Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model’s predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy.

心脏肥大是一种与环境有关的现象，其中无数的生物化学和生物力学因素通过复杂的大规模信号网络调节心肌的生长。尽管许多研究已经研究了肥大性信号传导途径，但对于肥大性信号传导作为一个整体网络以及该网络如何以上下文相关的方式起作用的了解较少。在这里，我们开发了一种系统化的方法，CLASSED（基于上下文的逻辑基础的信令网络开发），用于基于上下文特定的数据修订大规模信令模型，并识别主要的反应和调节上下文特定的响应的新串扰。CLASSED包含四个连续阶段，其中以自动验证模块为核心，该模块从交互图构建基于逻辑的ODE模型并输出模型验证百分比。通过估计默认参数，分类定性验证，混合Morris-Sobol全局灵敏度分析以及发现缺失的上下文相关串扰来开发特定于上下文的模型。将此管道应用于具有上下文特定数据的我们的先前知识肥大网络中，发现了关键的信号传导反应，这些反应明显地调节了细胞对异丙肾上腺素，去氧肾上腺素，血管紧张素II和伸展的反应。此外，通过CLASSED，我们开发了针对特定背景的β-肾上腺素能心脏肥大模型。该模型预测了在ISO特定情况下钙/钙调蛋白依赖性途径与Ras上游信号传导之间的新串扰。心肌细胞实验验证了该模型对CaMKII-Gβγ和CaN-Gβγ相互作用在ISO特定背景下介导肥大信号中的作用的预测，并揭示了心肌细胞和成纤维细胞之间ERK1 / 2的磷酸化程度和易位性的差异。CLASSED是一种用于开发特定于上下文的大规模信号网络的系统方法，可深入了解β-肾上腺素能心脏肥大中新发现的串扰。