Identifying transcription-factor combinations to modulate circadian rhythms by leveraging virtual knockouts on transcription networks
Description
The mammalian circadian systems consist of indigenous, self-sustained 24-hours rhythm generators. They comprise many genes, molecules, and regulators. To decode their systematic controls, a robust computational approach, LogicTRN was employed. It integrates TF-occupancy and time-series gene-expression data as input. The model equations were constructed and solved to determine the transcriptional regulatory logics in the mouse transcriptome network. This hypothesizes to explore the underlying mechanisms of combinatorial transcriptional regulations for circadian rhythms in the mouse. We reconstructed the quantitative transcriptional-regulatory networks for circadian gene regulation at a dynamic scale. Transcriptional-simulations with virtually knocked-out TFs were performed to estimate their influence on networks. The potential TF/TF-combinations modulating the circadian rhythms were identified. Of them, CLOCK/CRY1 double knockout preserves the highest modulating capacity. Our quantitative framework offers a quick, robust, and physiologically relevant way to characterize the TF-combinations to modulate the circadian rhythms at a dynamic scale effectively.