Intracellular viral infection kinetics using a stochastic approach

Abstract

Stochastic simulation is carried out to investigate intracellular viral reaction kinetics and the time evolution of the average particle number ( N̄) and coefficient variation (CV) for genome, template, and structural protein. The coefficient variation of these components is found to be ordered as: CVtemplate > CVstructural protein > CVgenome. The average particle number is also calculated via a deterministic approach. The magnitude value of the difference between the stochastic and deterministic approaches is found to be N̄template ≈ N̄structural protein > N̄genome. The Poisson algorithm has been used to investigate the number of particles in the dynamics of intracellular viral reaction kinetics. Our results show that the average particle number ( N̄) obtained using the Poisson algorithm for both dynamics and stationary states is le ss than that obtained using the mean field approach relative to the quantity ( N) found via the Gillespie algorithm. Also, the standard deviation of N̄ calculated via the Poisson algorithm is more than that obtained via the Gillespie algorithm. The equilibrium time for the population of particles via the Poisson algorithm is less than that via the Gillespie algorithm and the mean field approach. In the present study, the Gillespie algorithm is more reliable than the Poisson algorithm.