We study the  stochastic oscillations in protein number dynamics of  the three-component negative feedback transcription regulatory system called the repressilator . We quantify the degree of fluctuations in oscillation periods and amplitudes, as well as the noise propagation along the regulatory cascade using the exact stochastic simulations with the Gillespie algorithm in the stable oscillation regime  . For the single protein species level, the fluctuation in the oscillation amplitudes is found to be larger than that of  oscillation periods, the distributions of which are reasonably described by the Weibull distribution and the Gaussian tail, respectively. Correlations between successive periods and between successive amplitudes respectively are measured to assess the noise propagation properties, which are found to decay faster for the amplitude than for the period. Local fluctuation property is also studied.
We study the noise characteristics of stochastic oscillations in protein number dynamics of simple genetic oscillatory systems. Using the three-component negative feedback transcription regulatory system called the repressilator as a prototypical example, we quantify the degree of fluctuations in oscillation periods and amplitudes, as well as the noise propagation along the regulatory cascade in the stable oscillation regime via dynamic Monte Carlo simulations . For the single protein-species level, the fluctuation in the oscillation amplitudes is found to be larger than that of the oscillation periods, the distributions of which are reasonably described by the Weibull distribution and the Gaussian tail, respectively. Correlations between successive periods and between successive amplitudes , respectively, are measured to assess the noise propagation properties, which are found to decay faster for the amplitude than for the period. The local fluctuation property is also studied.
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noise characteristics of
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simple genetic oscillatory systems. Using
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as a prototypical example, we
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using the exact stochastic simulations with the Gillespie algorithm
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via dynamic Monte Carlo simulations
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protein species
protein-species
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the
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respectively
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Local
The local