Correlating In silico Feed-forward Loop Knockout Experiments with the Topological Features of Transcriptional Regulatory Networks

Abstract

Motifs and degree distribution in transcriptional regulatory networks play an important role towards their faulttolerance and efficient information transport. In this paper, we designed an innovative in silico feed-forward loop motif knockout experiment to assess their impact on the following six topological features: average shortest path, diameter, closeness centrality, betweenness centrality, global and local clustering coefficients. The experiments were conducted on the transcriptional regulatory network of E. coli. 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.