Defining informative priors for ensemble modelling in systems biology

Areti Tsigkinopoulou, Aliah Hawari, Megan Uttley, Rainer Breitling

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Ensemble modelling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches to sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modelling have been published, the issue of generating informative priors has not been addressed yet. Here we present a protocol which aims to fill this gap. The protocol concerns the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessing their plausibility, and creating log-normal probability distributions, which can be used as informative priors in ensemble modelling. 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 approximately 5–10 minutes per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modelling especially in fields such as synthetic biology and systems medicine.
Original languageEnglish
Pages (from-to)2643-2663
Number of pages20
JournalNature protocols
Early online date23 Oct 2018
Publication statusPublished - 2018

Research Beacons, Institutes and Platforms

  • Manchester Institute of Biotechnology


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