Abstract
Modeling and simulation of biochemical systems are some of the important research areas in the rapid rise of Systems Biology. Often biochemical kinetic models represent cellular processes as systems of chemical reactions. The dynamics of these systems is typically described by using stochastic models. We introduce a method for an accurate sensitivity analysis of continuous such models of well-stirred biochemical systems. Sensitivity analysis plays a central role in the study of biochemical systems, being an important tool in their model construction, investigation and validation. In particular, it enables the identification of the key reaction rate parameters and it gives insight on how to effectively reduce the system while maintaining its overall behavior. The efficiency and accuracy of the method discussed are tested on several examples of practical interest.
Highlights
In recent years, modeling and simulation of biochemical systems have been widely used to study important biological processes [29]
We introduce a method for an accurate sensitivity analysis of continuous such models of well-stirred biochemical systems
We show that a sensitivity analysis of the deterministic models of biochemical systems is not accurate in estimating the sensitivities of the more general, stochastic models
Summary
In recent years, modeling and simulation of biochemical systems have been widely used to study important biological processes [29]. In a single cell only few regulatory molecules are available, so a continuous model fails to correctly describe the system dynamics and the random fluctuations which are inherent to such a system [26; 48; 51] In this case, stochastic systems are required to accurately capture the system behaviour rather than deterministic models [33; 3; 34; 6; 11; 12; 13; 39; 45]. When cellular processes include large numbers of species and large number of chemical reactions, the exact method developed by Gillespie becomes extremely expensive In addition to their computational cost, stochastic models are complicated and difficult to analyze. Stochastic models have been used in biology to study many important biological processes, such as the cellular dynamics. Due to their intrinsic noise some biochemical systems change their qualitative behaviour compared to when the noise is absent In such cases, a deterministic approach to modeling gives an inaccurate description of the system dynamics [14; 37; 49; 18]. The parameters with large sensitivity are good control points in the system behavior
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