Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated. We integrated and processed hundreds of disparate observations regarding ABA signal transduction responses underlying stomatal closure into a network of 84 nodes and 156 edges and, as a result, established those relationships, including identification of a 36-node, strongly connected (feedback-rich) component as well as its in- and out-components. The network’s domination by a feedback-rich component may reflect a general feature of rapid signaling events. We developed a discrete dynamic model of this network and elucidated the effects of ABA plus knockout or constitutive activity of 79 nodes on both the outcome of the system (closure) and the status of all internal nodes. The model, with more than 10²⁴ system states, is far from fully determined by the available data, yet model results agree with existing experiments in 82 cases and disagree in only 17 cases, a validation rate of 75%. Our results reveal nodes that could be engineered to impact stomatal closure in a controlled fashion and also provide over 140 novel predictions for which experimental data are currently lacking. Noting the paucity of wet-bench data regarding combinatorial effects of ABA and internal node activation, we experimentally confirmed several predictions of the model with regard to reactive oxygen species, cytosolic Ca²⁺ (Ca²⁺_c), and heterotrimeric G-protein signaling. We analyzed dynamics-determining positive and negative feedback loops, thereby elucidating the attractor (dynamic behavior) repertoire of the system and the groups of nodes that determine each attractor. Based on this analysis, we predict the likely presence of a previously unrecognized feedback mechanism dependent on Ca²⁺_c. This mechanism would provide model agreement with 10 additional experimental observations, for a validation rate of 85%. Our research underscores the importance of feedback regulation in generating robust and adaptable biological responses. The high validation rate of our model illustrates the advantages of discrete dynamic modeling for complex, nonlinear systems common in biology.

气孔是叶子表面的微小孔，水分流失和二氧化碳吸收通过气孔发生，植物激素脱落酸 (ABA) 会因干旱而关闭。这个过程对于耐旱性至关重要，并且是过去几十年来广泛实验研究的主题。尽管已经阐明了由 ABA 与受体结合组成的核心信号链，它减轻了蛋白激酶 OPEN STOMATA 1 (OST1) 的蛋白磷酸酶 2C (PP2C) 的负调节，并最终导致阴离子通道激活、渗透水损失和关于气孔关闭，已经确定了 70 多个附加组件，但它们之间的关系以及它们之间的关系以及核心组件的阐明却很少。我们将气孔关闭背后的 ABA 信号转导反应的数百个不同观察结果整合并处理到一个由 84 个节点和 156 个边组成的网络中，最终建立了这些关系，包括识别 36 个节点、强连接（反馈丰富）组件及其输入和输出组件。反馈丰富的组件对网络的控制可能反映了快速信号事件的一般特征。我们开发了该网络的离散动态模型，并阐明了 ABA 加敲除或 79 个节点的本构活动对系统结果（闭合）和所有内部节点状态的影响。该模型具有超过 10 ^{24 种}系统状态，远未完全由现有数据确定，但模型结果在 82 个案例中与现有实验一致，只有 17 个案例与现有实验不一致，验证率为 75%。 我们的结果揭示了可以设计为以受控方式影响气孔关闭的节点，并提供了 140 多种目前缺乏实验数据的新颖预测。注意到关于 ABA 和内部节点激活的组合效应的湿台数据的缺乏，我们通过实验证实了模型关于活性氧、胞质 Ca ²⁺ (Ca ²⁺ _c ) 和异三聚体 G 蛋白信号传导的几个预测。我们分析了动态决定的正反馈循环和负反馈循环，从而阐明了系统的吸引子（动态行为）库以及决定每个吸引子的节点组。基于此分析，我们预测可能存在先前未被识别的依赖于 Ca ²⁺ _{c 的}反馈机制。该机制将提供与 10 个额外实验观察结果一致的模型，验证率为 85%。我们的研究强调了反馈调节在产生强大且适应性强的生物反应中的重要性。我们模型的高验证率说明了对生物学中常见的复杂非线性系统进行离散动态建模的优势。