复杂系统生物学模型的贝叶斯不确定性分析：仿真、全局参数搜索和基因功能评估
Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions
摘要：
背景：许多数学模型现在已被用于系统生物学的各个领域。这些模型越来越多地涉及大量的未知参数，具有复杂的结构，这可能导致相对于分析需要的大量评估时间，并且需要与观察到的数据进行比较。正确分析这些模型通常需要在高维参数空间上进行全局参数搜索，该参数搜索包含并尊重最重要的不确定性来源。这可能是一项极其困难的任务，但它对于任何生物系统的任何有意义的推断或预测都是必不可少的。因此，它代表了对整个系统生物学的根本挑战。结果如下：介绍了用于复杂模型不确定性分析的贝叶斯统计方法，该方法旨在解决高维全局参数搜索问题。贝叶斯模拟器，模仿系统生物学模型，但这是非常快的评估是嵌入在一个迭代的历史匹配：一个有效的方法来搜索高维空间内一个更正式的统计设置，同时纳入主要来源的不确定性。该方法通过应用到两个模型的激素串扰在拟南芥根的发展，其中有32个速率参数，我们确定的速率参数值，导致可接受的匹配观察到的趋势数据集证明。生物学后果的比较，包括基因功能的评价，进行了说明。
英文摘要：
Background: Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Results: Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embedded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to two models of hormonal crosstalk in Arabidopsis root development, which have 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches to observed trend data. The biological consequences of the resulting comparison, including the evaluation of gene functions, are described.
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