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首页> 外文期刊>Journal of Theoretical Biology >Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity
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Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity

机译：汽车T细胞信号传导机械模型的数据驱动分析预测细胞对细胞异质性的影响

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Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patient's T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote tumor cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells' response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cell's ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling. (C) 2019 The Author(s). Published by Elsevier Ltd.
机译：由于蛋白质表达的可变性，相同群体的细胞可以表现出对刺激的不同反应。在人口平均值掩盖这些潜在的差异时，理解这种异质性非常重要。使用计算建模，我们可以比单独使用实验更精确地询问系统，以便了解每个蛋白质的表达如何影响生物系统。在这里，我们检查汽车T细胞信号传导的机械模型，其将受体 - 抗原结合到MAPK激活，以确定可以增加细胞反应的细胞内调制。 Car T细胞癌疗法涉及去除患者的T细胞，将它们改变以表达可以与肿瘤相关抗原结合的工程化受体以促进肿瘤细胞杀伤，然后将细胞注入患者。这种细胞群，如所有细胞群，都会具有异质蛋白表达，这可能影响治疗的功效。因此，重要的是检查细胞对细胞异质性的影响。我们首先通过机械模型的Monte Carlo模拟生成模拟单元响应的数据集，其中初始蛋白质浓度被随机取样。我们使用局部最小二乘建模分析数据集以确定蛋白质表达与ERK磷酸化之间的关系，机械模型的输出。使用该数据驱动分析，我们发现，只有直接与接收器和MAPK级联，网络的开始和结束的蛋白质的表达才与细胞的响应相关。令人惊讶的是，我们还发现，由于增加了来自磷酸酶的负反馈强度，因此增加了存在的受体量实际上可以抑制细胞的响应的能力。总的来说，我们已经组合了数据驱动和机械模型，以产生对汽车T细胞信号的详细洞察力。 （c）2019年作者。 elsevier有限公司出版

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