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• W2105536900 abstract "Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data.We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens.We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism.The software implemented in Python 2.7 programming language is available from request.stwong@tmhs.org." @default.
• W2105536900 created "2016-06-24" @default.
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• W2105536900 date "2011-06-14" @default.
• W2105536900 modified "2023-09-28" @default.
• W2105536900 title "An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data" @default.
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• W2105536900 doi "https://doi.org/10.1093/bioinformatics/btr202" @default.
• W2105536900 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3117391" @default.
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