Abstract
The MAPK pathway is one of the main signal transaction system in all eukaryotes which regulates the cellular growth control. Because of its vital role, the regulation of the pathway is conducted via many proteins, thereby constitutes a complex structure. In inference of this system via MCMC techniques based on the Euler approximation, we have observed that there are many proteins which indicate high structural dependencies on other proteins and these species have caused singular diffusion matrices, hereby resulted in infeasible acceptance probabilities. Therefore, we have discarded these problematic substrates at the beginning of the inference and estimated the parameters by using merely linearly independent species in the system. However in that case, the accuracy of the estimation has been highly affected by the underlying exclusion, particularly, when the number of dependent species was big. The elimination of those proteins has led to a significant rise in the number of current missing components in MCMC. In this study, we implicitly include these proteins in our computation via an alternative approach which simulates dependent terms as a linear combination of linearly independent species. In that way, we can add the effect of dependent species in the calculation of acceptance probabilities of reaction rates and states. From the analysis, we conclude that the highlighted innovation decreases the average error of estimates and suggests less computational cost in inference of the MAPK pathway.