A thermodynamic-based approach for the resolution and prediction of protein network structures
© 2018 Elsevier B.V. The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront. We suggest studying cancer...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/135794 |
| _version_ | 1811075304992538624 |
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| author | Flashner-Abramson, Efrat Abramson, Jonathan White, Forest M Kravchenko-Balasha, Nataly |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Flashner-Abramson, Efrat Abramson, Jonathan White, Forest M Kravchenko-Balasha, Nataly |
| author_sort | Flashner-Abramson, Efrat |
| collection | MIT |
| description | © 2018 Elsevier B.V. The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront. We suggest studying cancer from a thermodynamic-based point of view. We hypothesize that by modelling biological systems based on physico-chemical laws, highly complex systems can be reduced to a few parameters, and their behavior under varying conditions, including response to therapy, can be predicted. Here we validate the predictive power of our thermodynamic-based approach, by uncovering the protein network structure that emerges in MCF10a human mammary cells upon exposure to epidermal growth factor (EGF), and anticipating the consequences of treating the cells with the Src family kinase inhibitor, dasatinib. |
| first_indexed | 2024-09-23T10:03:56Z |
| format | Article |
| id | mit-1721.1/135794 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:03:56Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1357942023-02-23T15:36:13Z A thermodynamic-based approach for the resolution and prediction of protein network structures Flashner-Abramson, Efrat Abramson, Jonathan White, Forest M Kravchenko-Balasha, Nataly Massachusetts Institute of Technology. Department of Biological Engineering © 2018 Elsevier B.V. The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront. We suggest studying cancer from a thermodynamic-based point of view. We hypothesize that by modelling biological systems based on physico-chemical laws, highly complex systems can be reduced to a few parameters, and their behavior under varying conditions, including response to therapy, can be predicted. Here we validate the predictive power of our thermodynamic-based approach, by uncovering the protein network structure that emerges in MCF10a human mammary cells upon exposure to epidermal growth factor (EGF), and anticipating the consequences of treating the cells with the Src family kinase inhibitor, dasatinib. 2021-10-27T20:29:20Z 2021-10-27T20:29:20Z 2018 2019-09-26T16:28:20Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135794 en 10.1016/J.CHEMPHYS.2018.03.005 Chemical Physics Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Other repository |
| spellingShingle | Flashner-Abramson, Efrat Abramson, Jonathan White, Forest M Kravchenko-Balasha, Nataly A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title | A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title_full | A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title_fullStr | A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title_full_unstemmed | A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title_short | A thermodynamic-based approach for the resolution and prediction of protein network structures |
| title_sort | thermodynamic based approach for the resolution and prediction of protein network structures |
| url | https://hdl.handle.net/1721.1/135794 |
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