Stochastic model of siRNA endosomal escape mediated by fusogenic peptides

Gene silencing via small interfering RNA (siRNA) represents a transformative tool in cancer therapy, offering specificity and reduced off-target effects compared to conventional treatments. A crucial step in siRNA-based therapies is endosomal escape, the release of siRNA from endosomes into the cyto...

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Published in:Mathematical biosciences Vol. 387; p. 109476
Main Authors: Yadav, Nisha, Boulos, Jessica, Alexander-Bryant, Angela, Cook, Keisha
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.09.2025
Subjects:
Bayes Theorem
Cancer
Compartmental model
Computer Simulation
Endosomal escape
Endosomes - metabolism
Humans
Image processing
Microscopy, Fluorescence
Models, Biological
Parameter estimation
Peptides
Peptides - metabolism
RNA, Small Interfering - metabolism
Stochastic Processes
Stochastic simulation algorithm
Parameter estimation
Compartmental model
Peptides
Image processing
Stochastic simulation algorithm
Endosomal escape
Cancer
ISSN:0025-5564, 1879-3134, 1879-3134
Online Access:Get full text
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Summary:Gene silencing via small interfering RNA (siRNA) represents a transformative tool in cancer therapy, offering specificity and reduced off-target effects compared to conventional treatments. A crucial step in siRNA-based therapies is endosomal escape, the release of siRNA from endosomes into the cytoplasm. Quantifying endosomal escape is challenging due to the dynamic nature of the process and limitations in imaging and analytical techniques. Traditional methods often rely on fluorescence intensity measurements or manual image processing, which are time-intensive and fail to capture continuous dynamics. This paper presents a novel computational framework that integrates automated image processing to analyze time-lapse fluorescent microscopy data of endosomal escape, hierarchical Bayesian inference, and stochastic simulations. Our method employs image segmentation techniques such as binary masks, Gaussian filters, and multichannel color quantification to extract precise spatial and temporal data from microscopy images. Using a hierarchical Bayesian approach, we estimate the parameters of a compartmental model that describes endosomal escape dynamics, accounting for variability over time. These parameters inform a Gillespie stochastic simulation algorithm, ensuring realistic simulations of siRNA release events over time. By combining these techniques, our framework provides a scalable and reproducible method for quantifying endosomal escape. The model captures uncertainty and variability in parameter estimation, and endosomal escape dynamics. Additionally, synthetic data generation allows researchers to validate experimental findings and explore alternative conditions without extensive laboratory work. This integrated approach not only improves the accuracy of endosomal escape quantification but also provides predictive insights for optimizing siRNA delivery systems and advancing gene therapy research. •Exact quantification of endosomal escape.•Stochastic simulation model of endosomal escape.•Image processing.•Parameter estimation.•Live cell imaging.
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ISSN:0025-5564
1879-3134
1879-3134
DOI:10.1016/j.mbs.2025.109476