Integrative Genomics, Data Science, and Systems Modeling for Plant Resilience in Changing Environments

Plant resilience research increasingly examines how plants adapt to interacting abiotic and biotic stresses in changing environments. Climate change is producing more frequent combinations of heat, drought, salinity, flooding and pathogens, challenging conventional approaches to studying plant performance. Advances in genomics, transcriptomics, proteomics, metabolomics, epigenomics, phenomics and environmental sensing have generated extensive datasets, but these are often produced at different scales and analyzed separately. Recent studies indicate that integrating multi-omics, phenotypic and environmental information can reveal regulatory networks, stress-response pathways and biomarkers associated with adaptation. Machine learning, artificial intelligence, network inference and systems modeling offer further opportunities to predict genotype–phenotype–environment interactions. However, challenges remain concerning model interpretability, transferability, causal validation, data interoperability and the translation of computational predictions into breeding strategies. Integrated and experimentally validated frameworks are needed to transform complex datasets into actionable knowledge for developing climate-resilient plants.This Research Topic aims to advance interdisciplinary approaches for explaining and predicting plant resilience across molecular, organismal and ecosystem scales. It will promote studies integrating biological, phenotypic and environmental data to identify mechanisms, regulatory networks, biomarkers and genotype–phenotype–environment relationships underlying plant performance under changing conditions. Contributions may also examine how artificial intelligence, machine learning and systems modeling can support hypothesis generation, predictive breeding and genomic selection. Emphasis will be placed on approaches that are interpretable, reproducible, experimentally validated and relevant to sustainable plant production.To gather further insights into the integration of biological, environmental and computational perspectives on plant resilience, this Research Topic will cover diverse plant systems, stress conditions, data types and analytical scales, with emphasis on mechanistic understanding, predictive capacity and practical relevance. We welcome articles addressing, but not limited to, the following themes:• Integration of genomics, transcriptomics, proteomics, metabolomics, epigenomics, phenomics and enviromics• Artificial intelligence and machine learning for plant stress biology and predictive phenotyping• Gene regulatory, metabolic and signaling network inference• Systems modeling of genotype–phenotype–environment interactions• Integration of climate, environmental and remote-sensing data with biological datasets• Big-data analytics, bioinformatics pipelines and interoperable data infrastructures• Biomarker discovery and validation for plant resilience• Predictive breeding, genomic selection and climate-resilient crop improvement• Explainable artificial intelligence for plant biology• Mechanistic models of plant responses to combined stresses• Open datasets, reproducible workflows and FAIR data practicesThe Research Topic welcomes original research articles, reviews, systematic reviews, methods, perspectives and data reports. Topic editors: Andrés J. Cortés, Felipe López-Hernández Source: https://www.frontiersin.org/research-topics/84400/integrative-genomics-data-science-and-systems-modeling-for-plant-resilience-in-changing-environments