Advanced Fault Diagnosis Methods in Molecular Networks

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.细胞信号网络（cell signaling networks）的故障分析是系统生物学（systems biology）领域的重要研究方向，在靶点发现（target discovery）与药物开发（drug development）中均具有实际应用价值。本文针对信号网络的故障诊断开发了若干先进方法，并将其应用于半胱天冬酶网络（caspase network）与SHP2信号网络（SHP2 network）。本研究旨在阐明网络内分子功能异常如何以及在多大程度上会导致整个网络的故障。网络功能异常（即故障）被定义为无法响应输入信号（input signals）并产生预期输出。分子脆弱性水平（vulnerability level）被定义为当该分子功能异常时，整个网络发生故障的概率。本研究开发了一种可针对不同输入信号组合计算单分子脆弱性水平的方法。此外，本研究还提出了一种更复杂但具备生物学意义的多故障脆弱性水平计算方法，用于分析两个或多个分子同时功能异常的场景。最后，本研究基于三值逻辑模型（ternary logic model）开发了一种网络故障诊断方法：该模型将分子的活性水平分为三类，区别于此前已发表的二值逻辑模型（binary logic model），并给出了三值框架下分子脆弱性的计算公式。多故障分析结果显示，高脆弱性分子对通常包含单故障分析中识别出的高脆弱性分子。针对半胱天冬酶网络的三值故障分析结果表明，使用更复杂的三值模型得到的预测结果，与更简单的二值方法的预测结果大体一致。本研究表明，增加分子活性水平的分类数量会提升模型的复杂度，但三值模型的预测能力并未成比例地增强。