Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model
Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model
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DOI：
10.1186/1752-0509-4-157
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发表时间：
2010-11-18
影响因子：
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通讯作者：
Saucerman, Jeffrey J.
中科院分区：
文献类型：
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作者：
Kraeutler, Matthew J.;Soltis, Anthony R.;Saucerman, Jeffrey J.
背景：细胞信号网络的大规模预测建模需要新的方法。虽然质量行动和酶动力学的方法需要大量的生化数据，目前的逻辑为基础的方法主要用于定性预测，并缺乏直接的定量比较与生化models.Results：我们开发了一个基于逻辑的微分方程建模方法的细胞信号网络的基础上归一化希尔激活/抑制功能控制的逻辑AND和OR运营商表征信号串扰。使用这种方法，我们模拟了心脏β（1）-肾上腺素能信号网络，包括36个反应和25个物种。将该模型与同一网络的广泛表征和验证的生化模型进行直接比较，发现新模型即使使用默认参数也能相当准确地预测关键网络属性。与先前基于逻辑的方法相比，归一化希尔函数改进了全局函数关系的定量预测。全面的敏感性分析揭示了PKA负反馈对上游信号传导的重要作用，以及磷酸二酯酶作为网络关键负调节因子的重要性。然后，该模型扩展到最近确定的蛋白质相互作用的数据，涉及整合素介导的mechanodonduction.Conclusions：归一化希尔微分方程建模方法允许定量预测网络的功能关系和动态，即使在系统中有限的生化数据。
Background: New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models.Results: We developed a logic-based differential equation modeling approach for cell signaling networks based on normalized Hill activation/inhibition functions controlled by logical AND and OR operators to characterize signaling crosstalk. Using this approach, we modeled the cardiac beta(1)-adrenergic signaling network, including 36 reactions and 25 species. Direct comparison of this model to an extensively characterized and validated biochemical model of the same network revealed that the new model gave reasonably accurate predictions of key network properties, even with default parameters. Normalized Hill functions improved quantitative predictions of global functional relationships compared with prior logic-based approaches. Comprehensive sensitivity analysis revealed the significant role of PKA negative feedback on upstream signaling and the importance of phosphodiesterases as key negative regulators of the network. The model was then extended to incorporate recently identified protein interaction data involving integrin-mediated mechanotransduction.Conclusions: The normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data.