Presentation Information
[POS-13]Dissecting the Network Architecture of Circadian Clock Model: Identifying Key Regulatory Mechanisms and Essential Interactions
*Shashank Kumar Singh1, Ashutosh Srivastava1 (1. Department of Biological Sciences and Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat, India (India))
Keywords:
Plant circadian rhythms,Computational modelling,Sensitivity analysis,Regulatory network
Circadian rhythms are self-sustained biological oscillations that regulate various physiological processes in plants, including growth, metabolism, and responses to environmental cues. These rhythms emerge from an intricate transcriptional and translational feedback network that integrates multiple entrainment signals, such as light and temperature. The plant circadian regulatory network consists of interconnected feedback loops involving transcription factors like CCA1, LHY, PRRs, TOC1, ELF3, ELF4, and RVE8. Computational modeling has been instrumental in dissecting this complex network, providing insights into the fundamental mechanisms governing rhythmicity. In this study, we perform a systematic analysis of a circadian clock model to identify key regulatory mechanisms and essential interactions. We used the Pay 2022 model as a reference and modified it to improve its predictive capability. Our analysis employs four approaches: (1) sensitivity analysis to examine how the period of major components varies with individual parameter changes, (2) network impact analysis to assess the influence of components and parameters on the entire system, (3) phase portrait analysis to visualize dynamic interactions between essential components, and (4) knockout analysis to determine essential parameters required for sustained rhythmicity. Our results highlight the most influential kinetic parameters controlling the circadian period, reveal hierarchical organization within the network, and identify critical interactions that sustain oscillations. These findings enhance our understanding of the circadian clock’s robustness and adaptability, paving the way for improved predictive models and experimental validation.