Contribution of canonical feed-forward loop motifs on the fault-tolerance and information transport efficiency of transcriptional regulatory networks
Motifs and degree distribution in transcriptional regulatory networks play an important role towards their fault-tolerance and efficient information transport. In this paper, we designed an innovative in silico canonical feed-forward loop motif knockout experiment in the transcriptional regulatory network of E. coli to assess their impact on the following five topological features: average shortest path, diameter, closeness centrality, global and local clustering coefficients. Additional experiments were conducted to assess the effects of such motif abundance on E. colis resilience to nodal failures and the end-to-end transmission delay. The purpose of this study is two-fold: (i) motivate the design of more accurate transcriptional network growing algorithms that can produce similar degree and motif distributions as observed in real biological networks and (ii) design more efficient bio-inspired wireless sensor network topologies that can inherit the robust information transport properties of biological networks. Specifically, we observed that canonical feed forward loops demonstrate a strong negative correlation with the average shortest path, diameter and closeness centralities while they show a strong positive correlation with the average local clustering coefficient. Moreover, we observed that such motifs seem to be evenly distributed in the transcriptional regulatory network; however, the direct edges of multiple such motifs seem to be stitched together to facilitate shortest path based routing in such networks.
feed-forward loop shortest path centrality clustering coefficient graph randomization