Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.
Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.
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DOI：
10.1186/1752-0509-4-142
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发表时间：
2010-10-24
影响因子：
--
通讯作者：
Ay A
中科院分区：
文献类型：
--
作者：
Dresch JM;Liu X;Arnosti DN;Ay A
基因表达的定量模型生成的参数值可以揭示生物学特征，如转录因子活性，协同性和阻遏物的局部效应。这种研究中的一个重要因素是敏感性分析，它决定了模型的输出对参数值变化的反应有多强烈。低灵敏度的参数可能无法准确估计，导致不必要的结论。低灵敏度可能反映了生物数据的性质，也可能是模型结构的结果。在这里，我们专注于分析的热力学模型，已被广泛用于分析基因转录。提取的参数值已被生物学解释，但到目前为止，很少有人注意到在这种情况下的参数敏感性。我们将局部和全局敏感性分析应用于最近的两个转录模型，以确定各个参数的敏感性。我们发现，在一种情况下，阻遏物的效率值是非常敏感的，而蛋白质的cooperativities值不是，并提供见解，为什么这些差异的敏感性源于生物效应和结构的应用模型。在第二种情况下，我们证明，被认为是证明系统的依赖于活化剂-活化剂协同性的参数是相对不敏感的。我们发现，有许多参数集，不满足的关系proferred作为最佳的解决方案，这表明分析的两种类型的转录增强子之间的结构差异可能不那么简单，改变激活剂协同性。我们的研究结果强调，需要灵敏度分析，以检查模型的构建和用于建模转录过程的生物数据的形式，以确定热力学模型的估计参数值的意义。参数敏感性的知识可以提供必要的背景，以确定如何在生物系统中解释建模结果。
Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context. We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity. Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary context to determine how modeling results should be interpreted in biological systems.
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影响因子：
4.3
作者：
Lillacci G;Khammash M
通讯作者：
Khammash M
影响因子：
9.9
作者：
Fakhouri, Walid D.;Ay, Ahmet;Sayal, Rupinder;Dresch, Jacqueline;Dayringer, Evan;Arnosti, David N.
通讯作者：
Arnosti, David N.
影响因子：
30.8
作者：
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通讯作者：
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DOI：
10.1159/000070462
发表时间：
2003-01-01
期刊：
ComPlexUs
影响因子：
--
作者：
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通讯作者：
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影响因子：
6.4
作者：
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通讯作者：
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