Cells in a genetically homogeneous cell‐population exhibit a significant degree of heterogeneity in their responses to an external stimulus. To understand origins and importance of this heterogeneity, individual‐based population model (IBPM), where parameters follow probability density functions (PDFs) instead of being constants, has been previously developed. However, parameter identification for an IBPM is challenging as estimating PDFs is computationally expensive. Also, because of experimental limitations and nonlinearity of models, not all parameters' PDFs are identifiable. Motivated by the above considerations, a new methodology is proposed in this study. First, a subset of parameters whose PDFs is identifiable are determined through sensitivity analysis, and only these PDFs are estimated. Second, an artificial neural network model is developed to find an empirical relation between these parameter and output PDFs to reduce computational costs of the parameter identification. The proposed approach is validated by estimating PDFs of parameters of a tumor necrosis factor‐α signaling model.

中文翻译：

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通过系统工程方法识别细胞间异质性

具有遗传同质性的细胞群体中的细胞在对外部刺激的反应中表现出很大程度的异质性。为了了解这种异质性的起源和重要性，以前已经开发了基于个体的人口模型（IBPM），其中参数遵循概率密度函数（PDF），而不是常数。但是，由于估计PDF的计算量很大，因此IBPM的参数识别具有挑战性。而且，由于实验的局限性和模型的非线性，并非所有参数的PDF都是可识别的。基于以上考虑，本研究提出了一种新的方法。首先，通过敏感性分析确定其PDF可识别的参数子集，并且仅估计这些PDF。第二，开发了一个人工神经网络模型来查找这些参数与输出PDF之间的经验关系，以减少参数识别的计算成本。通过估计肿瘤坏死因子-α信号模型参数的PDF验证了该方法的有效性。