Defining informative priors for ensemble modeling in systems biologyCitation formats

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Defining informative priors for ensemble modeling in systems biology. / Tsigkinopoulou, Areti; Hawari, Aliah; Uttley, Megan; Breitling, Rainer.

In: Nature protocols, Vol. 13, No. 11, 01.11.2018, p. 2643-2663.

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Tsigkinopoulou, Areti ; Hawari, Aliah ; Uttley, Megan ; Breitling, Rainer. / Defining informative priors for ensemble modeling in systems biology. In: Nature protocols. 2018 ; Vol. 13, No. 11. pp. 2643-2663.

Bibtex

@article{fdaeec1ef1ef4d879e82486d4f7b8bde,
title = "Defining informative priors for ensemble modeling in systems biology",
abstract = "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.",
author = "Areti Tsigkinopoulou and Aliah Hawari and Megan Uttley and Rainer Breitling",
year = "2018",
month = nov,
day = "1",
doi = "10.1038/s41596-018-0056-z",
language = "English",
volume = "13",
pages = "2643--2663",
journal = "Nature Protocols: recipes for researchers",
issn = "1754-2189",
publisher = "Springer Nature",
number = "11",

}

RIS

TY - JOUR

T1 - Defining informative priors for ensemble modeling in systems biology

AU - Tsigkinopoulou, Areti

AU - Hawari, Aliah

AU - Uttley, Megan

AU - Breitling, Rainer

PY - 2018/11/1

Y1 - 2018/11/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85055474663&partnerID=8YFLogxK

U2 - 10.1038/s41596-018-0056-z

DO - 10.1038/s41596-018-0056-z

M3 - Article

VL - 13

SP - 2643

EP - 2663

JO - Nature Protocols: recipes for researchers

JF - Nature Protocols: recipes for researchers

SN - 1754-2189

IS - 11

ER -