Quantal basis of vesicle growth and information content, a unified approach

Function suggests nano-structure: electrophysiology supports that granule membranes play dice

“…By varying the parameters in a simulation, basic structures and mechanisms involved in such a process can be sequestered. The G&E model to be introduced has been developed to emulate parsimoniously a mechanism for the introduction of vesicles of monomer size which grow by a unit addition mechanism and are later eliminated from the system [17][18][19]22]. A mature granule fuses with a unit granule (to grow into a bigger granule) or with the cell's membrane (to exit the cell) via the formation of a circular rosette [11] composed of SNARE proteins [24 -31].…”

“…Besides intracellular features such as the steady-state distribution of the granule size (G n ), the G&E model determines as well, the distributions of the size of secreted granules under spontaneous and evoked secretion modes. The G&E model is based on the intracellular parameters l, m, b and g that determine for a generic G n granule its growth rate (to G nþ1 ) l ¼ ln b and its elimination (exocytosis and secretion) basal rate m n ¼ mn g from the cell [22]. As will be shown, these parameters (that may be estimated from extracellular evoked and spontaneous secretion statistical data) are related to the number (K b ) of aggregated SNAREs necessary for HF and the number (K g ) of aggregated SNAREs necessary for basal exocytosis.…”

Function suggests nano-structure: towards a unified theory for secretion rate, a statistical mechanics approach

“…As usual with Markov modelling, these two competing scenarios manifest themselves at independent exponentially distributed times, and the earliest to happen, wins. The authors have argued [5,16] …”

“…Secreted granule content should display a distribution that is a mixture of the two, ranging from the exit distribution at the low Ca 2þ concentrations typical of basal secretion, to the stationary distribution, at the opposite end of high Ca 2þ concentration. The exit distribution EXIT depends [5,16] on the two parameters g -b and m/l, and the stationary distribution is given by STAT(n) ¼ C Â EXIT(n)n 2g . As g , 0, the stationary distribution dominates the exit distribution stochastically-smaller granules exit basally and bigger granules exit evokedly [5,16].…”

“…The exit distribution EXIT depends [5,16] on the two parameters g -b and m/l, and the stationary distribution is given by STAT(n) ¼ C Â EXIT(n)n 2g . As g , 0, the stationary distribution dominates the exit distribution stochastically-smaller granules exit basally and bigger granules exit evokedly [5,16].…”