Properties of cell death models calibrated and compared using Bayesian approaches.
Properties of cell death models calibrated and compared using Bayesian approaches.
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DOI：
10.1038/msb.2012.69
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发表时间：
2013
影响因子：
9.9
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中科院分区：
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使用模型来模拟和分析生物网络需要参数估计和模型识别的原则性方法。我们使用贝叶斯和蒙特卡罗方法来恢复受体介导的细胞死亡的质量作用模型的自由参数(初始蛋白质浓度和速率常数)的全概率分布。各个参数分布的宽度在很大程度上由不可识别性决定，但参数之间的协变，即使是那些很难确定的参数，也编码了基本信息。联合参数分布的知识使计算基于模型的预测的不确定性成为可能，而忽略它(例如，通过将参数视为值和方差的简单列表)会产生无意义的预测。根据联合分布计算贝叶斯因子，可以得出具有不同参数数量的竞争“直接”和“间接”凋亡模型的优势比(∼为20倍)。我们的结果表明，结合单细胞数据的贝叶斯模型校准和判别方法是一种在面对参数和拓扑不确定性的情况下区分相互竞争的假设的一般有用和严格的方法。
Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing ‘direct’ and ‘indirect’ apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.