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

Jin G, Zhao H, Zhou X, Wong ST - Bioinformatics (2011)

Bottom Line: Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data.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 molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism.

View Article: PubMed Central - PubMed

Affiliation: Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.

ABSTRACT

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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Related in: MedlinePlus

The downstream signaling network for targets of the pairwise combination of gefitinib and docetaxel. The related PPI data are not shown.
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Figure 2: The downstream signaling network for targets of the pairwise combination of gefitinib and docetaxel. The related PPI data are not shown.

Mentions: Before implementation of EPN on the drug-treated microarray data of pairwise drug combinations, we have to identify the downstream signals and related protein–protein interactions (PPIs) of the targets, i.e. epidermal growth factor receptor (EGFR) of gefitinib and B-cell lymphoma 2 (BCL2) of docetaxel. The combined data for signaling pathways were derived from three pathway databases, including KEGG: Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al., 2004), NCI PID: Pathway Interaction Database (Schaefer et al., 2009) and BioCarta (Schaefer et al., 2009). The PPIs used here are the physical interactions gathered from five PPI databases, IntAct (Kerrien et al., 2007), DIP (Xenarios et al., 2002), MINT (Chatr-aryamontri et al., 2007), MIPS (Mewes et al., 2002) and BioGrid (Breitkreutz et al., 2008). The identified downstream signaling network comprises 575 proteins with 919 signaling interactions, as shown in Figure 2, which interacts with another 433 proteins via 4365 PPIs.Fig. 2.


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

Jin G, Zhao H, Zhou X, Wong ST - Bioinformatics (2011)

The downstream signaling network for targets of the pairwise combination of gefitinib and docetaxel. The related PPI data are not shown.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117391&req=5

Figure 2: The downstream signaling network for targets of the pairwise combination of gefitinib and docetaxel. The related PPI data are not shown.
Mentions: Before implementation of EPN on the drug-treated microarray data of pairwise drug combinations, we have to identify the downstream signals and related protein–protein interactions (PPIs) of the targets, i.e. epidermal growth factor receptor (EGFR) of gefitinib and B-cell lymphoma 2 (BCL2) of docetaxel. The combined data for signaling pathways were derived from three pathway databases, including KEGG: Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al., 2004), NCI PID: Pathway Interaction Database (Schaefer et al., 2009) and BioCarta (Schaefer et al., 2009). The PPIs used here are the physical interactions gathered from five PPI databases, IntAct (Kerrien et al., 2007), DIP (Xenarios et al., 2002), MINT (Chatr-aryamontri et al., 2007), MIPS (Mewes et al., 2002) and BioGrid (Breitkreutz et al., 2008). The identified downstream signaling network comprises 575 proteins with 919 signaling interactions, as shown in Figure 2, which interacts with another 433 proteins via 4365 PPIs.Fig. 2.

Bottom Line: Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data.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 molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism.

View Article: PubMed Central - PubMed

Affiliation: Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus