Biosystems, Biomodeling and Bioprocesses Group, Université Libre de Bruxelles, Brussels, Belgium.
Bioinformatics. 2011 Apr 1;27(7):961-7. doi: 10.1093/bioinformatics/btr069. Epub 2011 Feb 10.
Oscillating signals produced by biological systems have shapes, described by their Fourier spectra, that can potentially reveal the mechanisms that generate them. Extracting this information from measured signals is interesting for the validation of theoretical models, discovery and classification of interaction types, and for optimal experiment design.
An automated workflow is described for the analysis of oscillating signals. A software package is developed to match signal shapes to hundreds of a priori viable model structures defined by a class of first-order differential equations. The package computes parameter values for each model by exploiting the mode decomposition of oscillating signals and formulating the matching problem in terms of systems of simultaneous polynomial equations. On the basis of the computed parameter values, the software returns a list of models consistent with the data. In validation tests with synthetic datasets, it not only shortlists those model structures used to generate the data but also shows that excellent fits can sometimes be achieved with alternative equations. The listing of all consistent equations is indicative of how further invalidation might be achieved with additional information. When applied to data from a microarray experiment on mice, the procedure finds several candidate model structures to describe interactions related to the circadian rhythm. This shows that experimental data on oscillators is indeed rich in information about gene regulation mechanisms.
The software package is available at http://babylone.ulb.ac.be/autoosc/.
生物系统产生的振荡信号具有形状，可以通过其傅里叶谱来描述，这些形状可能揭示产生这些信号的机制。从测量信号中提取这些信息对于验证理论模型、发现和分类相互作用类型以及优化实验设计都很有趣。
本文描述了一种用于分析振荡信号的自动化工作流程。开发了一个软件包，用于将信号形状与通过一类一阶微分方程定义的数百个可行的先验模型结构相匹配。该软件包通过利用振荡信号的模态分解并根据同时的多项式方程组来表述匹配问题，从而计算每个模型的参数值。基于计算出的参数值，软件返回与数据一致的模型列表。在使用合成数据集进行的验证测试中，它不仅可以筛选出用于生成数据的模型结构，还表明有时可以通过替代方程获得极好的拟合。列出所有一致的方程表明，通过其他信息可以进一步验证哪些是无效的。当应用于来自老鼠的微阵列实验的数据时，该程序找到了描述与生物钟相关的相互作用的几个候选模型结构。这表明实验数据确实包含有关基因调控机制的丰富信息。
该软件包可在 http://babylone.ulb.ac.be/autoosc/ 上获得。
