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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Evolutionary Model Selection and Parameter Estimation for Protein-Protein Interaction Network Based on Differential Evolution Algorithm
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Evolutionary Model Selection and Parameter Estimation for Protein-Protein Interaction Network Based on Differential Evolution Algorithm

机译：基于差分进化算法的蛋白质-蛋白质相互作用网络的进化模型选择和参数估计

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摘要

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network remains a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work, we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution algorithm (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms for PPI networks more accurately. We tested our method for its power in differentiating models and estimating parameters on simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show duplication attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks.
机译：揭示潜在的进化机制在理解细胞中的蛋白质相互作用网络中起着重要作用。尽管已经提出了许多进化模型，但是将这些模型应用于实际网络数据，尤其是区分哪种模型可以更好地描述所观察网络的进化过程的问题仍然是一个挑战。传统方法是使用带有假定参数的模型来生成网络，然后通过汇总统计信息来评估适用性，但是汇总统计信息无法捕获完整的网络结构信息并无法估计参数分布。在这项工作中，我们开发了一种基于近似贝叶斯计算和改进的差分进化算法（ABC-DEP）的新方法，该方法能够同时进行模型选择和参数估计，并能够更准确地检测PPI网络的潜在进化机制。我们在区分模型和在模拟数据上估计参数方面测试了该方法的功能，发现与以前的方法相比，性能基准有了显着改善。我们进一步将我们的方法应用于人和酵母中蛋白质相互作用网络的真实数据。我们的结果表明，重复附着模型是人类PPI网络的主要进化机制，而无标度模型是酵母PPI网络的主要机制。

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