Mechanism of biological noise cancellation revealed

Makoto Sato from Kanazawa University and colleagues have now discovered that a particular biochemical signaling pathway contributes to noise cancelling in the differentiation process of neural stem cells -- the self-renewing cells that play an important part in the development of the nervous system of animals during the embryonic stage.

As a model system for understanding cancellation mechanisms of biological noise in a multicellular organism, the researchers looked at a particular development stage of the visual system in fruit flies: the propagation of the so-called 'proneural wave', during which cells of a type called neuroepithelial cell differentiate into neuroblasts (cells in the process of dividing into neurons).

Inspired by earlier research, Sato and colleagues hypothesized that a signaling pathway (a particular chain of reactions between proteins in cells) known as JAK/STAT is involved in noise cancellation. The scientists combined mathematical modeling and genetic experiments. For the former, they designed a set of mathematical equations that quantify the variations in the system and reproduce the progression of the proneural wave. When including JAK/STAT signaling, they found that spontaneous, noise-induced neuroblast differentiation is cancelled.

To confirm the effect of JAK/STAT signaling in vivo, the researchers performed experiments wherein the signaling pathway was reduced in a controlled way. Upon reduction, stochastic differentiation of neuroblasts was observed, consistent with the conclusion that JAK/STAT has a noise-canceling function. Further genetic experiments provided more insights into the details of how noise canceling by JAK/STAT happens, and that this may be a function that has been conserved throughout evolution.

Not only does the work of Sato and colleagues contribute to a better understanding of noise-canceling mechanisms in developmental biology, it also underlines the power of combining 'in silico' with 'in vivo' studies. Quoting the scientists, "by applying a combination of mathematical modeling and molecular genetics, we can solve biological questions that have previously been difficult to address."