ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Grafting has been practiced for millennia to combine the best characteristics of two plants. Despite recent molecular discoveries that gain insight into plant grafting, the systematic characterization of its underlying mechanisms is still lacking. Here, we take a step toward filling this gap by developing a generalized statistical mechanics model to decode genomic crosstalk between the scion and rootstock. Instead of traditional objectives of identifying individual genes that are differentially expressed between the two organs, our model codes thousands of interactive genes into informative, dynamic, omnidirectional, and personalized networks (idopNetworks) that program and rewire scion-rootstock crosstalk. We design an experiment of reciprocally micrografting young tissues to validate the application of idopNetworks to the genomic characterization of graft formation between two distantly related Populus species. Given its capacity to reveal the most comprehensive genomic underpinnings for proper interactions of the scion with rootstock to develop new plants, the idopNetworks model can be extended for the mechanistic exploration of a wide range of biological, evolutionary, and medical phenomena.
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