The interdependent network of gene regulation and metabolism is robust where it needs to be

Klosik, David F.; Grimbs, Anne; Bornholdt, Stefan; Hütt, Marc‐Thorsten

doi:10.1038/s41467-017-00587-4

Cited by 58 publications

(46 citation statements)

References 72 publications

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“…When α = 1 or M = 1, the second-order percolation transition point p II c = 1 G ′ 1 (1) , which is coincident with the result for the ordinary percolation in a single-layer network.…”

Section: Theorysupporting

confidence: 82%

Depth Penetration and Scope Extension of Failures in the Cascading of Multilayer Networks

2020

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Real-world complex systems always interact with each other, which causes these systems to collapse in an avalanche or cascading manner in the case of random failures or malicious attacks. The robustness of multilayer networks has attracted great interest, where the modeling and theoretical studies of which always rely on the concept of multilayer networks and percolation methods. A straightforward and tacit assumption is that the interdependence across network layers is strong, which means that a node will fail entirely with the removal of all links if one of its interdependent neighbours fails. However, this oversimplification cannot describe the general form of interactions across the network layers in a real-world multilayer system. In this paper, we reveal the nature of the avalanche disintegration of general multilayer networks with arbitrary interdependency strength across network layers. Specifically, we identify that the avalanche process of the whole system can essentially be decomposed into two microscopic cascading dynamics in terms of the propagation direction of the failures: depth penetration and scope extension. In the process of depth penetration, the failures propagate from layer to layer, where the greater the number of failed nodes is, the greater the destructive power that will emerge in an interdependency group. In the process of scope extension, failures propagate with the removal of connections in each network layer. Under the synergy of the two processes, we find that the percolation transition of the system can be discontinuous or continuous with changes in the interdependency strength across network layers, which means that sudden system-wide collapse can be avoided by controlling the interdependency strength across network layers. Our work not only reveals the microscopic mechanism of global collapse in multilayer infrastructure systems but also provides stimulating ideas on intervention programs and approaches for cascade failures.

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“…When α = 1 or M = 1, the second-order percolation transition point p II c = 1 G ′ 1 (1) , which is coincident with the result for the ordinary percolation in a single-layer network.…”

Section: Theorysupporting

confidence: 82%

Depth Penetration and Scope Extension of Failures in the Cascading of Multilayer Networks

2020

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“…Spectral analysis may help understanding the functional consequences of the alternative paths created by these multilayered networks 56,57 .…”

Section: Discussionmentioning

confidence: 99%

Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects

Guimarães

Pires

Cantor

et al. 2018

Preprint

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The functionality of distinct types of protein networks depends on the patterns of proteinprotein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

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“…To this aim, multiples sources of omics data encode different layers, representing a biological system as a network of networks. This integrated perspective allows for more predictive performances [17,18,19] and has been shown to better characterize the evolution of complex diseases such as cancer [20], as well as to better understand the response to genetic and metabolic perturbations in complex organisms like E. coli [21].…”

Section: Multi-omicsmentioning

confidence: 99%

Multilayer network modeling of integrated biological systems

Domenico

2018

Physics of Life Reviews

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Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks -obtained by aggregating or neglecting temporal or categorical description of biological data -are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.Gosak et al [1] review the most recent advances in the application of multilayer networks for modeling complex biological systems, from molecular interactions within a cell to neuronal connectivity of the human brain.

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The interdependent network of gene regulation and metabolism is robust where it needs to be

Cited by 58 publications

References 72 publications

Depth Penetration and Scope Extension of Failures in the Cascading of Multilayer Networks

Depth Penetration and Scope Extension of Failures in the Cascading of Multilayer Networks

Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects

Multilayer network modeling of integrated biological systems

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