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A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.

Ang Dong¹, Yihan Meng^1,2, Stephen Shing-Toung Yau¹

¹Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|January 7, 2026

Summary

This study introduces idopNetworks, a novel model for analyzing plant grafting by decoding genomic crosstalk. This approach reveals comprehensive genetic underpinnings for successful scion-rootstock interactions in developing new plants.

Keywords:

gene regulatory network modeling graft incompatibility plant grafting tissue crosstalk vascular reconnection

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Area of Science:

Plant Biology
Genomics
Systems Biology

Background:

Plant grafting is an ancient technique for combining desirable plant traits.
Current understanding of grafting mechanisms lacks systematic genomic characterization despite molecular advances.

Purpose of the Study:

To develop a generalized statistical mechanics model for decoding genomic crosstalk in plant grafting.
To characterize the genomic mechanisms underlying scion-rootstock interactions.

Main Methods:

Developed a generalized statistical mechanics model coding interactive genes into informative, dynamic, omnidirectional, and personalized networks (idopNetworks).
Designed reciprocal micrografting experiments using two distantly related Populus species.
Applied idopNetworks for genomic characterization of graft formation.

Main Results:

The idopNetworks model successfully characterized genomic crosstalk between scion and rootstock.
Demonstrated the model's ability to reveal comprehensive genomic underpinnings for graft formation.
Validated the application of idopNetworks in distantly related Populus species.

Conclusions:

idopNetworks provide a powerful tool for understanding the genomic basis of plant grafting.
The model offers a comprehensive approach to studying scion-rootstock interactions.
This methodology can be extended to explore diverse biological, evolutionary, and medical phenomena.