Global transcription regulation of RK2 plasmids: a case study in the combined use of dynamical mathematical models and statistical inference for integration of experimental data and hypothesis exploration.
Global transcription regulation of RK2 plasmids: a case study in the combined use of dynamical mathematical models and statistical inference for integration of experimental data and hypothesis exploration.
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DOI：
10.1186/1752-0509-5-119
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发表时间：
2011-07-29
影响因子：
--
通讯作者：
Stekel DJ
中科院分区：
文献类型：
--
作者：
Herman D;Thomas CM;Stekel DJ
IncP-1质粒是广泛存在于临床和环境细菌中的宿主质粒。它们通常携带抗生素抗性或分解代谢途径的基因。原型IncP-1质粒RK2是一个具有良好特征的生物系统，具有完全测序和注释的基因组和广泛的实验测量。它的中央控制操纵子，编码两个全局调节器KorA和KorB，是一个自然的负自我调节操纵子的例子。为了加深我们对该操纵子调控的理解，我们利用常微分方程构建了一个动力学数学模型，并采用贝叶斯推理方案，即使用Metropolis-Hastings算法的马尔可夫链蒙特卡罗（MCMC），作为整合实验测量和先验知识的一种方式。我们还比较了MCMC和代谢控制分析（MCA）作为确定模型参数敏感性的方法。我们确定了两组不同的参数值，具有不同的生物学解释，适合并解释实验数据。这使我们能够突出抑制蛋白作为二聚体的比例，作为定义系统动力学的关键实验测量。对关节后验分布的分析表明，蛋白质合成参数与KorA或KorB二聚体的部分抑制之间存在相关性，表明需要使用关节后验进行正确的参数估计。利用MCA，我们证明了系统对生长速率高度敏感，但对阻遏物单体化率在其选择的值区域不敏感；后者的结果也得到了MCMC的证实。最后，通过对KorA或KorB二聚体部分抑制的一系列不同模型的改进，我们表明，包括KorA和KorB部分抑制的模型与现有的实验数据最兼容。我们已经证明，动态数学模型与贝叶斯推理的结合在整合各种实验数据和确定IncP-1中央控制操纵子的关键决定因素和参数方面是有价值的。此外，我们还证明了贝叶斯推理和MCA是识别敏感参数的互补方法。我们认为，这证明了将这种方法组合应用于系统生物学动态建模的通用价值。
IncP-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria. They often carry genes for antibiotic resistance or catabolic pathways. The archetypal IncP-1 plasmid RK2 is a well-characterized biological system, with a fully sequenced and annotated genome and wide range of experimental measurements. Its central control operon, encoding two global regulators KorA and KorB, is a natural example of a negatively self-regulated operon. To increase our understanding of the regulation of this operon, we have constructed a dynamical mathematical model using Ordinary Differential Equations, and employed a Bayesian inference scheme, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, as a way of integrating experimental measurements and a priori knowledge. We also compared MCMC and Metabolic Control Analysis (MCA) as approaches for determining the sensitivity of model parameters. We identified two distinct sets of parameter values, with different biological interpretations, that fit and explain the experimental data. This allowed us to highlight the proportion of repressor protein as dimers as a key experimental measurement defining the dynamics of the system. Analysis of joint posterior distributions led to the identification of correlations between parameters for protein synthesis and partial repression by KorA or KorB dimers, indicating the necessary use of joint posteriors for correct parameter estimation. Using MCA, we demonstrated that the system is highly sensitive to the growth rate but insensitive to repressor monomerization rates in their selected value regions; the latter outcome was also confirmed by MCMC. Finally, by examining a series of different model refinements for partial repression by KorA or KorB dimers alone, we showed that a model including partial repression by KorA and KorB was most compatible with existing experimental data. We have demonstrated that the combination of dynamical mathematical models with Bayesian inference is valuable in integrating diverse experimental data and identifying key determinants and parameters for the IncP-1 central control operon. Moreover, we have shown that Bayesian inference and MCA are complementary methods for identification of sensitive parameters. We propose that this demonstrates generic value in applying this combination of approaches to systems biology dynamical modelling.
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影响因子：
1.1
作者：
Girolami, Mark
通讯作者：
Girolami, Mark
影响因子：
3
作者：
Komorowski M;Finkenstädt B;Harper CV;Rand DA
通讯作者：
Rand DA
影响因子：
14.9
作者：
BALZER, D;ZIEGELIN, G;LANKA, E
通讯作者：
LANKA, E
影响因子：
2.6
作者：
Bahl, Martin Iain;Hansen, Lars Hestbjerg;Sorensen, Soren J.
通讯作者：
Sorensen, Soren J.
影响因子：
56.9
作者：
MEYER, R;FIGURSKI, D;HELINSKI, DR
通讯作者：
HELINSKI, DR