Robustness and innovation in synthetic genotype networks

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. Here the authors combine construction of over 20 synthetic gene regulatory networks with mathematical modeling to exemplify how gene regulatory networks provide robustness in face of mutations while enabling transitions to innovative phenotypes. Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.

Automated summary

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. The authors combine construction of over 20 synthetic gene regulatory networks with mathematical modeling to exemplify how gene regulatory networks provide robustness in face of mutations while enabling transitions to innovative phenotypes.