Identification of stable genetic networks using convex programming
Identification of stable genetic networks using convex programming
复制标题
使用凸规划识别稳定遗传网络
DOI：
10.1109/acc.2008.4586910
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
George Pappas
中科院分区：
文献类型：
--
作者：
M. Zavlanos;A. Julius;Stephen P. Boyd;George Pappas
基因调控网络捕获基因和其他细胞物质之间的相互作用，导致转录和翻译的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用mRNA浓度来测量。在所谓的遗传扰动实验中，对平衡状态施加小的扰动，并测量由此产生的表达活性变化。本文提出了一种新的算法来识别稀疏稳定的遗传网络，以解释在平衡状态下获得的噪声遗传扰动实验。我们的识别算法还可以纳入各种可能的网络结构先验知识，这些知识可以是定性的，指定基因之间的积极、消极或没有相互作用，也可以是定量的，指定相互作用强度的范围。该方法基于凸规划松弛来处理稀疏性约束，因此适用于基因组尺度遗传网络的识别。
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 in mRNA concentrations 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. This paper develops a novel algorithm that identifies a sparse stable genetic network that explains noisy genetic perturbation experiments obtained at equilibrium. Our identification algorithm can also incorporate a variety of possible prior knowledge of the network structure, which can be either qualitative, specifying positive, negative or no interactions between genes, or quantitative, specifying a range of interaction strength. Our method is based on a convex programming relaxation for handling the sparsity constraint, and therefore is applicable to the identification of genome-scale genetic networks.
DOI：
10.1073/pnas.95.25.14863
发表时间：
1998-12-08
影响因子：
11.1
作者：
Eisen, MB;Spellman, PT;Botstein, D
通讯作者：
Botstein, D