Understanding Pattern Formation in Embryos: Experiment, Theory, and Simulation

Twenty-five years ago, Lewis Wolpert, the eminent developmental biologist, asked the question, “Do We Understand Development?” He concluded that such rapid progress had been made in the preceding two decades that “It is not unreasonable to think that enough will eventually be known to program a computer and simulate some aspects of development.” This prediction has been fulfilled, at least partially, with data-driven simulations of several different developmental processes being developed in the intervening years. Nevertheless, the question remains of whether we “understand” development and if simulations are sufficient to provide an explanation of development. While in silico replications and models are undoubtedly an important tool in the investigation and dissection of developmental processes, which complement traditional experimental methods, these need to be supplemented by theory that identifies principles and provides coherent explanations. Here, I use the example of pattern formation in the vertebrate neural tube to illustrate this idea.

the problem of pattern formation becomes one of understanding how spatial patterns of gene expression arise in a tissue.

Spatially restricted gene expression along the dorsal–ventral axis of the neural tube divides neural progenitors into molecular distinct domains. Each domain expresses a unique combination of transcription factors in response to gradients of extrinsic signals (Shh, BMP) emanating from the ventral and dorsal poles of the neuroepithelium. Moreover, each progenitor domain gives rise to a distinct set of molecular and functional distinguishable neuronal subtypes. BMP, bone morphogenetic protein; Shh, Sonic Hedgehog.

How do cells interpret the combination of Shh and BMP signaling to minimize patterning errors? Strikingly, a computational screen indicated that extending the cross-repressive interaction motifs could explain the behavior (Zagorski et al., 2017). A transcriptional network, composed of three TFs connected by genetic toggle switches and regulated by Shh and BMP signaling, produced dynamics of gene expression that replicated the observed temporal patterns of gene expression in the neural tube.

a GRN controlled by Shh and BMP signaling is able to establish accurately and then maintain gene expression within the developing neural tube.

cis-regulatory elements represent the means by which TF inputs are integrated to control gene expression.

The properties of the GRN cannot be ascribed to individual genes and cannot readily be determined by simply inspecting the network. Instead, they are a consequence of the interactions between the components, and dynamical systems theory offers a way to combine structure and process to explain mechanism.