Quantitative inference of dynamic regulatory pathways via microarray data.
Quantitative inference of dynamic regulatory pathways via microarray data.
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DOI：
10.1186/1471-2105-6-44
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发表时间：
2005-03-07
影响因子：
3
通讯作者：
Chen BS
中科院分区：
文献类型：
--
作者：
Chang WC;Li CW;Chen BS
细胞信号通路（网络）是生物学研究的主要课题之一。细胞内信号通路中基因间的相互作用是功能基因组学的基础。因此，哪些基因以及它们通过转录结合或物理相互作用相互影响的程度是关键问题。通过微阵列芯片同步测量基因表达，大量的动态信息被嵌入并有待发现。本文从系统生物学的角度，采用系统动力学建模的方法，探讨了细胞信号通路中基因间的因果关系。在这项研究中，一个二阶动力学模型的目的基因的调控机制，从上游因果关系的角度来描述。从目的基因的表达谱和动态模型中，我们可以估计其上游调控功能。根据这种上游调控功能，我们可以推断出上游调控基因的调控能力和激活延迟，进而连接起一条调控通路。迭代地，这些调控基因被认为是靶基因，以追溯其上游调控基因。然后，我们可以构建全基因组的调控通路（或网络）。总之，我们可以从基因表达谱中定量地推断基因调控途径，从而确认某些可疑的调控途径或寻找某些调控途径（网络）中的未知途径。最后，通过随机重排微阵列数据的时间顺序来验证所提出的方法。我们把我们的算法集中在推断已确定的因果基因的调控能力，以及在它们调控下游基因之前有多少延迟。有了这些信息，将使用微阵列数据建立一个调控途径。在本研究中，两个信号通路，即昼夜调节途径在拟南芥和代谢转换途径从发酵到呼吸的酵母酿酒酵母，利用微阵列数据重建，以评估我们提出的方法的性能。在昼夜节律调控途径中，我们主要确定了生物钟和光周期基因之间的相互作用，与已知的调控机制一致。我们还发现了隐色素和光敏素之间现在不太为人所知的规则。在代谢转换途径中，酶基因间的因果关系可以被适当检测。
The cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach. In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network) to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network). Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data. We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are reconstructed using microarray data to evaluate the performance of our proposed method. In the circadian regulatory pathway, we identified mainly the interactions between the biological clock and the photoperiodic genes consistent with the known regulatory mechanisms. We also discovered the now less-known regulations between crytochrome and phytochrome. In the metabolic shift pathway, the casual relationship of enzymatic genes could be detected properly.
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DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
7.4
作者：
Green, RM;Tingay, S;Tobin, EM
通讯作者：
Tobin, EM
影响因子：
4.8
作者：
Dickinson, JR;Eshantha, L;Hewlins, MJE
通讯作者：
Hewlins, MJE
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
影响因子：
3.5
作者：
Carré, IA
通讯作者：
Carré, IA