Habibi Iman, Cheong Raymond, Lipniacki Tomasz, Levchenko Andre, Emamian Effat S, Abdi Ali
Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States of America.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
PLoS Comput Biol. 2017 Apr 5;13(4):e1005436. doi: 10.1371/journal.pcbi.1005436. eCollection 2017 Apr.
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.
在本研究中，开发了一种新的计算方法来量化由噪声和信号故障导致的细胞决策错误。使用该方法对调节转录因子核因子κB（NF-κB）的肿瘤坏死因子（TNF）信号通路进行分析，识别出两种错误的细胞决策类型，即误报和漏报。这两种情况分别表示宣布不存在的信号以及错过确实存在的信号。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误报和漏报错误概率，并给出了一个公式，展示了这些指标如何依赖于信号转导噪声水平。我们还表明，与野生型细胞相比，细胞中存在异常时，决策过程会受到显著影响，并且该方法能够对这种影响进行建模和测量。在TNF-NF-κB通路中，该方法计算并揭示了A20缺陷细胞中误报和漏报概率的变化，这是由于细胞无法抑制TNF诱导的NF-κB反应所致。从生物学角度来看，在这个异常的TNF信号系统中，较高的误报指标表明感知到更多实际上在系统输入中不存在的细胞因子信号，而较高的漏报指标表明很可能错过实际存在的信号。总体而言，本研究证明了所开发的方法能够在正常和异常条件下以及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。与先前报道的通路容量指标相比，我们的结果表明引入的决策错误指标能更准确地表征信号故障。这主要是因为虽然容量是研究信号通路中信息传输的一个有用指标，但它没有捕捉到TNF诱导的噪声响应曲线之间的重叠。
