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一种用于转录调控网络的随机微分方程模型。

A stochastic differential equation model for transcriptional regulatory networks.

作者信息

Climescu-Haulica Adriana, Quirk Michelle D

机构信息

Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, Grenoble, France.

出版信息

BMC Bioinformatics. 2007 May 24;8 Suppl 5(Suppl 5):S4. doi: 10.1186/1471-2105-8-S5-S4.

DOI:10.1186/1471-2105-8-S5-S4
PMID:17570863
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1892092/
Abstract

BACKGROUND

This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution.

RESULTS

We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels.

CONCLUSION

When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks.

摘要

背景

本研究基于时间依赖性基因表达数据集，探索局部转录调控网络的定量特征。通过随机微分方程模型拟合基因表达水平的动态变化，得出一组特定的调节因子及其作用。

结果

我们表明，跟踪时间参数的β型Sigmoid函数是一种新型的调控函数原型，具有提高图谱预测性能的作用。随机微分方程模型很好地跟踪了基因表达水平的动态变化。

结论

当应用于生物学假设并结合启动子分析时，本文提出的方法可改进转录调控网络模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/0850573e6e54/1471-2105-8-S5-S4-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/3b58c84431ef/1471-2105-8-S5-S4-1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/b6255eea78f3/1471-2105-8-S5-S4-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/0850573e6e54/1471-2105-8-S5-S4-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/3b58c84431ef/1471-2105-8-S5-S4-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/c1dfd4d57fa7/1471-2105-8-S5-S4-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/b603e78b63cc/1471-2105-8-S5-S4-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/06a726ba6a02/1471-2105-8-S5-S4-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/b6255eea78f3/1471-2105-8-S5-S4-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/217a/1892092/0850573e6e54/1471-2105-8-S5-S4-6.jpg

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