Abstract
Random effect in cellular systems is an important topic in systems biology and often simulated with Gillespie’s stochastic simulation algorithm (SSA). Abridgment refers to model reduction that approximates a group of reactions by a smaller group with fewer species and reactions. This paper presents a theoretical analysis, based on comparison of the first exit time, for the abridgment on a linear chain reaction model motivated by systems with multiple phosphorylation sites. The analysis shows that if the relaxation time of the fast subsystem is much smaller than the mean firing time of the slow reactions, the abridgment can be applied with little error. This analysis is further verified with numerical experiments for models of bistable switch and oscillations in which linear chain system plays a critical role.
Highlights
With the rapid development in systems biology, biological models have become more and more complex
We test the accuracy of abridgment with a bistable switch model consisting of a chain reaction system
Numerical experiments on two oscillation models based on this bistable switch model are presented
Summary
With the rapid development in systems biology, biological models have become more and more complex. In order to study the stochastic effects, stochastic biochemical models have been built and simulated. Gillespie’s stochastic simulation algorithm (SSA) [1, 2] is one of the most important stochastic methods. The computational cost of the SSA can be very high, for systems with the multi-scale feature, which highlights the scale differences among reactions: Some reactions fire much faster than others, and those fast reactions often quickly reach equilibrium. Since the SSA tracks every reaction firing, in an SSA simulation most computational costs are spent on fast reactions. Slow reactions may be more important because very often they drive the dynamics of a system when fast reactions are in equilibrium
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