ReviewPlanta2026
AI-driven multiscale virtual plant cell modeling: from molecular mechanisms to tissue functions.
Review in Planta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
MAIN
conclusionAI-driven multiscale virtual plant cell modeling represents a paradigm shift in plant systems biology, enabling predictive simulation from molecular mechanisms to tissue functions and accelerating the engineering of climate-resilient crops. AI-driven multiscale virtual plant cell modeling is emerging as a pivotal paradigm for deciphering complex biological processes in plants. By integrating dynamic processes across molecular, subcellular, and tissue scales, this framework enables systematic simulation from protein interaction prediction to emergent tissue functions, significantly enhancing our understanding of plant environmental responses and developmental mechanisms. This review comprehensively summarizes key technological advances in multiscale modeling, including neural network-assisted molecular interaction prediction, virtual plant tissue simulator construction, deep vision-based 3D reconstruction techniques, and cross-scale dynamic coupling algorithms. It highlights the application value of generative adversarial networks (GANs), transfer learning, and multi-omics integration strategies in addressing data scarcity and cross-species modeling challenges. The review also discusses validation methodologies such as in vitro experimental verification, evolutionary conservation analysis, and uncertainty quantification. In applied contexts, multiscale modeling offers novel insights for plant metabolic engineering, developmental programming simulation, and stress response prediction, while identifying current bottlenecks in parameter transfer accuracy, model interpretability, and computational efficiency. Future directions, including quantum computing-enabled real-time simulation, agricultural digital twin systems, and brain-inspired autonomous models, are explored. The central role of AI technologies in transitioning plant systems biology from descriptive to predictive and engineering-oriented paradigms is emphasized.
Indexed as
Identifiers
41776108What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.