ArticleNature protocols2018
Defining informative priors for ensemble modeling in systems biology.
Article in Nature protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Kinetic sampling shows the effect of medium composition on metabolic control in Saccharomyces cerevisiae.Microbial cell factories · 2026Article
- Combined effect of polyphosphate kinase and lon protease in Streptomyces coelicolor A3(2) antibiotic production.Archives of microbiology · 2024Article
- An adaptableMolecular omics · 2024Article
- Spectral expansion methods for prediction uncertainty quantification in systems biology.Frontiers in systems biology · 2024Article
- dynamAedes: a unified modelling framework for invasive Aedes mosquitoes.Parasites & vectors · 2022Article
- KinMod database: a tool for investigating metabolic regulation.Database : the journal of biological databases and curation · 2022Article
- Bayesian parameter estimation for dynamical models in systems biology.PLoS computational biology · 2022Article
- Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States.Metabolites · 2021Article
- Multi-omics Study of Planobispora rosea, Producer of the Thiopeptide Antibiotic GE2270A.mSystems · 2021Article
- Synthetic biology approaches to actinomycete strain improvement.FEMS microbiology letters · 2021Review
- Unravelling the γ-butyrolactone network in Streptomyces coelicolor by computational ensemble modelling.PLoS computational biology · 2020Article
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Authors and funding
4 authors.
Funding
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.
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30353176What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.