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Evolved biological network topologies may resist perturbances to allow for more robust information transport across larger networks in which their network motifs may play a complex role. Although the abundance of individual motifs correlate with some metrics of biological robustness, the extent to which redundant regulatory interactions affect motif connectivity and how this connectivity affects robustness is unknown. To address this problem, we applied machine learning based regression modeling to evaluate how feed-forward loops interlinked by crosstalk altered information transport across a network in terms of packets successfully routed over networks of noisy channels via NS-2 simulation. The sample networks were extracted from the complete transcriptional regulatory networks of two well-studied bacteria: E.coli and Yeast. We developed 233 topological features for the E.coli subnetworks and 842 topological features for the Yeast subnetworks which distinctly account for the opportunities in which two feed-forward loops may exhibit crosstalk. Random forest regression modeling was used to infer significant features from this modest configuration space. The coefficient of determination was used as a primary performance metric to rank features within our regression models. Although only a handful of features were highly ranked, we observed that, in particular, feed-forward loop crosstalk patterns correlated substantially with an improved chance for successful information transmission. Additionally, both E.coli and Yeast subnetworks demonstrate very similar FFL crosstalk patterns that were considered significant in their contribution to information transport robustness in these two organisms. This finding may potentially allude to common design principles in transcriptomic networks from different organisms.

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Correction to: Similar Feed-forward Loop Crosstalk Patterns may Impact Robust Information Transport Across E. coli and S. Cerevisiae Transcriptional Networks