Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model.
Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model.
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DOI：
10.1016/j.xpro.2021.100739
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发表时间：
2021-09-17
期刊：
影响因子：
--
通讯作者：
Popel AS
中科院分区：
文献类型：
--
作者：
Zhao C;Popel AS
测量和分析巨噬细胞复杂的动态多标记物特征对于理解其在健康和疾病中的不同表型和功能至关重要。为此，我们最近开发了一个多路径计算模型，首次能够从定量、时间、剂量依赖和单细胞方面对巨噬细胞信号和激活进行系统水平的表征。该协议包括利用该模型以高分辨率和高效率计算探索不同生物场景的指令。有关本议定书的使用和执行的完整细节，请参阅。多途径机制巨噬细胞极化模型定量、时间/剂量依赖和单细胞分辨的计算平台基于模型的治疗性巨噬细胞复极在病理生理学中的评价能够测量和分析巨噬细胞复杂的动态多标记物特征，对于理解其在健康和疾病中的不同表型和功能至关重要。为此，我们最近开发了一个多路径计算模型，首次能够从定量、时间、剂量依赖和单细胞方面对巨噬细胞信号和激活进行系统水平的表征。该协议包括利用该模型以高分辨率和高效率计算探索不同生物场景的指令。
The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a systems-level characterization of macrophage signaling and activation from quantitative, temporal, dose-dependent, and single-cell aspects. This protocol includes instructions to utilize this model to computationally explore different biological scenarios with high resolution and efficiency. For complete details on the use and execution of this protocol, please refer to. A computational platform of multi-pathway mechanistic macrophage polarization Model resolution from quantitative, time/dose-dependent, and single-cell aspects Model-based evaluation of therapeutic macrophage repolarization in pathophysiology The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a systems-level characterization of macrophage signaling and activation from quantitative, temporal, dose-dependent, and single-cell aspects. This protocol includes instructions to utilize this model to computationally explore different biological scenarios with high resolution and efficiency.
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影响因子：
5.8
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