Abstract
Transcriptional regulatory networks (TRNs) are enriched for certain network motifs. This could either be the result of natural selection for particular hypothesized functions of those motifs, or it could be a byproduct of mutation (e.g. of the prevalence of gene duplication) and of less specific forms of selection. We have developed a powerful new method for distinguishing between adaptive vs. non-adaptive causes, by simulating TRN evolution under different conditions. We simulate mutations to transcription factor binding sites in enough mechanistic detail to capture the high prevalence of weak-affinity binding sites, which can complicate the scoring of motifs. Our simulation of gene expression is also highly mechanistic, capturing stochasticity and delays in gene expression that distort external signals and intrinsically generate noise. We use the model to study a well-known motif, the type 1 coherent feed-forward loop (C1-FFL), which is hypothesized to filter out short spurious signals. We found that functional C1-FFLs evolve readily in TRNs under selection for this function, but not in a variety of negative controls. Interestingly, a new “diamond” motif also emerged as a short spurious signal filter. Like the C1-FFL, the diamond integrates information from a fast pathway and a slow pathway, but their speeds are based on gene expression dynamics rather than topology. When there is no external spurious signal to filter out, but only internally generated noise, only the diamond and not the C1-FFL evolves.
Author Summary Frequently occurring motifs are thought to be fundamental building blocks of biological networks, conducting specific functions. However, we still lack definitive evidence that these motifs have evolved “adaptively” (to perform the particular function proposed for them), rather than “non-adaptively” (as byproducts of some other function, or as an artifact of patterns of mutations). Here we develop a powerful null model that captures important non-adaptive factors that can shape the evolution of transcriptional regulatory networks, and use it to provide the missing piece of evidence of adaptive origin in the case of the most studied motif, a feed-forward loop that is hypothesized to filter out short spurious signals. We also find evidence for an alternative solution to this problem, where the functionality of the feed-forward loop is encoded not in network topology, but in the dynamics of gene expression. Our model is suitable for studying whether other network features have evolved adaptively vs. non-adaptively.