Larvie Joy Edward, Sefidmazgi Mohammad Gorji, Homaifar Abdollah, Harrison Scott H, Karimoddini Ali, Guiseppi-Elie Anthony
Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.
Department of Biology, North Carolina A&T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.
Bioengineering (Basel). 2016 Apr 19;3(2):12. doi: 10.3390/bioengineering3020012.
Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim to capture dependencies among molecular entities such as transcription factors, proteins and metabolites. In most applications, the regulatory network structure is unknown, and has to be reverse engineered from experimental data consisting of expression levels of the genes usually measured as messenger RNA concentrations in microarray experiments. Steady-state gene expression data are obtained from measurements of the variations in expression activity following the application of small perturbations to equilibrium states in genetic perturbation experiments. In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR) originally proposed for the analysis of economic time series data is adapted to include a stability constraint for the recovery of a sparse and stable regulatory network that describes data obtained from noisy perturbation experiments. The approach is applied to real experimental data obtained for the SOS pathway in and the cell cycle pathway for yeast . Significant features of this method are the ability to recover networks without inputting prior knowledge of the network topology, and the ability to be efficiently applied to large scale networks due to the convex nature of the method.
基因调控网络代表了活细胞中基因调控的一种抽象映射。它们旨在捕捉分子实体（如转录因子、蛋白质和代谢物）之间的依赖性。在大多数应用中，调控网络结构是未知的，必须从实验数据中反向构建，这些实验数据通常由基因的表达水平组成，在微阵列实验中以信使RNA浓度来衡量。稳态基因表达数据是通过在基因扰动实验中对平衡状态施加小扰动后，测量表达活性的变化而获得的。在本文中，最初为分析经济时间序列数据而提出的最小绝对收缩和选择算子 - 向量自回归（LASSO-VAR）方法进行了调整，以纳入一个稳定性约束，用于恢复描述从有噪声的扰动实验中获得的数据的稀疏且稳定的调控网络。该方法应用于为大肠杆菌的SOS途径和酵母的细胞周期途径获得的实际实验数据。此方法的显著特点是无需输入网络拓扑的先验知识就能恢复网络，并且由于该方法的凸性，能够有效地应用于大规模网络。
