling Gene expression model The p53 network Experimental setup Human T cells (MOLT4/p53 wild-type) submitted to 5Gy irradiation. mRNA harvested 2,4,6,8,10,12 hours after irradiation, and just before (0 hrs time point). Affymetrix microarrays (HG-U133) were then run. Experiment was run in triplicates. Results of training step: activity profile of p53 Screening Q: what are the other genes that are p53 activated? Putative p53 targets must both:a) Fit the model wellb) Have a sensitivity coefficient Sj0 Ingredients needed ODE integration 2) Model fitting Start with a “random” set of parameters: Compute a solution: Compare with data using a merit function: Vary p systematically until a minimum value for M(p) is reached. Fitting algorithms: Originally used simplex-based method (Nelder-Mead) (GB paper) Followed by a MCMC step to determine confidence intervals (GB paper) rHVDM (Bioconductor) uses Levenberg-Marquardt (gradient-based). By-product is the Hessian, which allows to compute confidence intervals. Difference between MCMC and LM confidence intervals. Importance of confidence intervals Biological data is inherently noisy. Don’t want to assume that measurement are exact. example: Genes with a flat profile would be a good fit to the equation (Sj=0) Essential to identify these situations to detect targets of the transcription factor Parameter count reduction / identifiability Confidence intervals importance II Acknowledgements Sonia Shah (Bloomsbury Centre for Bioinformatics) Dan Brewer (Institute of Cancer Research) Crispin Miller (Patterson Institute for Cancer Research) Daniela Tomescu (ICH) Mike Hubank (ICH) Robin Callard (ICH) Jaroslav Stark (CISBIC, Imperial College) * * Outline Principle + Results (Genome