Inference of the stochastic MAPK pathway by modified diffusion bridge method

The MAPK pathway is one of the well-known systems in oncogene researches of eukaryotes due to its important role in cell life. In this study, we perform the parameter estimation of a realistic MAPK system by using western blotting data. In inference, we use the modified diffusion bridge algorithm with data augmentation technique by modelling the realistically complex system via the Euler–Maruyama approximation. This approximation, which is the discretized version of the diffusion model, can be seen as an alternative OR approach with respect to the (hidden) Markov chain method in stochastic modelling of the biochemical systems where the data can be fully or partially observed and the time-course measurements are though to be collected at small time steps. Hereby, the modified diffusion bridge technique, which is based on the Markov Chain Monte Carlo (MCMC) methods, enables us to accurately estimate the model parameters, presented as the stochastic reaction rate constants, of the diffusion model under high dimensional systems despite loss in computational demand. In the estimation of the parameters, due to the complexity in the decision-making problems of the MCMC updates at different stages, we face with the dependency challenges. We unravel them by checking the singularity of the system in every stage of updates. In modelling, we also assume with/without-measurement error approaches in all states. But in order to evaluate the performance of both models, we initially implement them in a toy system. From the results, we observe that the model with measurement error performs better than the model without measurement error in terms of the mixing features of the MCMC runs and the accuracy of estimates, thereby, it is used for the parameter estimation of the realistic MAPK pathway. From the outcomes, we consider that the suggested approach can be seen as a promising alternative method in inference of parameters via different OR techniques in system biology.