Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations
Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations
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DOI：
10.1142/9789812776303_0003
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发表时间：
2002-12
影响因子：
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通讯作者：
M. Hoon;S. Imoto;Kazuo Kobayashi;N. Ogasawara;Satoru Miyano
中科院分区：
文献类型：
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作者：
M. Hoon;S. Imoto;Kazuo Kobayashi;N. Ogasawara;Satoru Miyano
我们描述了一种新的方法，以线性微分方程组的形式，从时间进程基因表达数据中推断基因调控网络。由于生物学上已知的基因调控网络是稀疏的，我们预计这样的线性微分方程组中的大多数系数为零。在以前提出的方法中，系统中的非零系数的数量是基于特殊假设的限制的。相反，我们建议从数据中推断基因调控网络的稀疏程度，其中我们使用Akaike的信息标准来确定哪些系数不是零。我们将我们的方法应用于枯草芽孢杆菌的MMGE时间历程数据。
We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaike's Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.