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

## TL;DR

This paper introduces a new statistical mechanics model to understand how plant grafts form by analyzing gene interactions between the scion and rootstock.

## Contribution

The novel idopNetworks model captures dynamic, omnidirectional gene interactions during graft formation.

## Key findings

- idopNetworks reveal hub genes critical for vascular reconnection and graft compatibility.
- The model successfully characterizes genomic crosstalk in grafts between two Populus species.
- The framework can be applied to study broader biological and evolutionary phenomena.

## 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.

We introduce a statistical mechanics framework to decode the genomic crosstalk governing plant grafting. By integrating evolutionary game theory with transcriptomics, we reconstruct idopNetworks (informative, dynamic, omnidirectional, and personalized networks) that map scion–rootstock interactions. This framework identifies hub genes driving vascular reconnection and compatibility, offering a unified computational strategy for dissecting complex tissue communication mechanisms.