Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks
A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predictin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2017
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| Online Access: | http://hdl.handle.net/1721.1/108162 |
| _version_ | 1826211474712821760 |
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| author | Morris, Melody Kay Clarke, David C. Osimiri, Lindsey C. Lauffenburger, Douglas A |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Morris, Melody Kay Clarke, David C. Osimiri, Lindsey C. Lauffenburger, Douglas A |
| author_sort | Morris, Melody Kay |
| collection | MIT |
| description | A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predicting inhibitor treatments that reduce the phospho-levels of key transcription factors downstream of growth factors and inflammatory cytokines representative of hepatocellular carcinoma (HCC) microenvironments. We observed that the CFL models successfully predicted the effects of several kinase inhibitor combinations. Furthermore, the ensemble predictions revealed ambiguous predictions that could be traced to a specific structural feature of these models, which we resolved with dedicated experiments, finding that IL-1α activates downstream signals through TAK1 and not MEKK1 in HepG2 cells. We conclude that CFL-Q2LM (Querying Quantitative Logic Models) is a promising approach for predicting effective anticancer drug combinations in cancer-relevant microenvironments. |
| first_indexed | 2024-09-23T15:06:49Z |
| format | Article |
| id | mit-1721.1/108162 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:06:49Z |
| publishDate | 2017 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1081622022-09-29T12:46:12Z Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks Morris, Melody Kay Clarke, David C. Osimiri, Lindsey C. Lauffenburger, Douglas A Massachusetts Institute of Technology. Department of Biological Engineering Morris, Melody Kay Clarke, David C. Osimiri, Lindsey C. Lauffenburger, Douglas A A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predicting inhibitor treatments that reduce the phospho-levels of key transcription factors downstream of growth factors and inflammatory cytokines representative of hepatocellular carcinoma (HCC) microenvironments. We observed that the CFL models successfully predicted the effects of several kinase inhibitor combinations. Furthermore, the ensemble predictions revealed ambiguous predictions that could be traced to a specific structural feature of these models, which we resolved with dedicated experiments, finding that IL-1α activates downstream signals through TAK1 and not MEKK1 in HepG2 cells. We conclude that CFL-Q2LM (Querying Quantitative Logic Models) is a promising approach for predicting effective anticancer drug combinations in cancer-relevant microenvironments. United States. Army Research Office (W911NF-09-0001) 2017-04-14T14:12:12Z 2017-04-14T14:12:12Z 2016-08 2016-06 Article http://purl.org/eprint/type/JournalArticle 2163-8306 http://hdl.handle.net/1721.1/108162 Morris, MK; Clarke, DC; Osimiri, LC and Lauffenburger, DA. “Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks.” CPT: Pharmacometrics & Systems Pharmacology 5, no. 10 (August 27, 2016): 544–553. © 2016 The Authors en_US http://dx.doi.org/10.1002/psp4.12104 CPT: Pharmacometrics & Systems Pharmacology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | Morris, Melody Kay Clarke, David C. Osimiri, Lindsey C. Lauffenburger, Douglas A Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title_full | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title_fullStr | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title_full_unstemmed | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title_short | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
| title_sort | systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks |
| url | http://hdl.handle.net/1721.1/108162 |
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