Yu Le, Watterson Steven, Marshall Stephen, Ghazal Peter
Department of Electronic and Electrical Engineering, University of Strathclyde, Royal College Building, 204 George Street, Glasgow, UK G1 1XW.
Mol Biosyst. 2008 Oct;4(10):1024-30. doi: 10.1039/b804649b. Epub 2008 Aug 26.
Due to the large number of variables required and the limited number of independent experiments, the inference of genetic regulatory networks from gene expression data is a challenge of long standing within the microarray field. This report investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed microarray data. We interpret the discrete expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. In the former case, a BNp can be inferred trivially, for an arbitrary number of genes and attractors. In the latter case, we use the constraints posed by the distribution of attractor states and the need to conserve probability to arrive at one of three possible solutions: an unique, exact network; several exact networks or a 'most-likely' network. In the case of several exact networks we use a robustness requirement to select a preferred network. In the case that an exact option is not found, we select the network that best fits the observed attractor distribution. We apply the resulting algorithm to the interferon regulatory network using expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.
由于所需变量数量众多且独立实验数量有限，从基因表达数据推断基因调控网络一直是微阵列领域长期面临的挑战。本报告研究了具有扰动的布尔网络（BNp）从模拟数据和观测微阵列数据中的推断。我们将离散表达水平解释为基础网络的吸引子状态，并使用吸引子状态序列来确定模型。我们考虑已知吸引子完整序列的情况以及已知吸引子状态是通过从基础吸引子序列中采样得到的情况。在前一种情况下，对于任意数量的基因和吸引子，都可以轻松推断出BNp。在后一种情况下，我们利用吸引子状态分布所带来的约束以及概率守恒的需求，得出三种可能的解决方案之一：一个唯一的、精确的网络；几个精确的网络或一个“最可能”的网络。在有几个精确网络的情况下，我们使用稳健性要求来选择一个首选网络。在未找到精确选项的情况下，我们选择最符合观测到的吸引子分布的网络。我们将所得算法应用于干扰素调控网络，使用从感染巨细胞病毒的小鼠骨髓来源巨噬细胞获取的表达数据。
