Abstract

We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC) with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a) individual biochemical reactions, (b) nonlinear network dynamics approaching to attractors, and (c) cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a) and (c) are stochastic while that with (b) is dominantly deterministic. Both (b) and (c) are emergent properties of a dynamic biochemical network; We suggest that the (c) is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b) in a “punctuated equilibrium” manner.

Highlights

  • Quantitative modelling in terms of mathematical equations is the foundation of modern physical sciences

  • This begs an answer to the question: What is the theory one should use in modelling a biochemical reaction system in its living environment?

  • Michaelis-Menten kinetics into account, interestingly, we discover that in this case, our model of phosphorylation-dephosphorylation cycle (PdPC)

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Summary

Introduction

Quantitative modelling in terms of mathematical equations is the foundation of modern physical sciences. While this model is very simple, the issues arise from the model are important, and have not been widely discussed It is well-known, and as we shall discuss, nonlinear open chemical and biochemical reaction systems can exhibit bistability, which plays a crucial role in cellular genetic [35] and signal regulations [20,36]. The system is modelled in terms of a CME, and the bistability and (saddle-node) bifurcation are purely stochastic phenomenon They only occur in reaction systems with small volume and small number of molecules. Curve with χ = 1 is for the PdPC with hyperbolic activation without feeback: y = 1+θ first-order autocatalysis, following Equation (31) It exhibits an extreme version of sigmoldal shape called delayed onset. Both mechanisms lead to the same mathematical expression of the activation curve

Stochastic Bistability and Bifurcation without Deterministic Counterpart
Sel’kov-Goldbeter-Lefever’s Glycolytic Oscillator
Conclusions
Michaelis-Menten model
Keizer’s Model
Stationary Distribution
Schnakenberg Model
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