Quantifying Parameter Interdependence in Stochastic Discrete Models of Biochemical Systems

Stochastic modeling of biochemical processes at the cellular level has been subject intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model such processes. Numerous important systems consist many species to reactions. As result, their mathematical models depend on parameters. In applications, some parameters may be unknown, so values need estimated from experimental data. However, problem parameter value inference can quite challenging, especially setting. To estimate accurately subset parameters, system should sensitive with respect variations each these and they not correlated. this paper, we propose technique for detecting collinearity among models’ apply method selecting subsets that available analysis relies finite-difference sensitivity estimations singular decomposition matrix. We illustrated advantages proposed by successfully testing it several practical interest.