Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh.
BMC Bioinformatics. 2010 Jan 18;11 Suppl 1(Suppl 1):S56. doi: 10.1186/1471-2105-11-S1-S56.
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.
基因调控网络是对活细胞中基因调控的抽象映射，可以帮助预测生物体的系统行为。这种预测能力可能会导致改进的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，构成了推断基因调控网络的数字基础。在本文中，我们提出了一种使用线性时变模型从时间序列基因表达数据中推断基因调控网络的新方法。在这里，自适应差分进化，一种通用且强大的进化算法，被用作学习范例。
为了评估所提出工作的功效，使用了一个众所周知的非线性合成网络。该重建方法从无噪声和噪声时间序列数据中以高精度推断了这个合成网络拓扑和相关的调节参数。为了验证目的，还将所提出的方法应用于 Dictyostelium discoideum 中 cAMP 振荡的模拟表达数据集，并证明了它在找到正确调节方面的优势。通过分析大肠杆菌 SOS DNA 修复系统的真实表达数据集，也验证了这项工作的优势，与各种现有方法相比，它成功地找到了更多正确和合理的调节。
通过所提出的方法，从合成的、模拟的 cAMP 振荡表达数据和真实的表达数据中以有效的方式推断了基因相互作用网络。该方法的计算时间也相当小，这使得它更适合于更大的网络重建。因此，所提出的方法可以作为未来相关领域研究的起点。
