In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

中文翻译：

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通过机械数学模型破译肝脏中的信号转导网络

在健康和疾病中，肝细胞不断暴露于细胞因子和生长因子。虽然对这些因素诱导的单个信号转导途径进行了非常详细的研究，但由重复或组合刺激诱导的细胞反应是复杂的，并且不太了解。肝细胞细胞表面的生长因子受体显示受受体相互作用、受体运输和反馈调节的调节。在这里，我们举例说明了如何使用基于定量数据的机械数学模型在分子水平上解开这些相互作用。关键是在数学框架内基于定量纵向数据的机械水平分析。在这样的多层信息中，使用子模块的逐步数学建模是有利的，这是通过共享标准化的实验数据和数学模型来促进的。信号转导与肝脏代谢调节的整合以及与转化方法的机制联系有望为生物学和个性化医疗提供预测工具。