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Gene regulatory networks are comprised of biochemical reactions, which areinherently stochastic. Each reaction channel contributes to this stochasticityin different measure. In this paper we study the stochastic dynamics of proteininteraction networks (PIN) that are made up of monomers and dimers. The networkis defined by the dimers, which are formed by hybridizing two monomers. Thesize of a PIN was defined as the number of monomers that interacts with atleast one other monomer (including itself). We generated 4200 random PIN ofsizes between 2 and 8 (600 per size) and simulated via the Gillespie algorithmthe stochastic evolution of copy numbers of all monomers and dimers until theyreached a steady state. The simulations revealed that the Fano factors of bothmonomers and dimers in all networks and for all time points were close to one,either from below or above. Only 10% of Fano factors for monomers were above1.3 and 10% of Fano factors for dimers were above 1.17, with 5.54 and 3.47 asthe maximum value recorded for monomers and dimers, respectively. Thesefindings suggests that PIN in real biological setting contribute to the overallstochastic noise that is close to Poisson. Our results also show a correlationbetween stochastic noise, network size and network connectivity: for monomers,the Fano factors tend towards 1 from above, while the Fano factors for dimerstend towards 1 from below. For monomers, this tendency is amplified withincreased network connectivity.

Stochastic fluctuations in protein interaction networks are nearly Poissonian