Modeling Gene Regulation Networks Using Ordinary Differential Equations
Modeling Gene Regulation Networks Using Ordinary Differential Equations
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DOI：
10.1007/978-1-61779-400-1_12
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发表时间：
2012-01-01
期刊：
影响因子：
--
通讯作者：
Zhao, Hongyu
中科院分区：
文献类型：
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作者：
Cao, Jiguo;Qi, Xin;Zhao, Hongyu
基因调控网络由转录因子、转录因子间的相互作用和靶点组成。从基因组学数据中重构和研究这些调控网络具有重要意义。常微分方程是研究基因调控网络动态系统的常用工具。虽然常微分方程的形式通常是基于专家知识提供的，但常微分方程参数的值很少知道。从基因表达数据中推断ODE参数是一个具有挑战性的问题，因为ODE没有解析解，并且时间过程基因表达数据通常是稀疏的并且与大噪声相关联。在这一章中，我们回顾了如何广义剖析方法可以应用于获得估计的ODE参数的时间过程基因表达数据。我们还总结了广义轮廓估计的一致性和渐近正态性结果。
Gene regulation networks arc composed of transcription factors, their interactions, and targets. It is of great interest to reconstruct and study these regulatory networks from genomics data. Ordinary differential equations (ODEs) are popular tools to model the dynamic system of gene regulation networks. Although the form of ODEs is often provided based on expert knowledge, the values for ODE parameters arc seldom known. It is a challenging problem to infer ODE parameters from gene expression data, because the ODEs do not have analytic solutions and the time-course gene expression data are usually sparse and associated with large noise. In this chapter, we review how the generalized profiling method can be applied to obtain estimates for ODE parameters from the time-course gene expression data. We also summarize the consistency and asymptotic normality results for the generalized profiling estimates.