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  • Open Access

Nonlocal Decoding of Positional and Correlational Information during Development

Alex Chen Yi Zhang1,*, Pablo Mateu Hoyos1, David Brückner2,†, and Gašper Tkačik1,‡

  • *Contact author: alexchenyi.zhang@ist.ac.at
  • †Contact author: d.b.brueckner@unibas.ch
  • ‡Contact author: gtkacik@ist.ac.at
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Phys. Rev. Lett. 137, 038401 – Published 15 July, 2026

DOI: https://doi.org/10.1103/mbjk-v4ym

Abstract

In many developmental systems, cells differentiate into a tissue by reading out morphogen concentration fields, a process fundamentally limited by noise. How much can the precision of this process be improved by nonlocal information, e.g., via cell-cell communication? Using a Bayes-optimal framework, we show that positional inference depends crucially on morphogen spatial correlations and on the “structural prior” that encodes the geometry of the cellular lattice performing the readout, thereby determining what a cell can reliably assume about the position of its neighbors when interpreting nonlocal morphogen signals. We derive upper bounds on positional information gain due to nonlocal readout and identify signal processing algorithms that approximate optimal positional inference, as well as simple chemical reaction schemes which implement such algorithms. Our theory suggests that correlational information can be exploited to significantly enhance developmental precision.

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Information

Phys. Rev. Lett. 137, 038401– Published 15 July, 2026
  • Received 16 January 2026
  • Accepted 12 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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