Stochastic cycle selection in active flow networks
Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organizati...
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| Format: | Article |
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National Academy of Sciences (U.S.)
2018
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| Online Access: | http://hdl.handle.net/1721.1/114921 https://orcid.org/0000-0001-8316-5369 https://orcid.org/0000-0001-8865-2369 |
| _version_ | 1811086077258104832 |
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| author | Woodhouse, Francis G. Fawcett, Joanna B. Forrow, Aden Dunkel, Joern |
| author2 | Massachusetts Institute of Technology. Department of Mathematics |
| author_facet | Massachusetts Institute of Technology. Department of Mathematics Woodhouse, Francis G. Fawcett, Joanna B. Forrow, Aden Dunkel, Joern |
| author_sort | Woodhouse, Francis G. |
| collection | MIT |
| description | Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. Keywords: networks; active transport; stochastic dynamics; topology |
| first_indexed | 2024-09-23T13:20:31Z |
| format | Article |
| id | mit-1721.1/114921 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:20:31Z |
| publishDate | 2018 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/1149212022-10-01T14:39:40Z Stochastic cycle selection in active flow networks Woodhouse, Francis G. Fawcett, Joanna B. Forrow, Aden Dunkel, Joern Massachusetts Institute of Technology. Department of Mathematics Forrow, Aden Dunkel, Joern Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. Keywords: networks; active transport; stochastic dynamics; topology National Science Foundation (U.S.) (Award CBET-1510768) 2018-04-24T14:01:24Z 2018-04-24T14:01:24Z 2016-07 2016-03 2018-04-20T15:54:12Z Article http://purl.org/eprint/type/ConferencePaper 0027-8424 1091-6490 http://hdl.handle.net/1721.1/114921 Woodhouse, Francis G. et al. “Stochastic Cycle Selection in Active Flow Networks.” Proceedings of the National Academy of Sciences 113, 29 (July 2016): 8200–8205 © 2016 National Academy of Sciences https://orcid.org/0000-0001-8316-5369 https://orcid.org/0000-0001-8865-2369 http://dx.doi.org/10.1073/PNAS.1603351113 Proceedings of the National Academy of Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) National Academy of Sciences |
| spellingShingle | Woodhouse, Francis G. Fawcett, Joanna B. Forrow, Aden Dunkel, Joern Stochastic cycle selection in active flow networks |
| title | Stochastic cycle selection in active flow networks |
| title_full | Stochastic cycle selection in active flow networks |
| title_fullStr | Stochastic cycle selection in active flow networks |
| title_full_unstemmed | Stochastic cycle selection in active flow networks |
| title_short | Stochastic cycle selection in active flow networks |
| title_sort | stochastic cycle selection in active flow networks |
| url | http://hdl.handle.net/1721.1/114921 https://orcid.org/0000-0001-8316-5369 https://orcid.org/0000-0001-8865-2369 |
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