Parameter Estimation in Biochemical Models Using Marginal Probabilities

Estimation of model parameters from experimental or synthetic data is an essential technique for working with stochastic models and is of increasing interest. We formulate the objective function through a fitting scheme based on a maximum likelihood estimator (MLE) that uses the marginal distribution of the species involved, which is a new way not attempted before. The quality of the method is evaluated for some example models, such as the Michaelis-Menten enzyme kinetics and mono-molecular reaction chain. Our numerical tests are performed with both local and global optimization schemes. It is shown that the method performs well compared to existing approaches.