Develop predictive theoretical frameworks integrating regulatory links and gene expression data

Develop theoretical frameworks for gene regulatory networks that explicitly incorporate publicly available datasets of regulatory interactions (such as TRRUST v2) and single-cell gene expression measurements in young and old organisms (such as Tabula Muris Senis), ensuring that the frameworks can account for these data and generate quantitative predictions for future experiments.

Background

The paper studies information transmission in gene regulatory networks and how it changes with aging, noting that comprehensive data exist on regulatory links and on gene expression in young and old organisms. Despite the availability of these datasets, a gap remains in connecting them within a principled theoretical framework that can make actionable predictions for experiments.

Motivated by this need, the authors introduce a minimal, analytically tractable model that uses binarized single-cell RNA-seq data and curated regulatory interactions to infer parameters without fitting and to predict the effects of gene knock-ins on network information. The explicit open problem highlights the broader need for rigorous frameworks that can integrate such data to guide experimental design and validation.