Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Complex Biological Systems and Bioinformatics Lab (CBB), Bioinformatics department, University of Tehran, Tehran, Iran.
BMC Bioinformatics. 2019 Feb 26;20(1):92. doi: 10.1186/s12859-019-2635-6.
Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.
In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes.
The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.
基于样本组之间差异表达基因准确识别受扰信号通路是理解疾病和可药物靶标的关键因素之一。大多数通路分析方法通过使用基于简单图的模型整合通路拓扑结构来优先考虑受影响的信号通路。尽管这些模型取得了相对的成功，但它们在描述生物通路中存在的所有类型的依赖关系和相互作用方面存在局限性。
在这项工作中，我们提出了一种基于信号通路形式化建模的新方法。正式建模信号通路，然后应用模型检查工具在给定条件下找到每条通路受到扰动的可能性。通过采用形式化方法，建模了生物部分之间的各种复杂相互作用，这有助于降低所提出方法的假阳性率。我们基于此方法开发了一个名为基于形式模型检查的通路分析（FoPA）的工具。通过应用靶通路技术，将 FoPA 与三种著名的通路分析方法（PADOG、CePa 和 SPIA）在来自不同疾病的 36 个 GEO 数据集的基准上进行了比较。这种验证技术消除了对结果进行可能有偏差的人为评估的需要。在没有先验的所有相关通路知识的情况下，选择模拟的假输入（置换的类标签和诱饵通路）作为一组负对照来测试方法的假阳性率。最后，为了进一步评估 FoPA 的效率，将其应用于一组自闭症相关基因。
靶通路技术获得的结果表明，FoPA 能够像 PADOG 一样优先考虑靶通路，但优于 CePa 和 SPIA。此外，使用 FoPA 发现显著通路的假阳性率低于其他比较方法。此外，FoPA 可以检测到比其他方法更一致的相关通路。FoPA 对自闭症相关基因的结果突出了“肾素-血管紧张素系统”通路的作用。该通路被认为在一些神经退行性疾病中具有关键作用，而迄今为止，人们对其对自闭症发展的影响关注甚少。
