Human GRN blocking peptide

Developmental gene regulatory networks (GRNs) underpin metazoan embryogenesis and have undergone substantial modification to generate the tremendous variety of animal forms present on Earth today. The nematode Caenorhabditis elegans has been a central model for advancing many important discoveries in fundamental mechanistic biology and, more recently, has provided a strong base from which to explore the evolutionary diversification of GRN architecture and developmental processes in other species. In this short review, we will focus on evolutionary diversification of the GRN for the most ancient of the embryonic germ layers, the endoderm. Early embryogenesis diverges considerably across the phylum Nematoda. Notably, while some species deploy regulative development, more derived species, such as C. elegans, exhibit largely mosaic modes of embryogenesis. Despite the relatively similar morphology of the nematode gut across species, widespread variation has been observed in the signaling inputs that initiate the endoderm GRN, an exemplar of developmental system drift (DSD). We will explore how genetic variation in the endoderm GRN helps to drive DSD at both inter- and intraspecies levels, thereby resulting in a robust developmental system. Comparative studies using divergent nematodes promise to unveil the genetic mechanisms controlling developmental plasticity and provide a paradigm for the principles governing evolutionary modification of an embryonic GRN.

Models of gene regulatory networks (GRN) have proven useful for understanding many aspects of the highly complex behavior of biological control networks. Randomly generated non-Boolean networks were used in experimental simulations to generate data on dynamic phenotypes as a function of several genotypic parameters. We found that predictive relationships between some phenotypes and quantitative genotypic parameters such as number of network genes, interaction density, and initial condition could be derived depending on the strength of the topological (positional) genotype on specific phenotypes. We quantitated the strength of the topological genotype effect (TGE) on a number of phenotypes in multi-gene networks. For phenotypes with a low influence of topological genotype, derived and empirical relationships using quantitative genotype parameters were accurate in phenotypic outcomes. We found a number of dynamic network properties, including oscillation behaviors, that were largely dependent on genotype topology, and for which no such general quantitative relationships were determinable. It remains to be determined if these results are applicable to biological gene regulatory networks.