Abstract
Cell-to-cell communication via different signalling pathways often results in population-level synchronization of certain gene expressions in noisy cellular environments organized. How exactly such synchronization is accomplished within a population level remains elusive. Here, we have developed a stochastic kinetic mathematical model by taking the Notch signalling pathway as an example to understand the nature of such kind of synchronization observed for Hes1 (Hairy and enhancer of split-1) gene expression. Our stochastic simulations revealed that the periodic pulsatile input to the intrinsic genetic Hes1-oscillator leads to population-level synchrony by reducing the variability of the parent oscillator. Additionally, we demonstrate how the degree of synchronization can be changed by controlling the network motifs that drive the oscillation of the parent oscillator. Our model further successfully identifies the conditions for maximum synchronization. This study indicates how cells within a population filter out fluctuations while living in a heterogeneous cellular environment.
Graphical abstract
A stochastic kinetic model is developed to investigate the population-level synchronization of oscillatory gene expression that occurs in a noisy cellular environment. The study unravels that pulsatile input reduces variability to orchestrate synchronization of the intrinsic oscillator and highlights how cells filter out fluctuations in heterogeneous cellular environments.
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Acknowledgments
Thanks are due to UGC (Ref. No: 19-06/2016(i) EU-V) and IRCC IIT Bombay for providing the fellowship to (AG). This work is supported by the funding agency SERB, India (Grant no. CRG/2019/002640 and Grant no. MTR/2020/000261).
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Dedicated to Prof. S.P. Bhattacharyya on the occasion of his 75th birthday.
Special Issue on Interplay of Structure and Dynamics in Reaction Pathways, Chemical Reactivity and Biological Systems
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Giri, A., Kar, S. Deciphering the impact of pulsatile input in the population-level synchrony of the Hes1 oscillators. J Chem Sci 135, 71 (2023). https://doi.org/10.1007/s12039-023-02177-y
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DOI: https://doi.org/10.1007/s12039-023-02177-y