Stochastic Approach for Enzyme Reaction in Nano Size via Different Algorithms

Abstract

Stochastic simulations have been done for enzyme kinetics reaction with Michaelis-Menten mechanism in low population number. Gillespie and Poisson algorithms have been used for investigation of population number and fluctuation population around their mean values as a function of time. Our result shows that equilibrium time for population dynamics via Poisson algorithm is smaller than Gillespie algorithm. Variations of average population number versus time for all species have the following order: deterministic approach (mean fields) > Gillespie > Poisson. There is asymptotic limit for fluctuation population as a function of time via Poisson algorithm but there is not such trend for fluctuation population via Gillespie algorithm. There is a maximum for fluctuation population for all species for kinetics reaction with Michaelis-Menten mechanism as a function of time via Gillespie algorithm. The stochastic approach has also been used for horse liver alcohol dehydrogenase which catalyses the NAD \( ^{+} \) (nicotinamide heterocyclic ring) oxidation of ethanol to acetaldehyde and three kinds of third order reactions. Probability distribution function and fluctuation population for reactants are calculated as a function of time. Increasing a variety of species for third order reactions leads to decrease of coefficient variation.