Special function methods for bursty models of transcription.

Area of Science:

• Systems Biology
• Computational Biology
• Molecular Biology

Background:

• Gene expression is a complex process involving multiple steps, including transcription and degradation.
• Understanding the dynamics of pre-mRNA and mature mRNA is crucial for deciphering gene regulation.
• Existing models often face computational challenges in analyzing stochastic gene expression.

Purpose of the Study:

• To develop a computationally efficient Markov model for gene expression analysis.
• To approximate complex stochastic integrals using special functions.
• To enable robust biophysical parameter inference from transcriptomics data.

Main Methods:

• Utilized a Markov model to describe pre-mRNA and mRNA dynamics.
• Employed special functions to approximate solutions of the stochastic system.
• Applied the method to simulated transcriptomics data for validation.
• Proposed a non-Bayesian parameter estimation approach using characteristic functions.

Main Results:

• Demonstrated that stochastic system solutions can be approximated by special functions.
• Showcased the generalization of the special function solution to various burst distributions.
• Achieved effective control over precision and runtime in parameter inference.
• Validated a novel non-Bayesian method for parameter estimation.

Conclusions:

• The developed Markov model and special function approximation offer an efficient approach to gene expression analysis.
• This method facilitates accurate biophysical parameter inference from transcriptomics data.
• The proposed non-Bayesian estimation technique provides a valuable alternative for parameter determination.