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具有时间延迟的基因调控网络中的极端事件。

Extreme events in gene regulatory networks with time-delays.

作者信息

Vinoth S, Kingston S Leo, Srinivasan Sabarathinam, Kumarasamy Suresh, Kapitaniak Tomasz

机构信息

Center for Nonlinear and Complex Networks, SRM Institute of Science and Technology, Ramapuram, Chennai, 600 089, India.

Center for Research, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India.

出版信息

Sci Rep. 2025 Apr 16;15(1):13064. doi: 10.1038/s41598-025-97268-w.

Abstract

This work explores distinct complex dynamics of simplified two nodes of coupled gene regulatory networks with multiple delays in two self-inhibitory and mutually activated genes. We have identified the emergence of extreme events within a specific range of system parameter values. A detailed analysis of the time delay-induced emergence of extreme events is illustrated using bifurcation analysis, two-parameter phase diagrams, return maps, temporal plots, and probability density functions. The reasons behind the advent of extreme events are discussed in detail, with possible analogies to simplified two nodes of gene regulatory networks. The occasional large-amplitude bursting originated in the system via interior crisis-induced intermittency, Pomeau-Manneville intermittency, and the breakdown of quasiperiodic intermittency routes. Additionally, we have used various recurrence quantification statistical measures, such as mean recurrence time, determinism, and recurrence time entropy, to describe the transition from periodic or chaotic to unforeseen large deviations. Our approach shows that the sudden surge of variance and mean recurrence time at the transition points can be used as a new metric to detect the critical transitions of distinct extreme bursting events. The comprehensive overview of the interaction between gene regulatory networks, with insights into the formation of unusual dynamics, is beneficial to grasping different neuronal diseases.

摘要

这项工作探索了具有多个延迟的两个自抑制且相互激活基因的简化双节点耦合基因调控网络的独特复杂动力学。我们已经确定在特定系统参数值范围内极端事件的出现。使用分岔分析、双参数相图、返回映射、时间图和概率密度函数说明了对时间延迟诱导的极端事件出现的详细分析。详细讨论了极端事件出现背后的原因,并与简化的双节点基因调控网络进行了可能的类比。偶尔出现的大幅爆发是通过内部危机诱导的间歇性、庞加莱 - 曼内维尔间歇性以及准周期间歇性路径的崩溃在系统中产生的。此外,我们使用了各种递归量化统计量,如平均递归时间、确定性和递归时间熵,来描述从周期性或混沌到不可预见的大偏差的转变。我们的方法表明,在转变点处方差和平均递归时间的突然激增可以用作检测不同极端爆发事件临界转变的新指标。对基因调控网络之间相互作用的全面概述以及对异常动力学形成的洞察,有助于理解不同的神经疾病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a2f/12003715/8f7f1f3ee82e/41598_2025_97268_Fig2_HTML.jpg

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