Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in <i>Synechococcus elongatus</i> PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions.

ABSTRACT: Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO₂-to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully

SUBMITTER: Johnson Z 

PROVIDER: S-EPMC11979508 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature