Biochemical reaction networks in living cells usually involve reversible covalent modification of signaling molecules, such as protein phosphorylation. Under the frequent conditions of small molecule numbers,  mass action theory becomes insufficient to describe the dynamics of such systems. Instead, the biochemical reactions must be treated as stochastic processes , producing intrinsic concentration fluctuations of the chemicals. We investigate the stochastic reaction kinetics of covalent modification cycles (CMCs) by analytical modelling and numerically exact Monte-Carlo simulation of the temporaly fluctuating concentrationx(t). The statistical behaviour of this simple network module turns out to be so rich that CMCs can be viewed as versatile and tunable noise generators. Depending on the parameter regime, we find for the probability density P(x) several qualitatively different classes of distribution functions, including powerlaw distributions with a fractional and tunable exponent. These findings challenge the traditional view of biochemical control networks as deterministic computational systems  .
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 modelling 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 powerlaw 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 .
D
the frequent
A
as is frequently the case in living cells,
R
becomes insufficient
fails
R
, producing intrinsic
that intrinsically generate
R
temporaly fluctuating concentrationx(t). The statistical behaviour of this simple network module turns out to be so rich that CMCs can be viewed as versatile and tunable noise generators.
temporally fluctuating concentration.
R
P(x) several qualitatively different
of the concentration qualitatively distinct
A
and suggest that CMCs in cells can function as versatile and tunable noise generators