Effects of Antimodularity and Multiscale Influence in Random Boolean Networks

We investigate the effects of modularity, antimodularity, and multiscale influence on random Boolean networks (RBNs). On the one hand, we produced modular, antimodular, and standard RBNs and compared them to identify how antimodularity affects the dynamical behaviors of RBNs. We found that the antim...

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Main Authors: Luis A. Escobar, Hyobin Kim, Carlos Gershenson
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/8209146
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spelling doaj-88d29c4e2780469c901e2b72dc30df052020-11-25T01:04:38ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/82091468209146Effects of Antimodularity and Multiscale Influence in Random Boolean NetworksLuis A. Escobar0Hyobin Kim1Carlos Gershenson2Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, 04510 CDMX, MexicoCentro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, 04510 CDMX, MexicoCentro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, 04510 CDMX, MexicoWe investigate the effects of modularity, antimodularity, and multiscale influence on random Boolean networks (RBNs). On the one hand, we produced modular, antimodular, and standard RBNs and compared them to identify how antimodularity affects the dynamical behaviors of RBNs. We found that the antimodular networks showed similar dynamics to the standard networks. Confirming previous results, modular networks had more complex dynamics. On the other hand, we generated multilayer RBNs where there are different RBNs in the nodes of a higher scale RBN. We observed the dynamics of micro- and macronetworks by adjusting parameters at each scale to reveal how the behavior of lower layers affects the behavior of higher layers and vice versa. We found that the statistical properties of macro-RBNs were changed by the parameters of micro-RBNs, but not the other way around. However, the precise patterns of networks were dominated by the macro-RBNs. In other words, for statistical properties only upward causation was relevant, while for the detailed dynamics downward causation was prevalent.http://dx.doi.org/10.1155/2019/8209146
collection DOAJ
language English
format Article
sources DOAJ
author Luis A. Escobar
Hyobin Kim
Carlos Gershenson
spellingShingle Luis A. Escobar
Hyobin Kim
Carlos Gershenson
Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
Complexity
author_facet Luis A. Escobar
Hyobin Kim
Carlos Gershenson
author_sort Luis A. Escobar
title Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
title_short Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
title_full Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
title_fullStr Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
title_full_unstemmed Effects of Antimodularity and Multiscale Influence in Random Boolean Networks
title_sort effects of antimodularity and multiscale influence in random boolean networks
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2019-01-01
description We investigate the effects of modularity, antimodularity, and multiscale influence on random Boolean networks (RBNs). On the one hand, we produced modular, antimodular, and standard RBNs and compared them to identify how antimodularity affects the dynamical behaviors of RBNs. We found that the antimodular networks showed similar dynamics to the standard networks. Confirming previous results, modular networks had more complex dynamics. On the other hand, we generated multilayer RBNs where there are different RBNs in the nodes of a higher scale RBN. We observed the dynamics of micro- and macronetworks by adjusting parameters at each scale to reveal how the behavior of lower layers affects the behavior of higher layers and vice versa. We found that the statistical properties of macro-RBNs were changed by the parameters of micro-RBNs, but not the other way around. However, the precise patterns of networks were dominated by the macro-RBNs. In other words, for statistical properties only upward causation was relevant, while for the detailed dynamics downward causation was prevalent.
url http://dx.doi.org/10.1155/2019/8209146
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