{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T22:02:09Z","timestamp":1791237729997,"version":"4.1.0"},"reference-count":32,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"6","funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["SKIPPER 306321"],"award-info":[{"award-number":["SKIPPER 306321"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Appl. Math."],"published-print":{"date-parts":[[2016,1]]},"abstract":"<jats:p>Protein polymerization is involved in many important biological phenomena and gives rise to spontaneous formation of amyloid fibrils or, more generally, polymers. The starting point of this process, called nucleation, exhibits an important variability among replicated experiments. To analyze the stochastic nature of this phenomenon, one of the simplest models considers two populations of chemical components: free monomers and polymerized monomers. Initially there are only monomers. There are two reactions for the polymerization of a monomer: either two monomers collide to combine into two polymerized monomers or a monomer is polymerized after the encounter of a polymerized monomer. It turns out that this simple model does not explain completely the variability observed in the experiments. This paper investigates extensions of this model to take into account other mechanisms of the polymerization process that may have an impact on fluctuations. The first variant consists in introducing a preliminary conformation step to take into account the biological fact that, before being polymerized, a monomer has two states, regular or misfolded. Only misfolded monomers can be polymerized so that the fluctuations of the number of misfolded monomers can also be a source of variability of the number of polymerized monomers. For the second variant, based on numerical considerations, the reaction rate $\\alpha$ of spontaneous formation of a polymer is of the order of $N^{-\\nu}$ for some large scaling variable $N$ representing the reaction volume and $\\nu$ some positive constant. Asymptotic results involving different time scales are obtained for the corresponding Markov processes. First and second order results for the starting instant of nucleation are derived from these limit theorems. The proofs of the results rely on a study of a stochastic averaging principle for a model related to an Ehrenfest urn model, and also on a scaling analysis of a population model.<\/jats:p>","DOI":"10.1137\/16m1066920","type":"journal-article","created":{"date-parts":[[2016,11,29]],"date-time":"2016-11-29T12:16:13Z","timestamp":1480421773000},"page":"2333-2352","source":"Crossref","is-referenced-by-count":4,"title":["Asymptotics of Stochastic Protein Assembly Models"],"prefix":"10.1137","volume":"76","author":[{"given":"Marie","family":"Doumic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Eug\u00e8ne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Robert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2016,11,29]]},"reference":[{"key":"atypb1","first-page":"3","author":"Anderson D. F.","year":"2011","journal-title":"New York"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"P. Billingsley,\n                      Convergence of Probability measures\n                      , 2nd ed., Wiley Ser. Probab. Stat., Wiley, New York, 1999.","DOI":"10.1002\/9780470316962"},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1017\/S0001867800023752"},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1016\/j.freeradbiomed.2014.08.012"},{"key":"atypb5","doi-asserted-by":"crossref","unstructured":"D. A. Dawson,\n                      Measure-valued Markov processes\n                      , in E\u0301cole d'E\u0301te\u0301 de Probabilite\u0301s de Saint-Flour XXI-1991, Lecture Notes in Math. 1541, Springer, Berlin, 1993, pp. 1-260.","DOI":"10.1007\/BFb0084190"},{"key":"atypb6","unstructured":"C. Dellacherie and P.A. Meyer,\n                      Probabilities and Potential\n                      , North-Holland Math. 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Harris,\n                      The Theory of Branching Processes\n                      , Dover, Mineola, NY, 2002."},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1137\/060666457"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(94)90071-X"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1214\/13-AAP934"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.2307\/3212199"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1038\/nrm3810"},{"key":"atypb19","first-page":"186","author":"Kurtz T. G.","year":"1992","journal-title":"Berlin"},{"key":"atypb20","doi-asserted-by":"publisher","DOI":"10.1100\/tsw.2010.152"},{"key":"atypb21","doi-asserted-by":"crossref","unstructured":"J. J. McManus, P. Charbonneau, E. Zaccarelli, and N. Asherie,\n                      The Physics of Protein Self-Assembly\n                      , preprint, 2016.","DOI":"10.1016\/j.cocis.2016.02.011"},{"key":"atypb22","first-page":"190","volume":"3","author":"Morando P.","year":"1969","journal-title":"Se\u0301m. Probab. Strasbourg"},{"key":"atypb23","doi-asserted-by":"publisher","DOI":"10.1002\/pro.2524"},{"key":"atypb24","unstructured":"G. C. Papanicolaou, D. Stroock, and S. R. S. Varadhan,\n                      Martingale approach to some limit theorems\n                      , in Papers from the Duke Turbulence Conference, Duke Univ. Math. Ser., 3, Duke University, Durham, NC, 1977."},{"key":"atypb25","doi-asserted-by":"publisher","DOI":"10.1016\/j.febslet.2013.03.018"},{"key":"atypb26","doi-asserted-by":"crossref","unstructured":"L. C. G. Rogers and D. Williams,\n                      Diffusions, Markov Processes, and Martingales. Volume\n                      1: Foundations, Cambridge Math. 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