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Protein Synthesis Driven by Dynamical Stochastic Transcription.
Guilherme C P Innocentini1,2, Michael Forger3, Ovidiu Radulescu4
1Departamento de Matemática Aplicada, Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão, 1010, Cidade Universitária, São Paulo, SP, 05508-090, Brazil. ginnocentini@gmail.com.
Bulletin of Mathematical Biology
|December 17, 2015
Summary
This study introduces a mathematical model linking gene transcription and protein translation. The framework uses master equations for mRNA and random differential equations for protein dynamics, enabling prediction of protein population behavior.
Area of Science:
- Molecular Biology
- Biophysics
- Mathematical Biology
Background:
- Understanding the interplay between transcription and translation is crucial for gene expression.
- Existing models often simplify the stochastic nature of these molecular processes.
Purpose of the Study:
- To develop a unified mathematical framework for coupled transcription-translation.
- To analyze the dynamics of protein production influenced by mRNA fluctuations.
Main Methods:
- Utilizing master equations to model mRNA production dynamics.
- Employing random differential equations to describe protein density.
- Introducing a stochastic perturbation to couple transcription and translation.
Main Results:
- Derivation of analytical time-dependent distributions for mRNA numbers.
- Calculation of the probability density dynamics for protein populations.
- Demonstration of how mRNA stochasticity impacts protein dynamics.
Conclusions:
- The proposed framework provides a robust method for studying coupled gene expression.
- This approach allows for accurate prediction of protein population dynamics.
- The model highlights the significance of stochastic effects in molecular biology.
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