Determining interaction directionality in complex biochemical networks from stationary measurements
Abstract Revealing interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Some methods may reveal undirected network topology, e.g., using node-node correlation. Yet, the direction of the interaction, thus a causal inference, remains t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86332-0 |
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| _version_ | 1832585819837169664 |
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| author | N. Leibovich |
| author_facet | N. Leibovich |
| author_sort | N. Leibovich |
| collection | DOAJ |
| description | Abstract Revealing interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Some methods may reveal undirected network topology, e.g., using node-node correlation. Yet, the direction of the interaction, thus a causal inference, remains to be determined - especially in steady-state observations. We introduce a method to infer the directionality within this network only from a “snapshot” of the abundances of the relevant molecules. We examine the validity of the approach for different properties of the system and the data recorded, such as the molecule’s level variability, the effect of sampling and measurement errors. Simulations suggest that the given approach successfully infer the reaction rates in various cases. |
| format | Article |
| id | doaj-art-fe1f4e5800024a6d9f087a46ef62fa30 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fe1f4e5800024a6d9f087a46ef62fa302025-01-26T12:29:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-86332-0Determining interaction directionality in complex biochemical networks from stationary measurementsN. Leibovich0National Research Council of Canada, NRC-Fields Mathematical Sciences Collaboration CentreAbstract Revealing interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Some methods may reveal undirected network topology, e.g., using node-node correlation. Yet, the direction of the interaction, thus a causal inference, remains to be determined - especially in steady-state observations. We introduce a method to infer the directionality within this network only from a “snapshot” of the abundances of the relevant molecules. We examine the validity of the approach for different properties of the system and the data recorded, such as the molecule’s level variability, the effect of sampling and measurement errors. Simulations suggest that the given approach successfully infer the reaction rates in various cases.https://doi.org/10.1038/s41598-025-86332-0 |
| spellingShingle | N. Leibovich Determining interaction directionality in complex biochemical networks from stationary measurements Scientific Reports |
| title | Determining interaction directionality in complex biochemical networks from stationary measurements |
| title_full | Determining interaction directionality in complex biochemical networks from stationary measurements |
| title_fullStr | Determining interaction directionality in complex biochemical networks from stationary measurements |
| title_full_unstemmed | Determining interaction directionality in complex biochemical networks from stationary measurements |
| title_short | Determining interaction directionality in complex biochemical networks from stationary measurements |
| title_sort | determining interaction directionality in complex biochemical networks from stationary measurements |
| url | https://doi.org/10.1038/s41598-025-86332-0 |
| work_keys_str_mv | AT nleibovich determininginteractiondirectionalityincomplexbiochemicalnetworksfromstationarymeasurements |