Moschou, Panagiotis Nikolaou
- Department of Molecular Sciences, Swedish University of Agricultural Sciences
- Foundation for Research and Technology - Hellas (FORTH)
- University of Crete
Abstract
Plants exhibit remarkable abilities to learn, communicate, memorize, and develop stimulus-dependent decision-making circuits. Unlike animals, plant memory is uniquely rooted in cellular, molecular, and biochemical networks, lacking specialized organs for these functions. Consequently, plants can effectively learn and respond to diverse challenges, becoming used to recurring signals. Artificial intelligence (AI) and machine learning (ML) represent the new frontiers of biological sciences, offering the potential to predict crop behavior under environmental stresses associated with climate change. Epigenetic mechanisms, serving as the foundational blueprints of plant memory, are crucial in regulating plant adaptation to environmental stimuli. They achieve this adaptation by modulating chromatin structure and accessibility, which contribute to gene expression regulation and allow plants to adapt dynamically to changing environmental conditions. In this review, we describe novel methods and approaches in AI and ML to elucidate how plant memory occurs in response to environmental stimuli and priming mechanisms. Furthermore, we explore innovative strategies exploiting transgenerational memory for plant breeding to develop crops resilient to multiple stresses. In this context, AI and ML can aid in integrating and analyzing epigenetic data of plant stress responses to optimize the training of the parental plants.
Keywords
deep learning; DNA methylation; gene expression; machine learning; stress memory; transgenerational inheritance
Published in
Journal of Integrative Plant Biology
2025
Publisher: WILEY
SLU Authors
UKÄ Subject classification
Botany
Publication identifier
- DOI: https://doi.org/10.1111/jipb.13953
Permanent link to this page (URI)
https://res.slu.se/id/publ/142943