Li Xianhua, Ribaudo Nicholas, Huang Zuyi Jacky
Department of Chemical Engineering, Villanova University, Villanova, PA 19085, USA.
Department of Chemical Engineering, Villanova University, Villanova, PA 19085, USA ; The Center for Nonlinear Dynamics & Control (CENDAC), Villanova University, Villanova, PA 19085, USA ; Villanova Center for the Advancement of Sustainability in Engineering (VCASE), Villanova University, Villanova, PA 19085, USA.
Biomed Res Int. 2015;2015:415083. doi: 10.1155/2015/415083. Epub 2015 Nov 29.
Detection of malfunctioning reactions or molecules from clinical data is essential for disease treatments. In order to find an alternative to the existing oversimplistic mathematical models, a kinetic model is developed in this work to infer the malfunctioning reactions/molecules by quantifying the similarity between the clinical profile and the output profiles predicted from the model in which certain reactions/molecules malfunction. The new approach was tested in IL-6 and TNF-α/NF-κB signaling pathway, for four abnormal conditions including up/downregulation of single reaction rate constants and up/downregulation of single molecules. Since limited quantitative clinical data were available, the IL-6 ODE model was used to generate artificial clinical data for the abnormal steady-state value shown in two key molecules: nuclear STAT3 and SOCS3. Similarly, the TNF-α/NF-κB model was used to obtain the data in which abnormal oscillation dynamic was shown in the profile of NF-κB. The results show that the approach developed in this study was able to successfully identify the malfunctioning reactions and molecules from the clinical data. It was also found that this new approach was noise-robust and that it managed to reveal unique solution for the faulty components in a network.
从临床数据中检测出功能异常的反应或分子对于疾病治疗至关重要。为了找到现有过于简单的数学模型的替代方法，本研究开发了一种动力学模型，通过量化临床特征与模型预测的输出特征（其中某些反应/分子功能异常）之间的相似性，来推断功能异常的反应/分子。该新方法在IL-6和TNF-α/NF-κB信号通路中针对四种异常情况进行了测试，包括单个反应速率常数的上调/下调以及单个分子的上调/下调。由于可用的定量临床数据有限，使用IL-6常微分方程模型生成了两个关键分子（核STAT3和SOCS3）中显示异常稳态值的人工临床数据。同样，使用TNF-α/NF-κB模型获得了NF-κB图谱中显示异常振荡动态的数据。结果表明，本研究开发的方法能够成功地从临床数据中识别出功能异常的反应和分子。还发现这种新方法具有抗噪声能力，并且能够揭示网络中故障组件的独特解决方案。
