Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors
Many chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor. We used the Hopfield net as the archetypical examp...
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| Veröffentlicht in: | PloS one 2010-12, Vol.5 (12), p.e14413-e14413 |
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| description | Many chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor.
We used the Hopfield net as the archetypical example of a dynamic biological network. By progressively removing the links of fully connected Hopfield nets, we found that a designated attractor of the nets could still be supported until only slightly more than 1 link per node remained. As the number of links approached this minimum value, the rate of convergence to this attractor from an arbitrary starting state increased dramatically. Furthermore, with more than about twice the minimum of links, the net became increasingly able to support a second attractor.
We speculate that homeostatic biological networks may have evolved to assume a degree of connectivity that balances robustness and agility against the dangers of becoming trapped in an abnormal attractor. |
| doi_str_mv | 10.1371/journal.pone.0014413 |
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We speculate that homeostatic biological networks may have evolved to assume a degree of connectivity that balances robustness and agility against the dangers of becoming trapped in an abnormal attractor.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0014413</identifier><identifier>PMID: 21203505</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biochemistry/Theory and Simulation ; Biological evolution ; Computational Biology/Metabolic Networks ; Computational Biology/Systems Biology ; Computer Simulation ; Connectivity ; Cytokines ; Genetic Diseases, Inborn - metabolism ; Homeostasis ; Humans ; Kinetics ; Links ; Mathematics/Nonlinear Dynamics ; Medicine ; Models, Biological ; Models, Statistical ; Molecular Biology/Bioinformatics ; Nets ; Neural networks ; Neural Networks (Computer) ; Nonlinear Dynamics ; Physiology/Integrative Physiology ; Proteins ; Pulmonary fibrosis ; Robustness ; Software ; Systems Biology</subject><ispartof>PloS one, 2010-12, Vol.5 (12), p.e14413-e14413</ispartof><rights>COPYRIGHT 2010 Public Library of Science</rights><rights>2010 Anafi, Bates. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Anafi, Bates. 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c691t-50856250ba24a2f2428cfd7a422e60907e746fc8016d338a00b5caf52406fb453</citedby><cites>FETCH-LOGICAL-c691t-50856250ba24a2f2428cfd7a422e60907e746fc8016d338a00b5caf52406fb453</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3008716/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3008716/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21203505$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Anafi, Ron C</creatorcontrib><creatorcontrib>Bates, Jason H T</creatorcontrib><title>Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Many chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor.
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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anafi, Ron C</au><au>Bates, Jason H T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2010-12-22</date><risdate>2010</risdate><volume>5</volume><issue>12</issue><spage>e14413</spage><epage>e14413</epage><pages>e14413-e14413</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Many chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor.
We used the Hopfield net as the archetypical example of a dynamic biological network. By progressively removing the links of fully connected Hopfield nets, we found that a designated attractor of the nets could still be supported until only slightly more than 1 link per node remained. As the number of links approached this minimum value, the rate of convergence to this attractor from an arbitrary starting state increased dramatically. Furthermore, with more than about twice the minimum of links, the net became increasingly able to support a second attractor.
We speculate that homeostatic biological networks may have evolved to assume a degree of connectivity that balances robustness and agility against the dangers of becoming trapped in an abnormal attractor.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>21203505</pmid><doi>10.1371/journal.pone.0014413</doi><tpages>e14413</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Biochemistry/Theory and Simulation Biological evolution Computational Biology/Metabolic Networks Computational Biology/Systems Biology Computer Simulation Connectivity Cytokines Genetic Diseases, Inborn - metabolism Homeostasis Humans Kinetics Links Mathematics/Nonlinear Dynamics Medicine Models, Biological Models, Statistical Molecular Biology/Bioinformatics Nets Neural networks Neural Networks (Computer) Nonlinear Dynamics Physiology/Integrative Physiology Proteins Pulmonary fibrosis Robustness Software Systems Biology |
| title | Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors |
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