Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes and identify false alarms and miss events associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. Additionally, we demonstrate how graphing receiver operating characteristic curves conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision-making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision-making approach can be extended to more than two possible outcomes. As an example and to show how the introduced methods can be used in practice, we apply them to single cell data of PTEN, an important intracellular regulatory molecule in a p53 system, in wild-type and abnormal cells. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from viruses, bacteria, yeast and lower metazoans to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be utilized to better understand the transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases.

中文翻译：

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使用机器学习方法在嘈杂的细胞内网络中对影响决策的信号结果进行建模和测量。

表征细胞响应接收信号的决策对于理解细胞命运是如何确定的很重要。当我们考虑细胞异质性和生化过程的动力学时，问题变得多方面和复杂。在本文中，我们提出了一套统一的决策理论、机器学习和统计信号处理方法和指标，以使用单细胞数据在存在不确定性的情况下对信令决策的精度进行建模。首先，我们引入了可能由信令过程导致的错误决策，并识别与此类决策相关的错误警报和遗漏事件。然后，我们提出了一种使总决策错误概率最小的最优决策策略。此外，我们演示了绘制接收器操作特性曲线如何方便地揭示与不同细胞响应相关的误报和漏报概率之间的权衡。此外，我们扩展了引入的框架，使用多时间点测量和多维结果分析和决策算法，将生物化学过程和反应的动力学纳入细胞中。引入的多变量信号结果建模框架可用于分析在相同或不同时间点测量的几种分子种类。我们还展示了开发的二元结果分析和决策方法如何扩展到两个以上的可能结果。作为一个例子并展示如何在实践中使用引入的方法，我们将它们应用于 PTEN 的单细胞数据，野生型和异常细胞中 p53 系统中重要的细胞内调节分子。这里提出的统一信号结果建模框架可以应用于各种生物，从病毒、细菌、酵母和低等后生动物到更复杂的生物，如哺乳动物细胞。最终，这种信号转导结果建模方法可用于更好地了解从生理状态到病理状态的转变，例如炎症、各种癌症和自身免疫疾病。