Sensitivity analysis of biochemical reactions aims at quantifying the dependence of the reaction dynamics on the reaction rates. The computation of the parameter sensitivities  poses many computational challenges when taking stochastic noise into account. This paper proposes a new efficient finite difference method for computing parameter sensitivities of biochemical reactions. We employ propensity bounds of reactions to simulate the nominal and perturbed processes and build the estimator . The exactness of the simulation is reserved by applying the rejection-based mechanism. Our approach reduces the variance of the estimator by exploiting the positive correlation of these processes and improves the performance by skipping the propensity updates. Furthermore, by using propensity bounds to couple processes, our approach allows simultaneous perturbation of many reaction rates in computing the sensitivities, which further improves the efficiency of the estimator. We benchmark our method on  reaction models to prove its applicability and efficiency.
Sensitivity analysis of biochemical reactions aims at quantifying the dependence of the reaction dynamics on the reaction rates. The computation of the parameter sensitivities , however, poses many computational challenges when taking stochastic noise into account. This paper proposes a new finite difference method for efficiently computing sensitivities of biochemical reactions. We employ propensity bounds of reactions to couple the simulation of the nominal and perturbed processes . The exactness of the simulation is reserved by applying the rejection-based mechanism. For each simulation step, the nominal and perturbed processes under our coupling strategy are synchronized and often jump together, increasing their positive correlation and hence reducing the variance of the estimator. The distinctive feature of our approach in comparison with existing coupling approaches is that it only needs to maintain a single data structure storing propensity bounds of reactions during the simulation of the nominal and perturbed processes. Our approach allows to computing sensitivities of many reaction rates simultaneously. Moreover, the data structure does not require to be updated frequently, hence improving the computational cost. This feature is especially useful when applied to large reaction networks. We benchmark our method on biological reaction models to prove its applicability and efficiency.
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, however,
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efficient
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computing parameter
efficiently computing
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simulate the
couple the simulation of the
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and build the estimator
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Our approach reduces the variance of the estimator by exploiting the positive correlation of these processes and improves the performance by skipping the propensity updates. Furthermore, by using propensity bounds to couple processes, our approach allows simultaneous perturbation
For each simulation step, the nominal and perturbed processes under our coupling strategy are synchronized and often jump together, increasing their positive correlation and hence reducing the variance of the estimator. The distinctive feature of our approach in comparison with existing coupling approaches is that it only needs to maintain a single data structure storing propensity bounds of reactions during the simulation of the nominal and perturbed processes. Our approach allows to computing sensitivities
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in computing the sensitivities, which further improves the efficiency of the estimator.
simultaneously. Moreover, the data structure does not require to be updated frequently, hence improving the computational cost. This feature is especially useful when applied to large reaction networks.
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biological