基因网络相继故障机理分析

癌相关基因在疾病发生与发展过程中的作用机理是非常重要的研究课题。现代数据分析方法,为从基因组数据中推断癌相关基因之间的关联,以及分析基因组的作用机理提供了有效的手段。本文根据基因表达谱数据分别建立了正常组织与神经胶质瘤和肾癌的患病组织的基因互信息网络。用以介数为基础的相继故障模型研究了基因网络结构与鲁棒性之间的关系。定义了网络相继故障节点百分比、平均相继故障规模和相继故障规模比例累积概率等衡量网络鲁棒性的结构参数。通过对照组与实验组之间的比对,我们发现实验组网络比对照组网络更加稳定,并将引起网络大规模相继故障的基因称为结构性关键基因。这些结构性关键基因中的一部分已经被证明与神经胶质瘤或肾癌的发生、发展有密切关系。大多数基因被预测在神经胶质瘤和肾癌的发生中起着激励或抑制作用,需要进一步的试验验证。预测信息为研究癌相关基因提供了新的方向。

Analysis of cascading failure in gene networks

It is an important subject to study the functional mechanism of cancer-related genes make information and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we constructed mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigated the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality.Some important parameters,such as the percentage of failure nodes of the network, the average size-ratio of cascading failure and the cumulative probability of size-ratio of cascading failure are defined to measure the robustness of the networks. By comparing control group and experiment groups, we found that the networks of experiment groups are more robust than those of control group. The gene that can cause large scale failure is called structural key gene (SKG). Some of them have been confirmed to be closely,related to the formation and development of glioma and renal cancer,respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.