Precision of tissue patterning is controlled by dynamical properties of gene regulatory networks.

Katherine Exelby,Edgar Herrera-Delgado,Andreas Sagner,James Briscoe,Lorena Garcia Perez,Peter Sollich,Ruben Perez-Carrasco,Vicki Metzis

doi:10.1242/dev.197566

Abstract

ABSTRACTDuring development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

Highlights

Embryos are characterised by remarkably organised and reproducible patterns of cellular differentiation
We established a deterministic model of the gene regulatory networks (GRNs), based on coupled ordinary differential equations (ODEs), that replicated the response of the network to Sonic Hedgehog (Shh) signalling and the shifts in boundary position in mutant embryos, including Pax6−/− (Panovska-Griffiths et al, 2013; Balaskas et al, 2012; Cohen et al, 2014)
We reasoned that fluctuations in gene expression could result in noise driven transitions within the bistable region from a pMN state to a p3 identity. (For a glossary of dynamical systems terminology see supplementary Materials and Methods) We constructed a stochastic differential equation (SDE) model that retained the parameters of the ODE model but incorporated a description of intrinsic gene expression fluctuations, based on experimental measurements

Summary

Introduction

Embryos are characterised by remarkably organised and reproducible patterns of cellular differentiation An illustration of this accuracy are the sharp boundaries of gene expression observed in many developing tissues. A popular metaphor for the process of developmental pattern formation is the Waddington landscape, in which the differentiation trajectory of a cell is conceived as a ball rolling down a landscape of bifurcating valleys (Waddington, 1957) In this representation, the landscape is shaped by the GRN with the valleys representing cell fates and developmental signals allocating cell identity by determining the valley a cell enters. Cells can be driven out of a valley into an adjacent attractor, producing a change in identity, by developmental signals and by gene expression noise

Methods

Results

Conclusion