Revealing structure-function relationships in functional flow networks via persistent homology
Complex networks encountered in biology are often characterized by significant structural diversity. Whether due to differences in the three-dimensional structure of allosteric proteins, or the variation among the microscale structures of organisms' cerebral vasculature systems, identifying rel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2020-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.2.033234 |
| _version_ | 1827286051728654336 |
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| author | Jason W. Rocks Andrea J. Liu Eleni Katifori |
| author_facet | Jason W. Rocks Andrea J. Liu Eleni Katifori |
| author_sort | Jason W. Rocks |
| collection | DOAJ |
| description | Complex networks encountered in biology are often characterized by significant structural diversity. Whether due to differences in the three-dimensional structure of allosteric proteins, or the variation among the microscale structures of organisms' cerebral vasculature systems, identifying relationships between structure and function often poses a difficult challenge. Here we showcase an approach to characterizing structure-function relationships in complex networks applied in the context of flow networks tuned to perform specific functions. Using persistent homology, we analyze flow networks tuned to perform complex multifunctional tasks, answering the question of how local changes in the network structure coordinate to create functionality at the scale of the entire network. We find that the response of such networks encodes hidden topological features—sectors of uniform pressure—that are not apparent in the underlying network architectures. Regardless of differences in local connectivity, these features provide a universal topological description for all networks that perform these types of functions. We show that these features correlate strongly with the tuned response, providing a clear topological relationship between structure and function and structural insight into the limits of multifunctionality. |
| first_indexed | 2024-04-24T10:25:02Z |
| format | Article |
| id | doaj.art-96545f5668144fba8cde554bc055c826 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:25:02Z |
| publishDate | 2020-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-96545f5668144fba8cde554bc055c8262024-04-12T16:58:42ZengAmerican Physical SocietyPhysical Review Research2643-15642020-08-012303323410.1103/PhysRevResearch.2.033234Revealing structure-function relationships in functional flow networks via persistent homologyJason W. RocksAndrea J. LiuEleni KatiforiComplex networks encountered in biology are often characterized by significant structural diversity. Whether due to differences in the three-dimensional structure of allosteric proteins, or the variation among the microscale structures of organisms' cerebral vasculature systems, identifying relationships between structure and function often poses a difficult challenge. Here we showcase an approach to characterizing structure-function relationships in complex networks applied in the context of flow networks tuned to perform specific functions. Using persistent homology, we analyze flow networks tuned to perform complex multifunctional tasks, answering the question of how local changes in the network structure coordinate to create functionality at the scale of the entire network. We find that the response of such networks encodes hidden topological features—sectors of uniform pressure—that are not apparent in the underlying network architectures. Regardless of differences in local connectivity, these features provide a universal topological description for all networks that perform these types of functions. We show that these features correlate strongly with the tuned response, providing a clear topological relationship between structure and function and structural insight into the limits of multifunctionality.http://doi.org/10.1103/PhysRevResearch.2.033234 |
| spellingShingle | Jason W. Rocks Andrea J. Liu Eleni Katifori Revealing structure-function relationships in functional flow networks via persistent homology Physical Review Research |
| title | Revealing structure-function relationships in functional flow networks via persistent homology |
| title_full | Revealing structure-function relationships in functional flow networks via persistent homology |
| title_fullStr | Revealing structure-function relationships in functional flow networks via persistent homology |
| title_full_unstemmed | Revealing structure-function relationships in functional flow networks via persistent homology |
| title_short | Revealing structure-function relationships in functional flow networks via persistent homology |
| title_sort | revealing structure function relationships in functional flow networks via persistent homology |
| url | http://doi.org/10.1103/PhysRevResearch.2.033234 |
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