Anand Rajat, Chatterjee Samrat
Computational and Mathematical Biology Lab, Drug Discovery Research Centre, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India.
PLoS One. 2017 Apr 27;12(4):e0176172. doi: 10.1371/journal.pone.0176172. eCollection 2017.
Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time through the perturbations of biological processes. These perturbed biological processes usually work in an interdependent way. Systematic experiments tracking disease progression at gene level are usually conducted through a temporal microarray data. There is a need for developing methods to analyze such highly complex data to capture disease progression at the molecular level. In the present study, we have considered temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We first constructed a network between biological processes through common genes. We analyzed the data to obtain perturbed biological processes at each time point. Finally, we used the biological process network to find links between these perturbed biological processes. This enabled us to identify paths linking initial perturbed processes with final perturbed processes which capture disease progression. Using different datasets and statistical tests, we established that these paths are highly precise to the dataset from which these are obtained. We also established that the connecting genes present in these paths might contain some biological information and thus can be used for further mechanistic studies. The methods developed in our study are also applicable to a broad array of temporal data.
肥胖和糖尿病等代谢紊乱疾病是随着时间推移通过生物过程的扰动而逐渐发展的疾病。这些受到扰动的生物过程通常以相互依存的方式运作。在基因水平上跟踪疾病进展的系统实验通常通过时间微阵列数据进行。需要开发方法来分析此类高度复杂的数据，以在分子水平上捕捉疾病进展。在本研究中，我们考虑了来自一项研究小鼠肥胖和糖尿病发展的实验的时间微阵列数据。我们首先通过共同基因构建了生物过程之间的网络。我们分析数据以获得每个时间点受到扰动的生物过程。最后，我们使用生物过程网络来寻找这些受到扰动的生物过程之间的联系。这使我们能够识别出将初始受扰动过程与最终受扰动过程联系起来的路径，从而捕捉疾病进展。通过使用不同的数据集和统计测试，我们确定这些路径对从中获取它们的数据集具有高度的精确性。我们还确定这些路径中存在的连接基因可能包含一些生物学信息，因此可用于进一步的机制研究。我们研究中开发的方法也适用于广泛的时间数据。
