Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks

We examine and compare transcriptional networks extracted from the bacterium Escherichia coli and the baker's yeast Saccharomyces cerevisiae using discrete event simulation based in silico experiments. The packet receipt rate is used as a dynamical metric to understand information flow, while machine learning techniques are used to examine underlying relationships inherent to the network topology. To this effect, we defined sixteen features based on structural/topological significance, such as transcriptional motifs, and other traditional metrics, such as network density and average shortest path, among others. Support vector classification is carried out using these features after parameters were identified using a cross-validation grid-search method. Feature ranking is performed using analysis of variance F-value metric. We found that feed-forward loop based features rank consistently high in both the bacterial and yeast networks, even at different perturbation levels. This work paves the way to design specialized engineered systems, such as wireless sensor networks, that exploit topological properties of natural networks to attain maximum efficiency.