I have written a new paper manuscript on the following topic:

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 concentration x(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.

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