Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, United Kingdom.
Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, United Kingdom.
Nat Protoc. 2018 Nov;13(11):2643-2663. doi: 10.1038/s41596-018-0056-z.
Ensemble modeling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches that sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modeling have been published, the issue of generating informative priors has not yet been addressed. Here, we present a protocol that aims to fill this gap. This protocol discusses the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessment of their plausibility, and creation of log-normal probability distributions that can be used as informative priors in ensemble modeling. Furthermore, the protocol enables sampling from the generated distributions while maintaining thermodynamic consistency. Once all parameter values have been retrieved from literature and databases, the protocol can be implemented within ~5-10 min per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modeling, especially in fields such as synthetic biology and systems medicine.
分子系统生物学中的集合建模需要将动力学参数数据可重复地转化为有用的概率分布（先验），以及从这些分布中采样参数而不违反整体模型热力学一致性的方法。尽管已经发表了许多用于集合建模的开创性框架，但生成有用先验的问题尚未得到解决。在这里，我们提出了一个旨在填补这一空白的方案。该方案讨论了从各种来源（文献、数据库和实验）收集参数值、评估其合理性以及创建对数正态概率分布的问题，这些分布可作为集合建模中的有用先验。此外，该方案还能够在保持热力学一致性的情况下从生成的分布中进行采样。一旦从文献和数据库中检索到所有参数值，每个参数的方案实施时间约为 5-10 分钟。本方案的目的是促进集合建模中有用分布的设计和使用，特别是在合成生物学和系统医学等领域。
