Yepiskoposyan Hasmik, Talikka Marja, Vavassori Stefano, Martin Florian, Sewer Alain, Gubian Sylvain, Luettich Karsta, Peitsch Manuel Claude, Hoeng Julia
PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland.
Front Genet. 2019 Feb 15;10:87. doi: 10.3389/fgene.2019.00087. eCollection 2019.
Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways' defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary beating, and (3) goblet cell hyperplasia/metaplasia and that were built in the Biological Expression Language, which is both human-readable and computable. The network analysis of highly connected nodes and pathways demonstrated that the relevant biology was captured in the MCC models. We also show the scoring of transcriptomic data onto these network models and demonstrate that the models capture the perturbation in each dataset accurately. This work is a continuation of our approach to use computational biological network models and mathematical algorithms that allow for the interpretation of high-throughput molecular datasets in the context of known biology. The MCC network model suite can be a valuable tool in personalized medicine to further understand heterogeneity and individual drug responses in complex respiratory diseases.
黏液纤毛清除（MCC）被认为是杯状细胞分泌的黏液、气道表面液体层以及纤毛细胞纤毛摆动的协同作用，是气道抵御空气传播污染物的防御系统。由于该过程在分子水平上已有详尽描述，我们将现有信息整合到一套综合的因果生物网络（CBN）模型中。该套模型由三个独立模型组成，分别代表（1）纤毛组装、（2）纤毛摆动以及（3）杯状细胞增生/化生，它们是用生物表达语言构建的，这种语言既便于人类阅读又可进行计算。对高度连接的节点和通路进行网络分析表明，相关生物学内容已在MCC模型中得以体现。我们还展示了将转录组数据映射到这些网络模型上的情况，并证明这些模型能够准确捕捉每个数据集中的扰动。这项工作是我们利用计算生物网络模型和数学算法来解释高通量分子数据集在已知生物学背景下的情况这一方法的延续。MCC网络模型套件可以成为个性化医疗中的一个有价值工具，以进一步了解复杂呼吸道疾病中的异质性和个体药物反应。
