Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
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DOI：
10.1109/tcbb.2012.169
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发表时间：
2013-01-01
影响因子：
4.5
通讯作者：
Hashimoto, Ronaldo F.
中科院分区：
文献类型：
--
作者：
Higa, Carlos H. A.;Andrade, Tales P.;Hashimoto, Ronaldo F.
基因调控网络（GRN）的模型已经提出了沿着与算法推断其结构。所谓结构，我们指的是所研究的生物系统中基因之间的关系。尽管在生物体的基因组中发现了大量的基因，但据信，一小部分基因负责维持特定的核心调控机制（小型子网络）。我们提出了一个算法的推理子网络的基因从一个小的初始集的基因称为种子和时间序列基因表达数据。该算法有两个主要步骤：第一，通过添加基因来生长基因种子，第二，搜索可能具有生物学意义的子网络。种子生长步骤被视为一个特征选择问题，我们使用了一个阈值布尔网络与扰动模型来设计的标准函数，用于选择的功能（基因）。鉴于GRN的逆向工程是一个不一定有唯一解决方案的问题，所提出的算法的输出是一组网络，而不是一个单一的网络。该算法还分析了网络的动态特性，这可能是耗时的。然而，该算法适用于基因数量较少的情况。结果表明，该算法能够恢复可接受的基因相互作用速率，并生成可在湿实验室中探索的调控假设。
Models of gene regulatory networks (GRN) have been proposed along with algorithms for inferring their structure. By structure, we mean the relationships among the genes of the biological system under study. Despite the large number of genes found in the genome of an organism, it is believed that a small set of genes is responsible for maintaining a specific core regulatory mechanism (small subnetworks). We propose an algorithm for inference of subnetworks of genes from a small initial set of genes called seed and time series gene expression data. The algorithm has two main steps: First, it grows the seed of genes by adding genes to it, and second, it searches for subnetworks that can be biologically meaningful. The seed growing step is treated as a feature selection problem and we used a thresholded Boolean network with a perturbation model to design the criterion function that is used to select the features (genes). Given that the reverse engineering of GRN is a problem that does not necessarily have one unique solution, the proposed algorithm has as output a set of networks instead of one single network. The algorithm also analyzes the dynamics of the networks which can be time-consuming. Nevertheless, the algorithm is suitable when the number of genes is small. The results showed that the algorithm is capable of recovering an acceptable rate of gene interactions and to generate regulatory hypotheses that can be explored in the wet lab.