  Biochemical reaction networks in living cells usually involve reversible
covalent modification of signaling molecules, such as protein phosphorylation.
Under conditions of small molecule numbers, as is frequently the case in living
cells, mass action theory fails to describe the dynamics of such systems.
Instead, the biochemical reactions must be treated as stochastic processes that
intrinsically generate concentration fluctuations of the chemicals. We
investigate the stochastic reaction kinetics of covalent modification cycles
(CMCs) by analytical modeling and numerically exact Monte-Carlo simulation of
the temporally fluctuating concentration. Depending on the parameter regime, we
find for the probability density of the concentration qualitatively distinct
classes of distribution functions, including power law distributions with a
fractional and tunable exponent. These findings challenge the traditional view
of biochemical control networks as deterministic computational systems and
suggest that CMCs in cells can function as versatile and tunable noise
generators.
