Julius A, Zavlanos M, Boyd S, Pappas G J
University of Pennsylvania, Department of Electrical and Systems Engineering, USA.
IET Syst Biol. 2009 May;3(3):155-66. doi: 10.1049/iet-syb.2008.0130.
Gene regulatory networks capture interactions between genes and other cell substances, resulting in various models for the fundamental biological process of transcription and translation. The expression levels of the genes are typically measured as mRNA concentration in micro-array experiments. In a so-called genetic perturbation experiment, small perturbations are applied to equilibrium states and the resulting changes in expression activity are measured. One of the most important problems in systems biology is to use these data to identify the interaction pattern between genes in a regulatory network, especially in a large scale network. The authors develop a novel algorithm for identifying the smallest genetic network that explains genetic perturbation experimental data. By construction, our identification algorithm is able to incorporate and respect a priori knowledge known about the network structure. A priori biological knowledge is typically qualitative, encoding whether one gene affects another gene or not, or whether the effect is positive or negative. The method is based on a convex programming relaxation of the combinatorially hard problem of L(0) minimisation. The authors apply the proposed method to the identification of a subnetwork of the SOS pathway in Escherichia coli, the segmentation polarity network in Drosophila melanogaster, and an artificial network for measuring the performance of the method.
基因调控网络捕捉基因与其他细胞物质之间的相互作用，从而产生转录和翻译这一基本生物学过程的各种模型。在微阵列实验中，基因的表达水平通常以mRNA浓度来衡量。在所谓的基因扰动实验中，对平衡状态施加微小扰动，并测量由此产生的表达活性变化。系统生物学中最重要的问题之一是利用这些数据来识别调控网络中基因之间的相互作用模式，尤其是在大规模网络中。作者开发了一种新颖的算法，用于识别能够解释基因扰动实验数据的最小基因网络。通过构建，我们的识别算法能够纳入并尊重关于网络结构的先验知识。先验生物学知识通常是定性的，编码一个基因是否影响另一个基因，或者这种影响是正向还是负向。该方法基于对组合难题L(0)最小化的凸规划松弛。作者将所提出的方法应用于识别大肠杆菌中SOS途径的一个子网、黑腹果蝇中的分割极性网络以及一个用于测试该方法性能的人工网络。
