Quantitative Analysis of a Transient Dynamics of a Gene Regulatory Network
JaeJun Lee, Julian Lee

TL;DR
This paper quantitatively analyzes how stochastic noise affects gene regulatory networks, showing noise can cause transient extinction of proteins and how baseline production can mitigate this effect, with implications for biological stability.
Contribution
It provides a detailed analysis of noise-induced effects in gene regulation, highlighting the role of baseline production in preventing protein extinction.
Findings
Transient peaks appear near stable fixed points due to noise.
Extinction time diverges exponentially as noise approaches zero.
Baseline production helps protect against protein extinction.
Abstract
In a stochastic process, noise often modifies the picture offered by the mean field dynamics. In particular, when there is an absorbing state, the noise erases a stable fixed point of the mean field equation from the stationary distribution, and turns it into a transient peak. We make a quantitative analysis of this effect for a simple genetic regulatory network with positive feedback, where the proteins become extinct in the presence of stochastic noise, contrary to the prediction of the deterministic rate equation that the protein number converges to a non-zero value. We show that the transient peak appears near the stable fixed point of the rate equation, and the extinction time diverges exponentially as the stochastic noise approaches zero. We also show how the baseline production from the inactive gene ameliorates the effect of the stochastic noise, and interpret the opposite…
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