Inference of Morphogen Gradient Precision from Molecular Noise Data

During development, morphogen gradients provide spatial information for tissue patterning. Gradients and readout mechanisms are inevitably variable, yet the resulting patterns are strikingly precise. Measurement limitations currently preclude precise detection of morphogen gradients over long distances. Here, we develop a new formalism to estimate gradient precision along the entire patterning axis from measurements close to the source. Using numerical simulations, we infer gradient variability from measured molecular noise levels in morphogen production, decay, and diffusion. The predicted precision is much higher than previously measured—precise enough to allow even single gradients to define the central progenitor boundaries during neural tube development. Finally, we show that the patterning mechanism is optimized for precise progenitor cell numbers, rather than precise boundary positions, as the progenitor domain size is particularly robust to gradient alterations. We conclude that single gradients can yield the observed developmental precision, which provides new prospects for tissue engineering.