Kim Jaejik, Li Jiaxu, Venkatesh Srirangapatnam G, Darling Douglas S, Rempala Grzegorz A
Department of Biostatistics and Cancer Research Center, Georgia Regents University, Augusta, Georgia 30912, USA.
J Comput Biol. 2013 Jul;20(7):524-39. doi: 10.1089/cmb.2011.0222.
In modern systems biology the modeling of longitudinal data, such as changes in mRNA concentrations, is often of interest. Fully parametric, ordinary differential equations (ODE)-based models are typically developed for the purpose, but their lack of fit in some examples indicates that more flexible Bayesian models may be beneficial, particularly when there are relatively few data points available. However, under such sparse data scenarios it is often difficult to identify the most suitable model. The process of falsifying inappropriate candidate models is called model discrimination. We propose here a formal method of discrimination between competing Bayesian mixture-type longitudinal models that is both sensitive and sufficiently flexible to account for the complex variability of the longitudinal molecular data. The ideas from the field of Bayesian analysis of computer model validation are applied, along with modern Markov Chain Monte Carlo (MCMC) algorithms, in order to derive an appropriate Bayes discriminant rule. We restrict attention to the two-model comparison problem and present the application of the proposed rule to the mRNA data in the de-differentiation network of three mRNA concentrations in mammalian salivary glands as well as to a large synthetic dataset derived from the model used in the recent DREAM6 competition.
在现代系统生物学中，对纵向数据（如mRNA浓度变化）进行建模常常备受关注。通常会为此目的开发基于完全参数化常微分方程（ODE）的模型，但在某些情况下它们拟合不佳，这表明更灵活的贝叶斯模型可能更有益，特别是当可用数据点相对较少时。然而，在这种稀疏数据场景下，往往很难确定最合适的模型。对不合适的候选模型进行证伪的过程称为模型判别。我们在此提出一种在相互竞争的贝叶斯混合类型纵向模型之间进行判别的形式化方法，该方法既敏感又足够灵活，能够考虑纵向分子数据的复杂变异性。我们应用了计算机模型验证的贝叶斯分析领域的思想，以及现代马尔可夫链蒙特卡罗（MCMC）算法，以推导合适的贝叶斯判别规则。我们将注意力限制在双模型比较问题上，并展示所提出规则在哺乳动物唾液腺中三种mRNA浓度的去分化网络中的mRNA数据以及来自近期DREAM6竞赛中使用的模型的大型合成数据集上的应用。
