Gaussian Process Model for Inferring the Regulatory Activity of Transcription Factor Proteins

Inferring the concentration of transcription factors' proteins from the expression levels of target genes is a very active area of research in computational biology. Usually, the dynamics of the gene expression levels are modelled using differential equations where the transcription factor protein concentrations are treated as parameters, subsequently estimated using MCMC. We show how this inference problem can be solved more elegantly by placing a GP prior over the latent functions, obtaining comparable results to the standard MCMC approach in a fraction of the time.