Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data
Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data
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DOI：
10.1016/j.bpj.2009.09.031
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发表时间：
2009-12-16
影响因子：
3.4
通讯作者：
Wiggins, Chris H.
中科院分区：
文献类型：
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作者：
Bronson, Jonathan E.;Fei, Jingyi;Wiggins, Chris H.
单分子福斯特共振能量转移 (smFRET) 实验提供的时间序列数据不仅可以推断描述分子复合物的模型参数（例如速率常数），还可以推断有关模型本身的信息（例如构象状态的数量）。解决这样的状态是否存在或存在多少需要仔细对待模型选择问题，这里意味着区分具有不同状态数量的模型。最直接的模型选择方法将最大似然（选择最可能的参数值）的常见想法概括为最大证据：选择最可能的模型。在任何一种情况下，这样的推论都会带来巨大的计算挑战，我们在这里通过利用称为变分贝叶斯期望最大化的近似技术来解决这个挑战。我们演示了如何将该技术应用于时态数据，例如 smFRET 时间序列；相对于最大似然法，表现出优异的统计一致性；比较其在核糖体实验生成的 smFRET 数据上的性能；并说明这种概率或生成模型中的模型选择如何促进当前生物物理学中密切相关的时间数据的分析。此分析中使用的源代码（包括图形用户界面）可通过 hffp://vbFRET.sourceforge.net 开源获得。
Time series data provided by single-molecule Forster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or how many of them exist requires a careful approach to the problem of model selection, here meaning discrimination among models with differing numbers of states. The most straightforward approach to model selection generalizes the common idea of maximum likelihood-selecting the most likely parameter values-to maximum evidence: selecting the most likely model. In either case, such an inference presents a tremendous computational challenge, which we here address by exploiting an approximation technique termed variational Bayesian expectation maximization. We demonstrate how this technique can be applied to temporal data such as smFRET time series; show superior statistical consistency relative to the maximum likelihood approach; compare its performance on smFRET data generated from experiments on the ribosome; and illustrate how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics. Source code used in this analysis, including a graphical user interface, is available open source via hffp://vbFRET.sourceforge.net.