An Integrated computational & mammalian synthetic biology framework for engineering turing patterns

Turing patterns are thought to underlie the formation of many tissue structures during development. First proposed by Alan Turing, two key properties are known to affect the formation of patterns: the fine balance of the interaction parameters and the differential diffusion of an activating and inhibiting species. Despite many experimental examples, it has been difficult to determine if Turing patterns alone suffice to generate patterns in vivo. Engineering Turing patterns, in the absence of any endogenous patterning networks, may shed light on how they function in nature. Here I combined a computational and mammalian synthetic biology approach into a unified framework for engineering Turing patterns. Through theoretical analysis of a wide range of networks (15,514 networks with two and three interacting species), I found that ≈60% of all networks tested can achieve Turing patterns. However, features beyond structure determine a network's Turing patterning capability. Even though individual networks with three interacting molecules are more likely to form Turing patterns than two species networks, the parameter space always remains very limited (