Reverse engineering gene regulatory network from microarray data using linear time-variant model.
Reverse engineering gene regulatory network from microarray data using linear time-variant model.
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DOI：
10.1186/1471-2105-11-s1-s56
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发表时间：
2010-01-18
影响因子：
3
通讯作者：
Iba H
中科院分区：
文献类型：
--
作者：
Kabir M;Noman N;Iba H
基因调控网络是活细胞中基因调控的抽象映射，可以帮助预测活生物体的系统行为。这种预测能力可能会导致改进诊断测试和治疗方法的发展。DNA微阵列可以并行测量数千个基因的表达水平，为基因调控网络的推断提供了数字种子。本文提出了一种利用线性时变模型从时间序列基因表达数据推断基因调控网络的新方法。本文采用一种通用的鲁棒进化算法——自适应差分进化算法作为学习范式。为了评估所提出的工作的效力，使用了一个众所周知的非线性合成网络。重构方法从无噪声和有噪声的时间序列数据中，以较高的精度推断出该合成网络的拓扑结构和相关的调节参数。为了验证目的，本文还将该方法应用于盘状盘齿龙cAMP振荡的模拟表达数据集，并证明了该方法在寻找正确规律方面的优势。通过分析大肠杆菌中SOS DNA修复系统的真实表达数据集，也验证了本工作的强度，与现有的各种工作相比，成功地找到了更正确合理的规律。通过该方法，从合成的、模拟的cAMP振荡表达数据和真实的表达数据中有效地推断出基因相互作用网络。该方法的计算时间也相当小，更适合于较大的网络重构。因此，所提出的方法可以作为未来相关领域研究的一个开端。
Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm. To assess the potency of the proposed work, a well known nonlinear synthetic network has been used. The reconstruction method has inferred this synthetic network topology and the associated regulatory parameters with high accuracy from both the noise-free and noisy time-series data. For validation purposes, the proposed approach is also applied to the simulated expression dataset of cAMP oscillations in Dictyostelium discoideum and has proved it's strength in finding the correct regulations. The strength of this work has also been verified by analyzing the real expression dataset of SOS DNA repair system in Escherichia coli and it has succeeded in finding more correct and reasonable regulations as compared to various existing works. By the proposed approach, the gene interaction networks have been inferred in an efficient manner from both the synthetic, simulated cAMP oscillation expression data and real expression data. The computational time of this approach is also considerably smaller, which makes it to be more suitable for larger network reconstruction. Thus the proposed approach can serve as an initiate for the future researches regarding the associated area.