Accurate parametric sensitivity of stochastic biochemical systems

Computational and Systems Biology are experiencing a rapid development in recent years. Mathematical and computational modelling are critical tools for studying cellular dynamics. Molecular interactions in a cell may display significant random fluctuations when some key species have low amounts (RNA, DNA), making the traditional model of the deterministic reaction rate equations insufficient. Consequently, stochastic models are required to accurately represent the biochemical system behaviour. Nonetheless, stochastic models are more challenging to simulate and analyse than the deterministic ones. Parametric sensitivity is a powerful tool for exploring the system behaviour, such as system robustness with respect to perturbations in its parameters. We present an accurate method for estimating parametric sensitivities for stochastic discrete models of biochemical systems using a high order Coupled Finite Difference scheme and illustrate its advantages compared to the existing techniques