Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells.
Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells.
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DOI：
10.1371/journal.pcbi.1002815
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发表时间：
2012
影响因子：
4.3
通讯作者：
Wolkenhauer O
中科院分区：
文献类型：
--
作者：
Rateitschak K;Winter F;Lange F;Jaster R;Wolkenhauer O
目前的工作阐明了如何参数可识别性分析可以用来深入了解实验系统中的差异，以及如何处理参数估计的不确定性。本文介绍的病例研究调查了干扰素-γ（IFNγ）诱导的STAT 1信号传导在胰腺癌发展中起关键作用的两种细胞类型：胰腺星状细胞和癌细胞。IFNγ抑制两种类型细胞的生长，并且可能是同时打击癌细胞和基质细胞的试剂的原型。我们结合时间过程实验与数学建模，专注于常见的情况下，从不同的细胞类型的实验时间序列的配置文件之间的变化，观察。为了了解生化反应是如何导致观察到的变化，我们进行了参数可识别性分析。通过比较参数值估计值的置信区间和模型轨迹的变异性，我们成功地确定了胰腺星状细胞和癌细胞中不同的反应。我们的分析表明，有用的信息也可以从不可识别的参数。为了预测潜在的治疗靶点，我们研究了可识别和不可识别参数值的不确定性的后果。有趣的是，模型变量的灵敏度对参数变化和IFNγ诱导的胰腺星状细胞和癌细胞中STAT 1信号传导之间的差异具有鲁棒性。这为预测对两种细胞类型都有效的治疗靶点提供了基础。为了预测治疗靶点和设计治疗方法，重要的是研究不同细胞类型的相同途径。这与癌症研究特别相关，其中几种细胞类型参与致癌作用。胰腺癌被激活的胰腺星状细胞增强。因此，似乎有可能找到一种有效的治疗方法来打击星状细胞和癌细胞。细胞因子IFNγ是两种细胞类型中的增殖抑制剂。IFNγ的抗增殖作用由STAT 1信号转导介导。一个重要的方面是确定导致两种细胞类型之间磷酸化STAT 1的初始增加和STAT 1核积累的时间分布的差异的那些反应。我们通过对校准的数学模型进行参数可识别性分析来研究这方面的问题。我们计算了估计参数值的置信区间，发现它们提供了对差异背后的反应的见解。敏感性分析的一个关键发现阐明了用于增强STAT 1活性的预测靶点对参数不确定性是稳健的，而且它们在两种细胞类型之间是稳健的。因此，我们的案例研究举例说明了可识别性和敏感性分析如何为预测潜在的治疗靶点提供基础。
The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types. For the prediction of therapeutic targets and the design of therapies, it is important to study the same pathway across different cell types. This is particularly relevant for cancer research, where several cell types are involved in carcinogenesis. Pancreatic cancer is enhanced by activated pancreatic stellate cells. It would thus seem plausible for an effective therapy to hit stellate and cancer cells. The cytokine IFNγ is an inhibitor of proliferation in both cell types. Antiproliferative effects of IFNγ are mediated by STAT1 signalling. An important aspect is to determine those reactions that cause the differences in the initial increase of phosphorylated STAT1 and in the temporal profile of STAT1 nuclear accumulation between the two cell types. We examined this aspect by performing a parameter identifiability analysis for calibrated mathematical models. We calculated confidence intervals of the estimated parameter values and found that they provide insights into reactions underlying the differences. A key finding of sensitivity analysis elucidated that predicted targets for enhancement of STAT1 activity are robust against parameter uncertainty and moreover they are robust between the two cell types. Our case study therefore exemplified how identifiability and sensitivity analysis can provide a basis for the prediction of potential therapeutic targets.
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DOI：
10.1016/j.bpg.2007.10.004
发表时间：
2008-01-01
影响因子：
3.2
作者：
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