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Published on: April 6, 2016
Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling
Tien-Dzung Tran1,2, Duc-Tinh Pham3,4
1Complex Systems and Bioinformatics Lab, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam. trantd@haui.edu.vn.
Abstract:
Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model-the outside competitive dynamics model-wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.
Insights
Identifying cancer drug targets is crucial. This study introduces a novel network dynamics model to pinpoint key genes, finding that 82% of top predicted genes are actual anticancer drug targets, outperforming existing methods.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Cancer is driven by complex molecular signaling networks.
- Identifying effective drug targets within these networks is a significant challenge.
- Understanding network dynamics is key to discovering novel therapeutic strategies.
Purpose of the Study:
- To develop and validate a novel computational model for identifying potential anticancer drug target genes.
- To analyze the dynamics of molecular signaling networks to predict genes with high therapeutic potential.
- To assess the efficacy of the proposed model against existing methods for cancer drug target prediction.
Main Methods:
- Utilized an 'outside competitive dynamics' model to simulate interactions within cancer signaling networks.
- Applied a distributed consensus protocol for normal agents to adjust states based on internal leaders and external competitors.
- Quantified 'total support' of normal agents to leaders and correlated it with hierarchical closeness to identify biomarker genes.
- Experimented on 17 cancer signaling networks from the KEGG database.
Main Results:
- The total support of normal agents to leaders correlates with hierarchical closeness in cancer signaling networks.
- Identified that 82% of the top 3 predicted genes with the highest total support are established anticancer drug target genes.
- The proposed model demonstrated superior performance compared to four previous prediction methods for common cancer drug targets.
Conclusions:
- Driver agents exhibiting high support against external influence are strong candidates for anticancer drug targets.
- The 'outside competitive dynamics' model provides a robust framework for discovering novel cancer drug targets.
- This approach offers a promising advancement in precision oncology and drug development.
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