Prevalence of deficiency-zero reaction networks in an Erdös–Rényi framework
Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e....
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Veröffentlicht in: | Journal of applied probability 2022-06, Vol.59 (2), p.384-398 |
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description | Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e. the species. We consider these networks in an Erdös–Rényi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and the edge probability by
$p_n$
, then we prove that the probability of a random binary network being deficiency zero converges to 1 if
$p_n\ll r(n)$
as
$n \to \infty$
, and converges to 0 if
$p_n \gg r(n)$
as
$n \to \infty$
, where
$r(n)=\frac{1}{n^3}$
. |
doi_str_mv | 10.1017/jpr.2021.65 |
format | Article |
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$p_n$
, then we prove that the probability of a random binary network being deficiency zero converges to 1 if
$p_n\ll r(n)$
as
$n \to \infty$
, and converges to 0 if
$p_n \gg r(n)$
as
$n \to \infty$
, where
$r(n)=\frac{1}{n^3}$
.</description><identifier>ISSN: 0021-9002</identifier><identifier>EISSN: 1475-6072</identifier><identifier>DOI: 10.1017/jpr.2021.65</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Apexes ; Convergence ; Graph theory ; Mathematical analysis ; Networks ; Original Article</subject><ispartof>Journal of applied probability, 2022-06, Vol.59 (2), p.384-398</ispartof><rights>The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c299t-6e17a79284d4162399d1facacb4cd480899e4f46b9e5360a75d3c93e2ccc1a033</citedby><cites>FETCH-LOGICAL-c299t-6e17a79284d4162399d1facacb4cd480899e4f46b9e5360a75d3c93e2ccc1a033</cites><orcidid>0000-0002-2344-614X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0021900221000656/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,27901,27902,55603</link.rule.ids></links><search><creatorcontrib>Anderson, David F.</creatorcontrib><creatorcontrib>Nguyen, Tung D.</creatorcontrib><title>Prevalence of deficiency-zero reaction networks in an Erdös–Rényi framework</title><title>Journal of applied probability</title><addtitle>J. Appl. Probab</addtitle><description>Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e. the species. We consider these networks in an Erdös–Rényi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and the edge probability by
$p_n$
, then we prove that the probability of a random binary network being deficiency zero converges to 1 if
$p_n\ll r(n)$
as
$n \to \infty$
, and converges to 0 if
$p_n \gg r(n)$
as
$n \to \infty$
, where
$r(n)=\frac{1}{n^3}$
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Nguyen, Tung D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c299t-6e17a79284d4162399d1facacb4cd480899e4f46b9e5360a75d3c93e2ccc1a033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Apexes</topic><topic>Convergence</topic><topic>Graph theory</topic><topic>Mathematical analysis</topic><topic>Networks</topic><topic>Original Article</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anderson, David F.</creatorcontrib><creatorcontrib>Nguyen, Tung D.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of applied probability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anderson, David F.</au><au>Nguyen, Tung D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prevalence of deficiency-zero reaction networks in an Erdös–Rényi framework</atitle><jtitle>Journal of applied probability</jtitle><addtitle>J. Appl. Probab</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>59</volume><issue>2</issue><spage>384</spage><epage>398</epage><pages>384-398</pages><issn>0021-9002</issn><eissn>1475-6072</eissn><abstract>Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e. the species. We consider these networks in an Erdös–Rényi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and the edge probability by
$p_n$
, then we prove that the probability of a random binary network being deficiency zero converges to 1 if
$p_n\ll r(n)$
as
$n \to \infty$
, and converges to 0 if
$p_n \gg r(n)$
as
$n \to \infty$
, where
$r(n)=\frac{1}{n^3}$
.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><doi>10.1017/jpr.2021.65</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-2344-614X</orcidid></addata></record> |
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subjects | Apexes Convergence Graph theory Mathematical analysis Networks Original Article |
title | Prevalence of deficiency-zero reaction networks in an Erdös–Rényi framework |
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