Research Article
Correlating In silico Feed-forward Loop Knockout Experiments with the Topological Features of Transcriptional Regulatory Networks
@INPROCEEDINGS{10.4108/icst.bict.2014.257916, author={Ahmed Abdelzaher and Michael Mayo and Preetam Ghosh and Edward Perkins}, title={Correlating In silico Feed-forward Loop Knockout Experiments with the Topological Features of Transcriptional Regulatory Networks}, proceedings={8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)}, publisher={ICST}, proceedings_a={BICT}, year={2015}, month={2}, keywords={shortest path centrality graph randomization clustering coefficient feed-forward loop}, doi={10.4108/icst.bict.2014.257916} }- Ahmed Abdelzaher
Michael Mayo
Preetam Ghosh
Edward Perkins
Year: 2015
Correlating In silico Feed-forward Loop Knockout Experiments with the Topological Features of Transcriptional Regulatory Networks
BICT
ACM
DOI: 10.4108/icst.bict.2014.257916
Abstract
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 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.