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arXiv 0709.26462007-09-17q-bio.PEmath.CO

累积突变的马尔可夫模型

Markov models for accumulating mutations

Niko Beerenwinkel, Seth Sullivant

备注：: 21 pages, 8 figures

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摘要：

我们介绍并分析了遗传变化积累的等待时间模型。连续时间合取贝叶斯网络由突变的偏序集合和每个突变的固定率定义。偏序编码的顺序，其中突变可以固定在人口的约束，揭示了进化过程中的突变途径。我们研究了删失版本的模型，并推导出方程的EM算法进行最大似然估计的模型参数。我们还展示了如何选择最大似然偏序集。该模型应用于不同癌症和耐药HIV样本的遗传数据，表明了诊断和治疗的意义。

英文摘要：

We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an EM algorithm to perform maximum likelihood estimation of the model parameters. We also show how to select the maximum likelihood poset. The model is applied to genetic data from different cancers and from drug resistant HIV samples, indicating implications for diagnosis and treatment.

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