Probabilistic Signal Network Models from Multiple Replicates of Sparse Time-Course Data

Abstract

has sparse data with the number of time points being less than the number of proteins. Usually, each replicate is modeled separately, however, here all the information in each of the replicates is used to make a composite inference about the signal network. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the network.