Abstract
Gene expression is a multifaceted process crucial to understanding molecular biology and pharmacology. Our research focuses on elucidating the intricate relationship between gene length and kinetic parameters, such as Si, Kon, Koff, and SKoff, which significantly influence the mean expression levels of genes.Using a two-state stochastic gene expression model implemented in Python, we analyzed single-cell transcriptomics data to predict kinetic parameters for each gene. We classified genes into short and long categories, revealing distinct patterns in the relationship between gene length and these parameters. Our results indicate that burst size plays a critical role in mean expression, highlighting its importance for identifying gene targets that require lower drug doses for therapeutic effects.
Competing Interest Statement
The authors have declared no competing interest.
