  Noise plays a crucial role in the regulation of cellular and organismal
function and behavior.
  Exploring noise's impact is key to understanding fundamental biological
processes, such as gene expression, signal transduction, and the mechanisms of
development and evolution.
  Currently, a comprehensive method to quantify dynamical behavior of cellular
noise within these biochemical systems is lacking.
  In this study, we introduce an optically-controlled perturbation system
utilizing the light-sensitive Phytochrome B (PhyB) from \textit{Arabidopsis
thaliana}, which enables precise noise modulation with high spatial-temporal
resolution.
  Our system exhibits exceptional sensitivity to light, reacting consistently
to pulsed light signals, distinguishing it from other photoreceptor-based
promoter systems that respond to a single light wavelength.
  To characterize our system, we developed a stochastic model for phytochromes
that accounts for photoactivation/deactivation, thermal reversion, and the
dynamics of the light-activated gene promoter system.
  To precisely control our system, we determined the rate constants for this
model using an omniscient deep neural network that can directly map rate
constant combinations to time-dependent state joint distributions.
  By adjusting the activation rates through light intensity and degradation
rates via N-terminal mutagenesis, we illustrate that out optical-controlled
perturbation can effectively modulate molecular expression level as well as
noise.
  Our results highlight the potential of employing an optically-controlled gene
perturbation system as a noise-controlled stimulus source.
  This approach, when combined with the analytical capabilities of a
sophisticated deep neural network, enables the accurate estimation of rate
constants from observational data in a broad range of biochemical reaction
networks.
