Constructing graphs from genetic encodings

Abstract Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility ru...

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Autores principales:	Dániel L. Barabási, Dániel Czégel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/03969957840a4f80bddc9f47173eb027
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spelling	oai:doaj.org-article:03969957840a4f80bddc9f47173eb0272021-12-02T18:02:55ZConstructing graphs from genetic encodings10.1038/s41598-021-92577-22045-2322https://doaj.org/article/03969957840a4f80bddc9f47173eb0272021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92577-2https://doaj.org/toc/2045-2322Abstract Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erdős–Rényi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a node’s preferred connectivity.Dániel L. BarabásiDániel CzégelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dániel L. Barabási Dániel Czégel Constructing graphs from genetic encodings
description	Abstract Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erdős–Rényi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a node’s preferred connectivity.
format	article
author	Dániel L. Barabási Dániel Czégel
author_facet	Dániel L. Barabási Dániel Czégel
author_sort	Dániel L. Barabási
title	Constructing graphs from genetic encodings
title_short	Constructing graphs from genetic encodings
title_full	Constructing graphs from genetic encodings
title_fullStr	Constructing graphs from genetic encodings
title_full_unstemmed	Constructing graphs from genetic encodings
title_sort	constructing graphs from genetic encodings
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/03969957840a4f80bddc9f47173eb027
work_keys_str_mv	AT daniellbarabasi constructinggraphsfromgeneticencodings AT danielczegel constructinggraphsfromgeneticencodings
_version_	1718378788852596736

Constructing graphs from genetic encodings

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