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...
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| Vydané v: | CPT: pharmacometrics and systems pharmacology Ročník 5; číslo 10; s. 544 - 553 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
John Wiley & Sons, Inc
01.10.2016
John Wiley and Sons Inc |
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| ISSN: | 2163-8306, 2163-8306 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Morris, MK Clarke, DC Osimiri, LC Lauffenburger, DA |
| AuthorAffiliation | 1 Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA |
| AuthorAffiliation_xml | – name: 1 Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA |
| Author_xml | – sequence: 1 givenname: MK surname: Morris fullname: Morris, MK organization: Massachusetts Institute of Technology – sequence: 2 givenname: DC surname: Clarke fullname: Clarke, DC organization: Massachusetts Institute of Technology – sequence: 3 givenname: LC surname: Osimiri fullname: Osimiri, LC organization: Massachusetts Institute of Technology – sequence: 4 givenname: DA surname: Lauffenburger fullname: Lauffenburger, DA email: lauffen@mit.edu organization: Massachusetts Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27567007$$D View this record in MEDLINE/PubMed |
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| Title | Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks |
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