  Gene transcription is a stochastic process that involves thousands of
reactions. The first set of these reactions, which happen near a gene promoter,
are considered to be the most important in the context of stochastic noise. The
most common models of transcription are primarily concerned with the effect of
activators/repressors on the overall transcription rate and approximate the
basal transcription processes as a one step event. According to such effective
models, the Fano factor of mRNA copy distributions is always greater than
(super-Poissonian) or equal to 1 (Poissonian), and the only way to go below
this limit (sub-Poissonian) is via a negative feedback. It is partly due to
this limit that the first stage of transcription is held responsible for most
of the stochastic noise in mRNA copy numbers. However, by considering all major
reactions that build and drive the basal transcription machinery, from the
first protein that binds a promoter to the entrance of the transcription
complex (TC) into productive elongation, it is shown that the first two stages
of transcription, namely the pre-initiation complex (PIC) formation and the
promoter proximal pausing (PPP), is a highly punctual process. In other words,
the time between the first and the last step of this process is narrowly
distributed, which gives rise to sub-Poissonian distributions for the number of
TCs that have entered productive elongation. In fact, having simulated the PIC
formation and the PPP via the Gillespie algorithm using 2000 distinct parameter
sets and 4 different reaction network topologies, it is shown that only 4.4%
give rise to a Fano factor that is > 1 with the upper bound of 1.7, while for
31% of cases the Fano factor is below 0.5, with 0.19 as the lower bound. These
results cast doubt on the notion that most of the stochastic noise observed in
mRNA distributions always originates at the promoter.
