Background\ \ Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-speciﬁc networks involving a few interacting transcription factors (TFs) and all of their target genes. Results\ \ We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternative regulatory structures for each target gene. We use our methodology to identify targets of ﬁve TFs regulating Drosophila melanogaster mesoderm development. We ﬁnd that conﬁdent predicted links between TFs and targets are signiﬁcantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically signiﬁcantly outperforms existing alternatives. Conclusions\ \ Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations signiﬁcantly increases the accuracy.
Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations signiﬁcantly increases the accuracy.