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Gene transcription is a stochastic process that involves thousands ofreactions. 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. Themost common models of transcription are primarily concerned with the effect ofactivators/repressors on the overall transcription rate and approximate thebasal transcription processes as a one step event. According to such effectivemodels, the Fano factor of mRNA copy distributions is always greater than(super-Poissonian) or equal to 1 (Poissonian), and the only way to go belowthis limit (sub-Poissonian) is via a negative feedback. It is partly due tothis limit that the first stage of transcription is held responsible for mostof the stochastic noise in mRNA copy numbers. However, by considering all majorreactions that build and drive the basal transcription machinery, from thefirst protein that binds a promoter to the entrance of the transcriptioncomplex (TC) into productive elongation, it is shown that the first two stagesof transcription, namely the pre-initiation complex (PIC) formation and thepromoter proximal pausing (PPP), is a highly punctual process. In other words,the time between the first and the last step of this process is narrowlydistributed, which gives rise to sub-Poissonian distributions for the number ofTCs that have entered productive elongation. In fact, having simulated the PICformation and the PPP via the Gillespie algorithm using 2000 distinct parametersets 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 for31% of cases the Fano factor is below 0.5, with 0.19 as the lower bound. Theseresults cast doubt on the notion that most of the stochastic noise observed inmRNA distributions always originates at the promoter.

A detailed model of gene promoter dynamics reveals the entry into productive elongation to be a highly punctual process