  We consider the problem of estimating parameter sensitivity for Markovian
models of reaction networks. Sensitivity values measure the responsiveness of
an output to the model parameters. They help in analyzing the network,
understanding its robustness properties and identifying the important reactions
for a specific output. Sensitivity values are commonly estimated using methods
that perform finite-difference computations along with Monte Carlo simulations
of the reaction dynamics. These methods are computationally efficient and easy
to implement, but they produce a biased estimate which can be unreliable for
certain applications. Moreover the size of the bias is generally unknown and
hence the accuracy of these methods cannot be easily determined. There also
exist unbiased schemes for sensitivity estimation but these schemes can be
computationally infeasible, even for simple networks. Our goal in this paper is
to present a new method for sensitivity estimation, which combines the
computational efficiency of finite-difference methods with the accuracy of
unbiased schemes. Our method is easy to implement and it relies on an exact
representation of parameter sensitivity that we recently proved in an earlier
paper. Through examples we demonstrate that the proposed method can outperform
the existing methods, both biased and unbiased, in many situations.
