Quantitative comparison of alternative methods for coarse-graining biological networks
Quantitative comparison of alternative methods for coarse-graining biological networks
复制标题
粗粒度生物网络替代方法的定量比较
DOI：
10.1063/1.4812768
复制
发表时间：
2013-09-28
影响因子：
4.4
通讯作者：
Huang, Xuhui
中科院分区：
文献类型：
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作者：
Bowman, Gregory R.;Meng, Luming;Huang, Xuhui
马尔可夫模型和主方程是模拟蛋白质构象变化等动态过程的有力手段。然而，这些模型通常很难理解，因为它们之间有大量的组件和连接。因此，已经开发了各种方法来通过对这些复杂模型进行粗粒化来促进理解。在这里，我们使用贝叶斯模型比较来确定这些粗粒化方法中的哪一种提供了最忠实于原始状态集的模型。我们发现，贝叶斯凝聚聚类引擎和层次Nystrom扩展图(HNEG)通常提供了最好的性能。令人惊讶的是，原始的Perron聚类分析(PCCA)方法往往提供次好的结果，表现优于较新的PCCA+方法和最可能路径算法。我们还表明，模型之间的差异在质量上是显著的，而不是国家之间边界的微小变化。这些方法的性能与产生的粗粒度的熵很好地相关，这表明找到具有更相似种群的状态(即，避免可能只是噪声的低种群状态)可以提供更好的结果。(C)2013 AIP出版有限责任公司。
Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nystrom expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results. (C) 2013 AIP Publishing LLC.