On the Asymptotic Distribution of Nucleation Times of Polymerization Processes

TL;DR

This paper analyzes the asymptotic distribution of nucleation times in a stochastic polymerization model, revealing significant variability and providing mathematical insights into the timing of polymer formation.

Contribution

It introduces a novel stochastic model with nucleation properties and derives the asymptotic distribution of nucleation times, highlighting variability observed in biological experiments.

Findings

Distribution of nucleation times converges under scaling.

First polymerization time has significant variability.

Results applicable to biological polymerization processes.

Abstract

In this paper, we investigate a stochastic model describing the time evolution of a polymerization process. A polymer is a macro-molecule resulting from the aggregation of several elementary sub-units called monomers. Polymers can grow by addition of monomers or can be split into several polymers. The initial state of the system consists mainly of monomers. We study the time evolution of the mass of polymers, in particular the asymptotic distribution of the first instant when the fraction of monomers used in polymers is above some positive threshold $\delta$. A scaling approach is used by taking the mass $N$ as a scaling parameter. The mathematical model used in this paper includes {\em a nucleation property}: If $n_c$ is defined as the size of the nucleus, polymers with a size less than $n_c$ are quickly fragmented into smaller polymers, at a rate proportional to $\Phi(N)$ for some non-decreasing and unbounded function $\Phi$. For polymers of size greater than $n_c$, fragmentation still occurs but at bounded rates. If $T^N$ is the instant of creation of the first polymer whose size is $n_c$, it is shown that, under appropriate conditions, the variable $T^N{/}[\Phi(N)^{n_c{-}2}{/}N]$ converges in distribution, and that the first instant $L_\delta^N$ when a fraction $\delta$ of monomers is polymerized has the same order of magnitude. An original feature proved for this model is the significant variability of the variable $T^N$. This is a well known phenomenon observed in biological experiments but few mathematical models of the literature have this property. The results are proved via a series of technical estimates for occupation measures of some functionals of the corresponding Markov processes on fast time scales and by using coupling techniques.