Developing tensor networks to investigate stochastic phenotypes of somite segmentation

Abstract: This research project addresses how developmental processes—specifically zebrafish somite segmentation—maintain robust, synchronized gene expression oscillations despite the inherent randomness, or stochasticity, of cellular activity. By combining developmental biology with tensor network methods—a powerful computational approach from physics—the team aims to solve the chemical master equation to quantify how variability in individual cells and cell-to-cell coupling impact overall developmental precision. Ultimately, the project seeks to construct a “kinetic phase diagram” that illustrates how biological error rates in these developmental clocks are governed by cellular noise, providing new insight into the origins of morphological defects.