PGn1

PGn1, or Promoter-Gene Network 1, is a computational framework used in systems biology to model regulatory interactions between promoter regions and their target genes. It integrates promoter activity data, time-series gene expression, and transcription-factor motif information to infer causal links in transcriptional regulation. The architecture typically includes a promoter layer that aggregates promoter-driven signals, a regulator layer capturing transcription factors, and a gene layer representing target genes. Inference is performed with probabilistic graphical models, such as Bayesian networks or regularized regression, and yields a network of promoter-to-gene connections with confidence scores.

PGn1 emphasizes modular design: promoter modules define characteristic activity patterns; gene modules group co-regulated targets for improved robustness. The framework can incorporate prior knowledge from motif analyses, ChIP-seq data, and perturbation experiments, and can utilize time-resolved data to help resolve temporal order. Applications include reconstructing regulatory networks in yeast, plants, and mammalian cells, identifying key regulatory hubs, and benchmarking perturbation studies.

Advantages include interpretability and scalability; limitations involve sensitivity to data quality, difficulty separating direct versus indirect regulation, and challenges with post-transcriptional control. Open-source implementations and tutorials exist, enabling researchers to adapt PGn1 to organism- and condition-specific datasets. Overall, PGn1 provides a versatile approach for mapping promoter-gene relationships and exploring regulatory architecture.