Leveraging public AI tools to explore systems biology resources in mathematical modeling
Abstract Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00496-z |
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| _version_ | 1823861938944212992 |
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| author | Meera Kannan Gabrielle Bridgewater Ming Zhang Michael L. Blinov |
| author_facet | Meera Kannan Gabrielle Bridgewater Ming Zhang Michael L. Blinov |
| author_sort | Meera Kannan |
| collection | DOAJ |
| description | Abstract Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve. |
| format | Article |
| id | doaj-art-07a99a06b86c4ef090acb9b28bf33abb |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-07a99a06b86c4ef090acb9b28bf33abb2025-02-09T12:43:14ZengNature Portfolionpj Systems Biology and Applications2056-71892025-02-011111810.1038/s41540-025-00496-zLeveraging public AI tools to explore systems biology resources in mathematical modelingMeera Kannan0Gabrielle Bridgewater1Ming Zhang2Michael L. Blinov3Center for Cell Analysis and Modeling, UConn HealthCenter for Cell Analysis and Modeling, UConn HealthTheoretical Biology and Biophysics Group, Los Alamos National LaboratoryCenter for Cell Analysis and Modeling, UConn HealthAbstract Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.https://doi.org/10.1038/s41540-025-00496-z |
| spellingShingle | Meera Kannan Gabrielle Bridgewater Ming Zhang Michael L. Blinov Leveraging public AI tools to explore systems biology resources in mathematical modeling npj Systems Biology and Applications |
| title | Leveraging public AI tools to explore systems biology resources in mathematical modeling |
| title_full | Leveraging public AI tools to explore systems biology resources in mathematical modeling |
| title_fullStr | Leveraging public AI tools to explore systems biology resources in mathematical modeling |
| title_full_unstemmed | Leveraging public AI tools to explore systems biology resources in mathematical modeling |
| title_short | Leveraging public AI tools to explore systems biology resources in mathematical modeling |
| title_sort | leveraging public ai tools to explore systems biology resources in mathematical modeling |
| url | https://doi.org/10.1038/s41540-025-00496-z |
| work_keys_str_mv | AT meerakannan leveragingpublicaitoolstoexploresystemsbiologyresourcesinmathematicalmodeling AT gabriellebridgewater leveragingpublicaitoolstoexploresystemsbiologyresourcesinmathematicalmodeling AT mingzhang leveragingpublicaitoolstoexploresystemsbiologyresourcesinmathematicalmodeling AT michaellblinov leveragingpublicaitoolstoexploresystemsbiologyresourcesinmathematicalmodeling |