Computational biology blends mechanistic models with data-driven AI. In this survey, we focus on plant systems, with occasional pointers to human biology that were useful. We review four advances, organ-level growth models that allocate resources among leaves, stems, and fruit (e.g., GreenLab), whole-tree models linking physiology and form (e.g., L-PEACH), plant microbe models for nitrogen fixation (e.g., AMIGO), and AI workflows from high-throughput phenotyping to protein-structure prediction (e.g., AlphaFold). For each topic, we outline the core equations in plain terms, state key assumptions and identifiability limits, and give practical validation steps using open datasets, standard metrics, and simple ablations. We also clearly separate the survey from a forward-looking idea mimicking plant metabolism, which we present as future work, not a finished result. We end with concrete next steps: calibrating across scales, quantifying uncertainty, testing models across species and sites, and sharing reproducible pipelines that connect simulations with AI. Our aim is a tighter, clearer guide for both method builders and applied researchers. To bridge plant science and computer science, we suggest modeling plant metabolism mathematically. Such models mirror biochemical pathways, deepen biological insight, and open new avenues for algorithm design in computational biology. We argue that the convergence of computational biology and AI will advance plant science and, more broadly, benefit medicine, ecology, and environmental science. This paper summarizes the current state of the field and outlines a practical path for future work
Gul et al. (Thu,) studied this question.