Extension of Leap Condition in Approximate Stochastic Simulation Algorithms of Biological Networks

In the biological systems, Monte Carlo approaches are used to provide stochastic simulation of chemical reactions. The major algorithms (SSAs) direct method, also known as Gillespie algorithm, first reaction method and next method. While these methods give accurate generation results, they computationally demanding for large complex systems. To increase computational efficiency SSAs, approximate SSAs can be option. rely on leap condition. This condition means that propensity function during time interval $ t $[ t+\tau ]$ should not altered chosen step $\tau$. Here, proceed with system's history axis from one next, we compute how many times each realized in small $\tau$ so observe plausible simultaneous Hence, this study aims generate a realistic close confidence parameter which denotes underlying numbers reactions system by satifying For purpose, poisson $\tau$-leap algorithm extension handled. estimation associated parameters both algorithms, derive their maximum likelihood estimators, moment estimatora bayesian estimators. From derivations, theoretically show our novel intervals narrower than current under