One difficulty in building a mechanistic model of biological systems lies in determining correct parameter values. This paper proposes a novel parameter estimation method to infer unknown parameters, such as kinetic rates, from noisy experimental observations. Derived from the approximate Bayesian computation sequential Monte Carlo algorithm, our method predicts the distribution of each parameter rather than a single value via several intermediate distributions. Motivated by the computational intensity of the method, we improve the approximate Bayesian computation sequential Monte Carlo method in two aspects. First, to increase the efficiency, a windowing method is developed to reduce the parameter‐searching space, and an adaptive sampling weight mechanism is introduced to make the intermediate distributions converge to the target distributions in a much quicker manner. Second, to speed up the estimation process, we implement our method in a parallel computing environment to speed up the sampling process. Copyright © 2013 John Wiley & Sons, Ltd.