Quantitative guiding of developmental cell fate patterns using a dynamical landscape model.

Authors

Ismail Hajji, Francis Corson, Wolfgang Keil

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 30. Pages e2521973123. Jul 28, 2026. Epub Jul 20, 2026.

Abstract

During development, cells gradually assume specialized fates via changes of transcriptional dynamics in thousands of genes. Landscape modeling approaches, which abstract from the underlying gene regulatory networks and reason in a low-dimensional phenotypic space, have been remarkably successful in explaining terminal fate outcomes. The success of these models also prompts their application toward inferring dynamic perturbations of multicellular patterning that alter cell fate outcomes in predictable ways, a task that is otherwise highly challenging due to the complex dynamics of the underlying gene circuits. Here, we accomplish this task by combining a landscape model for Caenorhabditis elegans vulval fate patterning with temporally controlled perturbations of EGF and Notch signaling in vivo using temperature-sensitive mutant alleles. We find that nonintuitive fate outcomes that emerge in combinations of these alleles at static temperature conditions through pathway epistasis are correctly predicted by the model. We then show that short pulses of signaling in these genetic backgrounds, delivered via temperature shifts, can be used to guide both the fraction of induced precursor cells and the specific fates they adopt with quantitative precision. Analysis of the underlying cellular landscapes indicates that cell fate guidance via pulses of signaling effectively redesigns the decision structure into one that has no equivalent in normal development, namely, a conversion of the three-way cell fate decision topology into two sequential binary fate decisions. Our results highlight the predictive power of landscape models and illustrate a method to quantitatively guide cell fate acquisition in a developmental context.

PMID:
42475567
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

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