Abstract
We study a biological autoregulation process, involving a protein that enhances its own transcription, in a parameter region where bistability would be present in the absence of fluctuations. We calculate the rate of fluctuation-induced rare transitions between locally-stable states using a path integral formulation and Master and Chapman-Kolmogorov equations. As in simpler models for rare transitions, the rate has the form of the exponential of a quantity $S_0$ (a "barrier") multiplied by a prefactor $\eta$. We calculate $S_0$ and $\eta$ first in the bursting limit (where the ratio $\gamma$ of the protein and mRNA lifetimes is very large). In this limit, the calculation can be done almost entirely analytically, and the results are in good agreement with simulations. For finite $\gamma$ numerical calculations are generally required. However, $S_0$ can be calculated analytically to first order in $1/\gamma$, and the result agrees well with the full numerical calculation for all $\gamma > 1$. Employing a method used previously on other problems, we find we can account qualitatively for the way the prefactor $\eta$ varies with $\gamma$, but its value is 15-20% higher than that inferred from simulations.
Highlights
In this paper we study such transitions in our simple model, with the aim of understanding how different features of the biochemical circuitry affect the rate at which these events occur
It is possible to decompose the result in a natural way into protein and mRNA contributions, and we find that the former of these is quite insensitive to γ, while the latter falls off toward zero with increasing γ
We examined the γ-dependence of the two terms in the extremal action (78), which we call the mRNA and protein actions, respectively
Summary
Fluctuations are intrinsic to biology because many biochemical processes involve small numbers of molecules [1–3]. Several groups have solved the time-independent Chapman-Kolmogorov equations for this and related systems and found the stationary protein number distributions Using both an extension of their methods and the 1-d limit of our path integral, we are able to calculate the switching rate analytically, and simulations confirm the theoretical predictions. We take a closer look at the bursting limit, showing how the mRNA concentration can be eliminated from the Hamilton equations and how the activation barrier can be evaluated analytically This result makes contact with the calculations mentioned above of the stationary distribution, and by extending the methods used by those authors we are able to evaluate the prefactor in the switching rate analytically. This means that when mutiply b by some factor, the transcription rate parameters a and g0 in (3) are divided by the same factor, i.e., we keep bg(y) invariant
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