Identification of critical regulatory genes in cancer signaling network using controllability analysis

2017 ◽  
Vol 474 ◽  
pp. 134-143 ◽  
Author(s):  
Vandana Ravindran ◽  
Sunitha V. ◽  
Ganesh Bagler
Keyword(s):  
Signaling Network ◽  
Regulatory Genes ◽  
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

Chemosensory cholinergic signaling network in the thymic medullary epithelium

Pneumologie ◽  
2015 ◽  
Vol 69 (07) ◽  
Author(s):  
A Soultanova ◽  
A Panneck ◽  
A Rafiq ◽  
B Schütz ◽  
V Chubanov ◽  
...  
Keyword(s):  
Signaling Network ◽  
Cholinergic Signaling

Chemosensory cholinergic signaling network in the thymic medullary epithelium

Pneumologie ◽  
2016 ◽  
Vol 70 (07) ◽  
Author(s):  
A Soultanova ◽  
C Cen ◽  
K Fleck ◽  
G Krasteva-Christ ◽  
U Boehm ◽  
...  
Keyword(s):  
Signaling Network ◽  
Cholinergic Signaling

Dynamics of fatty acid regulatory genes during ovarian development in Penaeus monodon

Reproduction ◽  
2018 ◽  
Author(s):  
Pacharawan Deenarn ◽  
Punsa Tobwor ◽  
Rungnapa Leelatanawit ◽  
Somjai Wongtriphop ◽  
Jutatip Khudet ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Ovarian Development ◽  
Penaeus Monodon ◽  
Chain Fatty Acid ◽  
Regulatory Genes ◽  
Ovarian Maturation ◽  
Long Chain Fatty Acid ◽  
Eyestalk Ablation ◽  
Regulatory Pathways ◽  
Expression Levels

The delay in ovarian maturation in farmed black tiger shrimp Penaeus monodon has resulted in the widespread practice of feeding broodstock with the polychaetes Perinereis nuntia and their unilateral eyestalk ablation. Although this practice alters fatty acid content in shrimp ovaries and hepatopancreas, its effects on fatty acid regulatory genes have yet to be systematically examined. Here, microarray analysis was performed on hepatopancreas and ovary cDNA collected from P. monodon at different ovarian maturation stages, revealing that 72 and 58 genes in fatty acid regulatory pathways were differentially expressed in hepatopancreas and ovaries respectively. Quantitative real-time PCR analysis revealed that ovarian maturation was associated with higher expression levels of acetyl-CoA acetyltransferase, acyl-CoA dehydrogenase, acyl-CoA oxidase 3 and long-chain fatty acid transport protein 4 in hepatopancreas, whereas the expression levels of 15 fatty acid regulatory genes were increased in shrimp ovaries. To distinguish the effects of different treatments, transcriptional changes were examined in P. monodon with stage 1 ovaries before polychaete feeding, after one-month of polychaete feeding and after eyestalk ablation. Polychaete feeding resulted in lower expression levels of enoyl-CoA hydratase and acyl-CoA synthetase medium-chain family member 4, while the expression level of phosphatidylinositide phosphatase SAC1 was higher in shrimp hepatopancreas and ovaries. Additionally, eyestalk ablation resulted in a higher expression level of long-chain fatty acid-CoA ligase 4 in both tissues. Together, our findings describe the dynamics of fatty acid regulatory pathways during crustacean ovarian development and provide potential target genes for alternatives to eyestalk ablation in the future.

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