Moschou, Panagiotis Nikolaou
- Institutionen för molekylära vetenskaper, Sveriges lantbruksuniversitet
- Foundation for Research and Technology - Hellas (FORTH)
- Πανεπιστήμιο Κρήτης
Sammanfattning
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.
Nyckelord
deep learning; DNA methylation; gene expression; machine learning; stress memory; transgenerational inheritance
Publicerad i
Journal of Integrative Plant Biology
2025
Utgivare: WILEY
SLU författare
UKÄ forskningsämne
Botanik
Publikationens identifierare
- DOI: https://doi.org/10.1111/jipb.13953
Permanent länk till denna sida (URI)
https://res.slu.se/id/publ/142943