Explore biological pathways from noisy array data by directed acyclic Boolean networks
Explore biological pathways from noisy array data by directed acyclic Boolean networks
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DOI：
10.1089/cmb.2005.12.170
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发表时间：
2005-01-01
影响因子：
1.7
通讯作者：
Lu, HHS
中科院分区：
文献类型：
--
作者：
Li, LM;Lu, HHS
我们认为有向无环布尔（DAB）网络的结构作为一种工具，探索生物途径。在DAB网络中，基本对象是二进制元素和它们的布尔数。DAB由两种成对关系表征：相似性和先决条件。后者是一种偏序关系，即一个元件的导通状态是另一个元件的导通状态所必需的。DAB网络由其元素的状态空间唯一确定。我们安排样本从DAB网络的状态空间中的一个二进制数组，并引入一个随机机制的测量误差。我们的推理策略包括两个阶段。首先，我们考虑每对元素，并试图确定它们最可能的关系。同时，我们给这个关系分配一个分数，s-p-score。其次，我们对第一阶段获得的s-p分数进行排名。我们期望具有较小s-p-分数的关系更有可能为真，而具有较大s-p-分数的关系更有可能为假。关键思想是定义s-分数（指相似性），p-分数（指先决条件）和s-p-分数。与经典的统计检验一样，控制假阴性和假阳性是我们的主要关注点。我们举例说明了一个模拟的例子，经典的精氨酸生物合成途径的方法，并显示一些探索性的结果上发表的微阵列表达数据集的酵母酿酒酵母从实验中获得的信息素反应MAPK途径的激活和遗传扰动。
We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.