Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Satyaki Roy ◽  
Preetam Ghosh ◽  
Nirnay Ghosh ◽  
Sajal K. Das

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

10.1038/ng873 ◽  
2002 ◽  
Vol 31 (1) ◽  
pp. 60-63 ◽  
Nabil Guelzim ◽  
Samuele Bottani ◽  
Paul Bourgine ◽  
François Képès

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

2020 ◽  
Vol 14 (5) ◽  
pp. 292-296
Prithvi Singh ◽  
Mohd Amir ◽  
Upasana Chaudhary ◽  
Fozail Ahmad ◽  
Sachin Bhatt ◽  

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