Abstract
We propose a Markov chain approximation of the delayed stochastic simulation algorithm to infer properties of the mechanisms in prokaryote transcription from the dynamics of RNA levels. We model transcription using the delayed stochastic modelling strategy and realistic parameter values for rate of transcription initiation and RNA degradation. From the model, we generate time series of RNA levels at the single molecule level, from which we use the method to infer the duration of the promoter open complex formation. This is found to be possible even when adding external Gaussian noise to the RNA levels.
Highlights
Gene expression dynamics is influenced by even small fluctuations on the levels of various molecular species, such as RNA polymerases and transcription factors
We propose a Markov chain approximation of the delayed stochastic simulation algorithm to infer properties of the mechanisms in prokaryote transcription from the dynamics of RNA levels
We generate time series of RNA levels at the single molecule level, from which we use the method to infer the duration of the promoter open complex formation
Summary
Gene expression dynamics is influenced by even small fluctuations on the levels of various molecular species, such as RNA polymerases and transcription factors. Even the presence of a single molecule can cause phenotypic switching [1]. This makes the cellular metabolism inherently stochastic [2]. The stochasticity in the abundance of a substance is in general thought of being noise that obscures a signal that carries information relevant to the cell. Several modelling strategies have been proposed for accurately accounting for noise in the dynamics of gene regulatory networks (GRNs) [2, 4,5,6,7]
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