Computation and measurement of cell decision making errors using single cell data.
Computation and measurement of cell decision making errors using single cell data.
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DOI：
10.1371/journal.pcbi.1005436
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发表时间：
2017-04
影响因子：
4.3
通讯作者：
Abdi A
中科院分区：
文献类型：
--
作者：
Habibi I;Cheong R;Lipniacki T;Levchenko A;Emamian ES;Abdi A
在这项研究中，开发了一种新的计算方法来量化由于噪声和信令故障而导致的单元中的决策错误。使用这种方法分析调节转录因子核因子κB(NF-κB)的肿瘤坏死因子信号通路，可以识别两种类型的错误细胞决策，即错误警报和未命中。这两个事件分别表示声明一个不存在的信号和丢失一个确实存在的信号。利用单细胞实验数据和所开发的方法，我们计算了野生型细胞中的虚警和遗漏错误概率，并给出了这些度量如何依赖于信号转导噪声水平的公式。我们还表明，与野生型细胞相比，在细胞中存在异常的情况下，决策过程可以受到显著影响，并且该方法能够对这种影响进行建模和测量。在肿瘤坏死因子-核因子-κB途径中，该方法计算并揭示由于细胞无法抑制肿瘤坏死因子诱导的核因子-κB反应而导致的A20缺陷细胞中的假警报和错失概率的变化。在生物学方面，在这个异常的肿瘤坏死因子信号系统中，较高的错误警报度量表明感知到更多实际上不存在于系统输入的细胞因子信号，而较高的未命中度量表明它很可能错过实际存在的信号。总体而言，这项研究展示了所开发的方法在正常和异常条件下以及在存在转导噪声不确定性的情况下对细胞决策错误进行建模的能力。与以前报道的路径容量度量相比，我们的结果表明，引入的判决错误度量更准确地表征了信令故障。这主要是因为虽然容量是研究信号通路中信息传递的有用指标，但它没有捕捉到肿瘤坏死因子诱导的噪声反应曲线之间的重叠。细胞不断地接收来自周围环境的信号，并做出正确的决定，即对各种信号做出正确的反应，并启动某些细胞功能。近年来，细胞内决策过程的建模和量化已成为重要的研究领域。由于信号转导噪声，细胞对相似的输入做出不同的反应，这可能导致细胞做出错误的决定。在这里，我们开发了一种新的方法来描述细胞中的决策过程，使用统计信号处理和决策理论的概念。为了证明该方法的实用性，我们将其应用于一个重要的信号通路，该通路调节在细胞生存中发挥关键作用的分子。我们的方法揭示了细胞可能做出两种类型的错误决定，即错误警报和未命中事件。我们使用单细胞实验数据来衡量这些决定的可能性，并展示这些不正确的决定是如何与信号转导噪声或某些分子功能的缺失有关的。使用我们的方法，可以对其他分子系统中的决策错误进行建模。这些模型有助于理解和开发治疗炎症、各种癌症和自身免疫性疾病等病理过程的方法。
In this study a new computational method is developed to quantify decision making errors in cells, caused by noise and signaling failures. Analysis of tumor necrosis factor (TNF) signaling pathway which regulates the transcription factor Nuclear Factor κB (NF-κB) using this method identifies two types of incorrect cell decisions called false alarm and miss. These two events represent, respectively, declaring a signal which is not present and missing a signal that does exist. Using single cell experimental data and the developed method, we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level. We also show that in the presence of abnormalities in a cell, decision making processes can be significantly affected, compared to a wild-type cell, and the method is able to model and measure such effects. In the TNF—NF-κB pathway, the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells, caused by cell’s inability to inhibit TNF-induced NF-κB response. In biological terms, a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input, whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist. Overall, this study demonstrates the ability of the developed method for modeling cell decision making errors under normal and abnormal conditions, and in the presence of transduction noise uncertainty. Compared to the previously reported pathway capacity metric, our results suggest that the introduced decision error metrics characterize signaling failures more accurately. This is mainly because while capacity is a useful metric to study information transmission in signaling pathways, it does not capture the overlap between TNF-induced noisy response curves. Cell continuously receives signals from the surrounding environment and is supposed to make correct decisions, i.e., respond properly to various signals and initiate certain cellular functions. Modeling and quantification of decision making processes in a cell have emerged as important areas of research in recent years. Due to signal transduction noise, cells respond differently to similar inputs, which may result in incorrect cell decisions. Here we develop a novel method for characterization of decision making processes in cells, using statistical signal processing and decision theory concepts. To demonstrate the utility of the method, we apply it to an important signaling pathway that regulates molecules which play key roles in cell survival. Our method reveals that cells can make two types of incorrect decisions, namely, false alarm and miss events. We measure the likelihood of these decisions using single cell experimental data, and demonstrate how these incorrect decisions are related to the signal transduction noise or absence of certain molecular functions. Using our method, decision making errors in other molecular systems can be modeled. Such models are useful for understanding and developing treatments for pathological processes such as inflammation, various cancers and autoimmune diseases.