ArticleNucleic acids research2015
An implementation of the Gillespie algorithm for RNA kinetics with logarithmic time update.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Design of a self-regulating mRNA gene circuit.Scientific reports · 2024Article
- RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics.ACS synthetic biology · 2024Review
- Modelling ribosome kinetics and translational control on dynamic mRNA.PLoS computational biology · 2023Article
- Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.Bioinformatics (Oxford, England) · 2020Article
- A stochastic model for simulating ribosome kinetics in vivo.PLoS computational biology · 2020Article
- RNA folding kinetics using Monte Carlo and Gillespie algorithms.Journal of mathematical biology · 2018Article
- A Model for Viral Assembly around an Explicit RNA Sequence Generates an Implicit Fitness Landscape.Biophysical journal · 2017Article
- Network Properties of the Ensemble of RNA Structures.PloS one · 2015Article
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Abstract
In this paper I outline a fast method called KFOLD for implementing the Gillepie algorithm to stochastically sample the folding kinetics of an RNA molecule at single base-pair resolution. In the same fashion as the KINFOLD algorithm, which also uses the Gillespie algorithm to predict folding kinetics, KFOLD stochastically chooses a new RNA secondary structure state that is accessible from the current state by a single base-pair addition/deletion following the Gillespie procedure. However, unlike KINFOLD, the KFOLD algorithm utilizes the fact that many of the base-pair addition/deletion reactions and their corresponding rates do not change between each step in the algorithm. This allows KFOLD to achieve a substantial speed-up in the time required to compute a prediction of the folding pathway and, for a fixed number of base-pair moves, performs logarithmically with sequence size. This increase in speed opens up the possibility of studying the kinetics of much longer RNA sequences at single base-pair resolution while also allowing for the RNA folding statistics of smaller RNA sequences to be computed much more quickly.
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