Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise

Kun Xiong, Alex K. Lancaster, Mark L. Siegal and Joanna Masel ()
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Kun Xiong: University of Arizona
Alex K. Lancaster: Ronin Institute
Mark L. Siegal: New York University
Joanna Masel: University of Arizona

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract In transcriptional regulatory networks (TRNs), a canonical 3-node feed-forward loop (FFL) is hypothesized to evolve to filter out short spurious signals. We test this adaptive hypothesis against a novel null evolutionary model. Our mutational model captures the intrinsically high prevalence of weak affinity transcription factor binding sites. We also capture stochasticity and delays in gene expression that distort external signals and intrinsically generate noise. Functional FFLs evolve readily under selection for the hypothesized function but not in negative controls. Interestingly, a 4-node “diamond” motif also emerges as a short spurious signal filter. The diamond uses expression dynamics rather than path length to provide fast and slow pathways. When there is no idealized external spurious signal to filter out, but only internally generated noise, only the diamond and not the FFL evolves. While our results support the adaptive hypothesis, we also show that non-adaptive factors, including the intrinsic expression dynamics, matter.

Date: 2019
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DOI: 10.1038/s41467-019-10388-6

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