Stochastic Turing patterns in the development of a one-dimensional organism

Cells having the same genetic information can behave very differently, due to inevitable

stochastic fluctuations in gene expression, known as noise. How do cells in multicellular

organisms achieve high precision in their developmental fate in the presence of noise, in

order to reap the benefits of division of labor? We address this fundamental question

from Systems Biology and Statistical Physics perspectives, with Anabaena cyanobacterial

filaments as a model system, one of the earliest examples of multicellular organisms in

Nature. These filaments can form one-dimensional, nearly-regular patterns of cells of

two types. The developmental program uses tightly regulated, non-linear processes that

include activation, inhibition, and transport, in order to create spatial and temporal

patterns of gene expression that we can follow in real time, at the level of individual

cells. We study cellular decisions, properties of the genetic network behind pattern

formation, and establish the spatial extent to which gene expression is correlated along

filaments. Motivated by our experimental results, I will show that pattern formation in

Anabaena can be described theoretically by a minimal, three-component model that

exhibits a deterministic, diffusion-driven Turing instability in a small region of parameter

space. Furthermore, I will discuss how noise can enhance considerably the robustness of

the developmental program, by promoting the formation of stochastic patterns in

regions of parameter space for which deterministic patterns do not form, suggesting a

novel, much more robust mechanism for pattern formation in this and other systems.