An S-System Parameter Estimation Method (SPEM) for Biological Networks
An S-System Parameter Estimation Method (SPEM) for Biological Networks
复制标题
生物网络的 S 系统参数估计方法 (SPEM)
DOI：
10.1089/cmb.2011.0269
复制
发表时间：
2012-02-01
影响因子：
1.7
通讯作者：
Nardini, Christine
中科院分区：
文献类型：
--
作者：
Yang, Xinyi;Dent, Jennifer E.;Nardini, Christine
实验生物学的进步，加上计算能力的进步，给计算生物学的跨学科领域带来了新的挑战。其中一项广泛的挑战在于基因网络的逆向工程，从确定静态网络的结构到从时间序列数据重建相互作用的动态。在这里，我们将注意力集中在后一个领域，特别是参数化基因之间定向相互作用的动态网络。通过基于连接基因之间已知的幂律关系模型（S 系统）的参数化方法，我们能够解释网络中的非线性，而不会影响分析网络特征的能力。在本文中，我们介绍 S 系统参数估计方法（SPEM）。 SPEM 是一个免费的 R 软件包 (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html)，它获取时间序列中的基因表达数据，并将相互作用网络作为一组微分方程返回。这里介绍和测试的方法不仅可以在合成数据上提供准确的结果，而且更重要的是在真实的、因此本质上是有噪声的生物数据上提供准确的结果。综上所述，SPEM 显示出高灵敏度和正预测值，以及免费可用性和可扩展性（因为基于开源软件）。我们期望这些特性使其成为具有挑战性的动态基因网络重建中有用且广泛适用的软件。
Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.