The regulation of plant growth is a highly complex, dynamic process that is influenced by a combination of molecular, environmental, and developmental factors. Recent progress in high-throughput genomics, transcriptomics, proteomics, metabolomics, network biology, and artificial intelligence has shifted plant growth research from isolated pathway descriptions toward integrated and predictive systems-level frameworks. These advances now allow hormone signaling, gene regulatory networks, protein interactions, metabolic reprogramming, and environmental variables to be analyzed together rather than as separate biological processes. Despite considerable progress, understanding how these molecular networks interact and how plant systems as a whole coordinate growth and stress adaptation remains challenging. In this review, we synthesize recent advances in plant growth regulation with particular emphasis on how multi-omics integration, phytohormonal crosstalk, gene regulatory network architecture, and computational modeling are reshaping plant growth as a systems-level trait. Rather than treating phytohormones, molecular pathways, and environmental responses as separate topics, we examine how their interactions generate context-dependent developmental outcomes across tissues and stress conditions. Furthermore, we discuss how recent progress in systems biology, network inference, and machine learning has shifted the field from descriptive cataloging toward predictive modeling of plant behavior. We also highlight emerging strategies for crop improvement and climate resilience that combine multi-omics datasets with interpretable computational frameworks to prioritize candidate regulators and forecast stress responses. By bridging the gap between molecular biology and computational modeling, this review argues for a transition from linear pathway descriptions to mechanistically connected, multiscale models of plant development. Ultimately, these systems-based approaches are vital for advancing crop improvement efforts to address changing global conditions.
Zaman et al. (Fri,) studied this question.