Controllability of human cancer signaling network

2016 ◽  
Author(s):  
Vandana Ravindran ◽  
V Sunitha ◽  
Ganesh Bagler
Keyword(s):  
Human Cancer ◽  
Signaling Network ◽  
Cancer Signaling

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach 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.

scholarly journals Atlas of Cancer Signaling Network: A Resource of Multi-Scale Biological Maps to Study Disease Mechanisms

2021 ◽  
pp. 490-506
Author(s):  
Luis Cristobal Monraz Gomez ◽  
Maria Kondratova ◽  
Nicolas Sompairac ◽  
Christine Lonjou ◽  
Jean-Marie Ravel ◽  
...  
Keyword(s):  
Signaling Network ◽  
Multi Scale ◽  
Cancer Signaling ◽  
Disease Mechanisms

scholarly journals Mining signaling flow to interpret mechanisms of synergy of drug combinations using deep graph neural networks

2021 ◽  
Author(s):  
Heming Zhang ◽  
Yixin Chen ◽  
Philip R Payne ◽  
Fuhai Li
Keyword(s):  
Drug Resistance ◽  
Signaling Pathways ◽  
Drug Combination ◽  
Drug Targets ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Signaling Network ◽  
Drug Combinations ◽  
Convolutional Network ◽  
Cancer Signaling

Complex signaling pathways/networks are believed to be responsible for drug resistance in cancer therapy. Drug combinations inhibiting multiple signaling targets within cancer-related signaling networks have the potential to reduce drug resistance. Deep learning models have been reported to predict drug combinations. However, these models are hard to be interpreted in terms of mechanism of synergy (MoS), and thus cannot well support the human-AI based clinical decision making. Herein, we proposed a novel computational model, DeepSignalingFlow, which seeks to address the preceding two challenges. Specifically, a graph convolutional network (GCN) was developed based on a core cancer signaling network consisting of 1584 genes, with gene expression and copy number data derived from 46 core cancer signaling pathways. The novel up-stream signaling-flow (from up-stream signaling to drug targets), and the down-stream signaling-flow (from drug targets to down-stream signaling), were designed using trainable weights of network edges. The numerical features (accumulated information due to the signaling-flows of the signaling network) of drug nodes that link to drug targets were then used to predict the synergy scores of such drug combinations. The model was evaluated using the NCI ALMANAC drug combination screening data. The evaluation results showed that the proposed DeepSignalingFlow model can not only predict drug combination synergy score, but also interpret potentially interpretable MoS of drug combinations.

scholarly journals Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

Oncotarget ◽  
2014 ◽  
Vol 6 (7) ◽  
pp. 5041-5058 ◽  
Author(s):  
Lorenzo Tortolina ◽  
David J. Duffy ◽  
Massimo Maffei ◽  
Nicoletta Castagnino ◽  
Aimée M. Carmody ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dynamic Modeling ◽  
Targeted Therapies ◽  
Signaling Network ◽  
Cancer Signaling

scholarly journals A map of human cancer signaling

2007 ◽  
Vol 3 (1) ◽  
pp. 152 ◽  
Author(s):  
Qinghua Cui ◽  
Yun Ma ◽  
Maria Jaramillo ◽  
Hamza Bari ◽  
Arif Awan ◽  
...  
Keyword(s):  
Human Cancer ◽  
Cancer Signaling
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