Impact of basic network motifs on the collective response to perturbations

Bao, Xiaoge; Hu, Qitong; Ji, Peng; Lin, Wei; Kurths, Jürgen; Nagler, Jan

doi:10.1038/s41467-022-32913-w

Cited by 29 publications

(21 citation statements)

References 35 publications

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“…More studies need to be performed to address the real changing temporal characteristics of human mobility to better understand the propagation patterns. In addition, our study of disease propagation could also extend to subjects such as idea propagation [24-26], culture spreading [27], and signal propagation [28]. For example, signal propagation patterns on complex networks may be helpful in understanding the complex behavior of the contagion, especially how the social system would respond according to perturbation of the dynamics [28,29].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, our study of disease propagation could also extend to subjects such as idea propagation [24-26], culture spreading [27], and signal propagation [28]. For example, signal propagation patterns on complex networks may be helpful in understanding the complex behavior of the contagion, especially how the social system would respond according to perturbation of the dynamics [28,29]. Our work put forward the method to timing the earliest case, given real local resurgence data.…”

Section: Discussionmentioning

confidence: 99%

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Time tracing the earliest case of local pandemic resurgence

et al. 2023

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Origin identification of the earliest cases during the pandemic is crucial in containing the transmission of the disease. The high infectiousness of the disease during its incubation period (no symptom yet) and underlying human interaction pattern make it difficult to capture the entire line of the spread. The hidden spreading period is when the disease is silently spreading, for the “silent spreaders” showing no symptoms yet can transmit the infection. Being uncertain of the hidden spreading period would bring a severe challenge to the contact tracing mission. To find the possible hidden spreading period span, we utilized the SEITR (susceptible–exposed–infected–tested positive–recovered) model on networks where the relation between E state and T state can implicitly model the hidden spreading mechanism. We calibrated the model with real local resurgence epidemic data. Through our study, we found that the hidden spreading period span of the possible earliest case of local resurgence could vary according to the people interaction networks. Our modeling results showed the clustering and shortcuts that exist in the human interaction network significantly affect the results in finding the hidden spreading period span. Our study can be a guide for understanding the pandemic and for contact tracing the origin of local resurgence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Time tracing the earliest case of local pandemic resurgence

et al. 2023

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“…Stimulated by the external signals, the selfdynamics and interaction dynamics of p53 network will change accordingly, reflecting the adaptability of cells. Understanding the spatiotemporal signal propagation [46][47][48] in this network with perturbations ideas may bring some inspiration.…”

Section: Discussionmentioning

confidence: 99%

Resembling the bottleneck effect in p53 core network including the dephosphorylation of ATM by Wip1: A computational study

Wang

Lü

et al. 2022

Front. Phys.

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As one of the key proteins, wild-type p53 can inhibit the tumor development and regulate the cell fate. Thus, the study on p53 and its related kinetics has important physiological significance. Previous experiments have shown that wild-type p53-transcribed phosphatase one protein Wip1 can maintain the continuous oscillation of the p53 network through post-translational modification. However, the relevant details are still unclear. Based on our previous p53 network model, this paper focuses on the modification of Wip1 dephosphorylated ataxia telangiectasia mutant protein ATM. Firstly, the characteristics and mechanism of p53 network oscillation under different numbers of DNA double strand damage were clarified. Then, the influence of ATM dephosphorylation by Wip1 on network dynamics and its causes are investigated, including the regulation of network dynamics transition by the mutual antagonism between ATM dephosphorylation and autophosphorylation, as well as the precise regulation of oscillation by ATM-p53-Wip1 negative feedback loop. Finally, the cooperative process between the dephosphorylation of ATM and the degradation of Mdm2 in the nucleus was investigated. The above results show that Wip1 interacts with other components in p53 protein network to form a multiple coupled positive and negative feedback loop. And this complex structure provides great feasibility in maintaining stable oscillation. What’s more, for the state of oscillation, the bottleneck like effect will arise, especially under a certain coupled model with two or more competitive negative feedback loops. The above results may provide some theoretical basis for tumor inhibition by artificially regulating the dynamics of p53.

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“…The outbreak and spread of infectious diseases from one location to all over the world [1][2][3][4][5][6][7], the control of proteins or genes over the biochemical processes [8][9][10][11][12], and many other dynamic processes involve the propagation of perturbations from some nodes to the overall network [13], which indicates that the patterns of global propagation for small perturbations have a wide range of applications in the real world [14]. Specifically, studying the propagation time of dynamical systems can effectively monitor the speed of signal propagation, which is of great significance for research on network control and network stability [15][16][17][18][19][20][21]. However, due to the complexity and heterogeneity of network interactions, conventional analytical tools are often challenging to decouple global topology in real complex networks, and are difficult to apply to the field of signal propagation.…”

Section: Introductionmentioning

confidence: 99%

Information transfer pathways: signal propagation in complex global topologies

Hu,

Zhang

2024

Phys. Scr.

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In the real world, many dynamic behaviors can be explained by the propagation of perturbations, such as the transfer of chemical signals and the spread of infectious diseases. Previous researchers have achieved excellent results in approximating the global propagation time, revealing the mechanism of signal propagation through multiple paths. However, the known frameworks rely on the extension of physical concepts rather than mathematically rigorous derivations. As a result, they may not perfectly predict time or explain the underlying physical significance in certain specific cases. In this paper, we propose a novel method for decomposing network topology, focusing on two modules: the tree-like module and the path-module. Subsequently, we introduce a new framework for signal propagation analysis, which can be applied to estimate the propagation time for two fundamental global topology modules and provide a rigorous proof for the propagation time in global topology. Compared to previous work, our results are not only more concise, clearly defined, efficient, but also are more powerful in predicting propagation time which outperforms some known results in some cases, for example, biochemical dynamics. Additionally, the proposed framework is based on information transfer pathways, which can be also applied to other physical fields, such as network stability, network controlling and network resilience.

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Impact of basic network motifs on the collective response to perturbations

Cited by 29 publications

References 35 publications

Time tracing the earliest case of local pandemic resurgence

Time tracing the earliest case of local pandemic resurgence

Resembling the bottleneck effect in p53 core network including the dephosphorylation of ATM by Wip1: A computational study

Information transfer pathways: signal propagation in complex global topologies

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