#31 · Systems biology

Mapping post-transcriptional gene expression models onto promoter-based frameworks

The two preprints share a single technical strategy: a partitioned Poisson arrival construction that recasts stochastic post-transcriptional regulation models as promoter-based models, so that analytical results from promoter-based systems carry over to the post-transcriptional setting. One preprint uses this mapping to obtain exact protein-level moments; the other derives large deviation rate functions for the same model class. What is new is the reduction itself, which delivers both moment and large-deviation outputs from one framework. The published study is narrower in scope. Analyzing 1035 E. coli expression samples, it finds that binding-site regulons overlap little, while 877 dominantly regulated genes aid transcriptional network inference. The common thread is inference of regulatory structure from expression statistics, though the published work contributes a dataset-scale view of regulon structure.