Integrative Systems Biology and Multi-Omics Approaches for Decoding Plant Stress Resilience

Plants face a wide range of environmental stresses — including drought, salinity, temperature extremes, and pathogen attack — that severely impact agricultural productivity and threaten global food security. Traditional reductionist approaches have identified many of the individual genes and signaling pathways involved in stress responses, but consistently fall short of capturing the complex, multilayered nature of these biological systems.Recent advances in systems biology, combined with the integration of multi-omics data (genomics, transcriptomics, proteomics, metabolomics, and epigenomics), now offer the resolution needed to understand how cellular networks dynamically reorganise under stress. Critically, the emergence of machine learning, graph neural networks, and genome-scale metabolic modelling has opened new possibilities for reconstructing regulatory networks, predicting phenotypic outcomes, and identifying the key molecular nodes driving stress tolerance — capabilities that move the field decisively beyond descriptive cataloguing.Despite this progress, substantial challenges remain: integrating heterogeneous multi-omics datasets, building models with genuine predictive power, and translating computational insights into actionable biological understanding. This Research Topic addresses these challenges directly, bringing together computational biologists, bioinformaticians, and molecular plant scientists around the shared goal of a systems-level understanding of plant stress.We welcome submissions addressing, but not limited to, the following themes:• Dynamic gene regulatory network (dGRN) modelling and temporal reconstruction under abiotic and biotic stress — capturing the molecular rewiring that occurs at stress onset versus long-term acclimation• Machine learning, graph neural networks (GNNs), and deep learning for predictive network analysis — identifying hub genes, key transcription factors, and cross-tolerance mechanisms from large-scale multi-omics data• Flux balance analysis and multiscale metabolic modelling — simulating how cellular metabolic fluxes shift under multi-stress conditions, from single-cell to whole-tissue resolution• Multi-omics data integration for deciphering stress signaling pathways — combining genomics, transcriptomics, proteomics, and metabolomics to map regulatory crosstalk and adaptive networks• Epigenetic and epitranscriptomic regulation of plant stress responses — chromatin accessibility (ATAC-seq), histone modifications, and their role in governing metabolic and transcriptional plasticity• Bioinformatics tools and data integration pipelines — novel computational frameworks and software designed specifically for multi-omics systems biology analyses in plant stress contexts• Regulatory network reconstruction and identification of key molecular players — frameworks for mapping stress-responsive networks and validating functional nodes driving toleranceArticle types: Original Research, Review, and Perspectives Topic editors: Muhammad Sajid, Pannaga Pavan JUTUR, Faisal Islam Source: https://www.frontiersin.org/research-topics/82056/integrative-systems-biology-and-multi-omics-approaches-for-decoding-plant-stress-resilience