Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells.
Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells.
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DOI：
10.1186/1752-0509-8-76
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发表时间：
2014-06-25
影响因子：
--
通讯作者：
Bois FY
中科院分区：
文献类型：
--
作者：
Hamon J;Jennings P;Bois FY
将组学数据流用于构建改进的系统生物学模型，对于改进其生物学结果的预测具有很大的潜力。我们最近发现，环孢素A（CsA）强烈激活核因子（红细胞衍生2）样2途径（Nrf 2）在肾近端肾小管上皮细胞（RPTECs）暴露在体外。我们在这里提出了一个定量校准的微分方程模型的Nrf 2途径与我们收集的组学数据的子集。CsA在细胞、培养基和瓶壁之间交换的体外药代动力学数据，以及组学标志物对CsA暴露的反应时间过程的数据与耦合PK-系统生物学模型相当吻合。模型参数值的后验统计分布通过贝叶斯框架中的马尔可夫链蒙特卡罗抽样获得。在5 μM CsA重复暴露时，ROS产生和控制出现复杂的循环模式。在15 μM暴露下发现平台反应。在这些暴露水平之上不久，该模型预测细胞ROS量不成比例地增加，这与RPTEC中CsA的体外EC 50约为40 μM一致。所提出的模型可以用来分析和预测细胞对氧化应激的反应，提供足够的数据来设置其参数为细胞特异性值。组学数据可以用于贝叶斯统计框架中的效果，该框架保留了关于可能参数值的先验信息。
Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells (RPTECs) exposed in vitro. We present here a quantitative calibration of a differential equation model of the Nrf2 pathway with a subset of the omics data we collected. In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs. The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.
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