Title: Translating ceRNA susceptibilities into correlation functions,
Abstract: Competition to bind microRNAs induces an effective positive crosstalk between
their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While
such an effect is known to play a significant role in specific conditions,
estimating its strength from data and, experimentally, in physiological
conditions appears to be far from simple. Here we show that the susceptibility
of ceRNAs to different types of perturbations affecting their competitors (and
hence their tendency to crosstalk) can be encoded in quantities as intuitive
and as simple to measure as correlation functions. We confirm this scenario by
extensive numerical simulations and validate it by re-analyzing PTEN's
crosstalk pattern from TCGA breast cancer dataset. These results clarify the
links between different quantities used to estimate the intensity of ceRNA
crosstalk and provide new keys to analyze transcriptional datasets and
effectively probe ceRNA networks in silico.