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An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data

Motivation: 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) mode...

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Autores principales: Jin, Guangxu, Zhao, Hong, Zhou, Xiaobo, Wong, Stephen T. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117391/
https://www.ncbi.nlm.nih.gov/pubmed/21685086
http://dx.doi.org/10.1093/bioinformatics/btr202
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author Jin, Guangxu
Zhao, Hong
Zhou, Xiaobo
Wong, Stephen T. C.
author_facet Jin, Guangxu
Zhao, Hong
Zhou, Xiaobo
Wong, Stephen T. C.
author_sort Jin, Guangxu
collection PubMed
description Motivation: 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. Methods: 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. Results: 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. Availability: The software implemented in Python 2.7 programming language is available from request. Contact: stwong@tmhs.org
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spelling pubmed-31173912011-06-17 An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data Jin, Guangxu Zhao, Hong Zhou, Xiaobo Wong, Stephen T. C. Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: 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. Methods: 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. Results: 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. Availability: The software implemented in Python 2.7 programming language is available from request. Contact: stwong@tmhs.org Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117391/ /pubmed/21685086 http://dx.doi.org/10.1093/bioinformatics/btr202 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
Jin, Guangxu
Zhao, Hong
Zhou, Xiaobo
Wong, Stephen T. C.
An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title_full An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title_fullStr An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title_full_unstemmed An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title_short An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
title_sort enhanced petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data
topic Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117391/
https://www.ncbi.nlm.nih.gov/pubmed/21685086
http://dx.doi.org/10.1093/bioinformatics/btr202
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