Engineering principles in plant metabolism: integrating control, mechanics, and transport under dynamic environments.

Plant responses to dynamic environmental conditions remain challenging to describe within a unified mechanistic framework. This is largely because metabolism operates under simultaneous and interacting regulatory, mechanical, and transport constraints. Here, we synthesize three engineering perspectives as complementary frameworks to quantify these constraints on plant metabolism: (i) control engineering, for dynamic regulation of the biochemical pathways; (ii) structural and mechanical engineering, to quantify load-bearing constraints and geometry-dependent scaling relationships in plant tissues; and (iii) fluid dynamics, which describes xylem and phloem transport as flow through networks under variable demand. For each domain, we present plant-specific case studies demonstrating how engineering analysis can reveal constraints and dynamic relationships that are difficult to extract from descriptive approaches alone, and we explicitly distinguish quantitatively validated examples from instructive structural analogies. Photosynthetic carbon fixation under fluctuating conditions serves as a recurring example, as it simultaneously involves feedback regulation, mechanical stomata control, and hydraulic water supply. To overcome the limitations of purely mechanistic models as system complexity increases, we discuss emerging hybrid modeling strategies that combine mechanistic understanding with machine learning. These approaches facilitate parameterization and cross-scale integration, providing a foundation for future predictive digital twins of plant metabolism. Finally, we present a strengths, weaknesses, opportunities, and threats analysis of key limitations arising from nonlinearity, heterogeneity, and measurement constraints, and conclude by outlining open experimental and modeling questions at the interface of plant metabolism and engineering. We aim to help plant biologists translate their biological questions into predictive, multi-scale models and, ultimately, digital representations of plant metabolism.