  In this paper, we investigate how stochastic reaction processes are affected
by external perturbations. We describe an extension of the deterministic
metabolic control analysis (MCA) to the stochastic regime. We introduce
stochastic sensitivities for mean and covariance values of reactant
concentrations and reaction fluxes and show that there exist MCA-like summation
theorems among these sensitivities. The summation theorems for flux variances
are shown to depend on the size of the measurement time window ($\epsilon$),
within which reaction events are counted for measuring a single flux. The
degree of the $\epsilon$-dependency can become significant for processes
involving multi-time-scale dynamics and is estimated by introducing a new
measure of time scale separation. This $\epsilon$-dependency is shown to be
closely related to the power-law scaling observed in flux fluctuations in
various complex networks. We propose a systematic way to control fluctuations
of reactant concentrations while minimizing changes in mean concentration
levels. Such orthogonal control is obtained by introducing a control vector
indicating the strength and direction of parameter perturbations leading to a
sensitive control. We also propose a possible implication in the control of
flux fluctuation: The control distribution for flux fluctuations changes with
the measurement time window size, $\epsilon$. When a control engineer applies a
specific control operation on a reaction system, the system can respond
contrary to what is expected, depending on the time window size $\epsilon$.
