Accession Number : AD1052937


Title :   Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks


Descriptive Note : Journal Article - Open Access


Corporate Author : Department of Biological Engineering, Massachusetts Institute of Technology Cambridge United States


Personal Author(s) : Morris , M K ; Clarke ,D C ; Osimiri,L C ; Lauffenburger,D A


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1052937.pdf


Report Date : 27 Aug 2016


Pagination or Media Count : 10


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-1a 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.


Descriptors :   PHYSIOLOGICAL EFFECTS OF DRUGS , proteins , therapy , drug combinations , transcription factors , peptide growth factors , liver diseases , experimental data , biological factors , inhibitors , hepatitis , cytokines , albumins , simulations , predictions , carcinoma , fuzzy logic


Subject Categories : Pharmacology
      Anatomy and Physiology


Distribution Statement : APPROVED FOR PUBLIC RELEASE