Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.
Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.
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DOI：
10.1186/1471-2105-11-413
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发表时间：
2010-08-04
影响因子：
3
通讯作者：
Tomlin, Claire J
中科院分区：
文献类型：
--
作者：
Aswani, Anil;Keranen, Soile V E;Tomlin, Claire J
背景技术背景：转录因子和它们的靶基因的表达水平之间的相关性可以用来推断动物调控网络内的相互作用，但目前的方法是有限的，在他们的能力作出正确的predictions.RESULTS：在这里，我们描述了一种新的方法，它使用非参数统计生成常微分方程（ODE）模型的表达数据。与其他动态方法相比，我们的方法需要最少的信息的数学结构的ODE;它不使用定性描述的网络内的相互作用;它采用了新的统计数据，以防止过度拟合。它生成因子活性的时空图，突出显示不同调节因子可能影响靶基因表达水平的时间和空间位置。我们确定了一个ODE模型的前夕mRNA模式形成的果蝇胚盘，并表明，这再现了实验模式。与非动态的空间相关模型相比，我们的ODE与实验测量的模式的一致性提高了59%。我们的模型表明，蛋白质因子往往有可能表现为激活剂和抑制剂相同的顺式调节模块取决于因子的浓度，并意味着不同的模式激活和抑制。我们的方法提供了网络中转录因子调控潜力的客观量化，适用于低维和中维基因表达数据集，并且包括对现有动态和静态模型的改进。
BACKGROUND: The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.RESULTS: Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.CONCLUSIONS: Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.