Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of , based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO and O, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

中文翻译：

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用于植物细胞表型自主预测的资源分配模型


预测复杂环境条件下植物细胞的反应是植物生物学中的一个挑战。在这里，我们基于资源平衡分析（RBA）约束建模框架，开发了叶片光合细胞的细胞和分子尺度的资源分配模型。 RBA 模型包含涉及植物细胞生长和存活并位于细胞区室中的代谢网络和主要大分子过程。我们模拟了温度、辐照度、CO 和 O 分压等不同环境条件的模型，并将 RBA 预测与已知资源分布和定量表型特征（例如相对生长率、C:N 比）进行比较，最后与经验结果进行比较由完善的 Farquhar 模型给出的 CO 固定特征。与其他基于约束的标准建模方法（例如通量平衡分析）相比，RBA 模型无需经验约束即可做出准确的定量预测。总而言之，我们表明 RBA 显着提高了复杂环境条件下植物细胞表型的自主预测，并提供了植物细胞基因型和表型之间的机制联系。