Chemical memory reactions induced bursting dynamics in gene expression

Area of Science:

• Systems Biology
• Biophysics
• Biochemical Engineering

Background:

• Biological systems exhibit memory, where past states influence current behavior, posing challenges for traditional kinetic modeling.
• Chemical memory reactions, firing under specific conditions, complicate existing stochastic approaches to chemical kinetics.
• Accurate modeling of these conditional reactions is crucial for understanding complex biological dynamics.

Purpose of the Study:

• To develop a novel theoretical framework for modeling chemical memory reactions in biological systems.
• To address the limitations of current stochastic methods in capturing conditional chemical kinetics.
• To investigate the role of memory reactions in gene expression and protein dynamics.

Main Methods:

• Introduction of memory chemical master equations and a memory stochastic simulation algorithm.
• Development of a stochastic model for single-gene expression to demonstrate memory reaction function.
• Application of the framework to model the p53-MDM2 core module dynamics.

Main Results:

• The proposed model successfully illustrates how memory reactions induce bursting dynamics in gene expression.
• Simulations of the p53-MDM2 module show memory reactions are key to sustained and damped oscillations in p53 protein numbers.
• The framework effectively captures experimentally observed gene expression dynamics.

Conclusions:

• The novel theory provides an effective tool for studying memory processes and conditional chemical reactions in complex biological systems.
• Memory reactions play a significant role in regulating gene expression and protein oscillations.
• This innovative modeling approach advances our understanding of biological memory.