Role of Motifs in Topological Robustness of Gene Regulatory Networks


Gene Regulatory Networks (GRNs) are biological networks that have been widely studied for their ability to regulate protein synthesis in cells by robust signal propagation. The innate biological robustness of GRN is attributed to the occurrence of statistically significant subgraphs, called motifs. While Wireless Sensor Network (WSN) topologies designed using GRN graphs, called bio-WSNs, have been proven to exhibit significant improvement in packet delivery and network latency over random graph-based WSNs, it is still not clear what role motifs play in the observed performance improvement of bio-WSNs. This work explores why a dominant 3-node motif, called Feed Forward Loop (FFL), typifies the robustness of GRN motifs. We also employ graph centrality metrics to corroborate biological studies that have shown motifs to provide pathways for signal propagation in GRNs. Finally, we perform graph-theoretic and simulation experiments on GRN subgraphs and their corresponding bio-WSNs to demonstrate that nodes with high FFL motif participation offer multiple short and robust communication pathways, despite the failure of random and targeted nodes and links.

Meeting Name

2017 IEEE International Conference on Communications, ICC 2017 (2017: May 21-25, Paris, France)


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research


This work is partially supported by NSF grants under award numbers IIS-1404673, CCF-1533918 and IIP1648907.

Keywords and Phrases

Feed Forward Loop; Independent Path; Network Efficiency; Node Motif based Centrality; Robustness

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2017