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Autori principali: Azencott, Robert, Geiger, Brett, Timofeyev, Ilya
Natura: Preprint
Pubblicazione: 2018
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Accesso online:https://arxiv.org/abs/1811.10176
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author Azencott, Robert
Geiger, Brett
Timofeyev, Ilya
author_facet Azencott, Robert
Geiger, Brett
Timofeyev, Ilya
contents Radical shifts in the genetic composition of large cell populations are rare events with quite low probabilities, which direct numerical simulations generally fail to evaluate accurately. In this paper, we develop a theoretical large deviations framework for a class of Markov chains modeling the genetic evolution of bacteria such as E. coli. In particular, we develop the cost function for discrete-time Markov chains which describe the daily evolution of histograms of bacterial populations. We also develop explicit formulas that can be used to numerically quantify the most likely evolutionary trajectories connecting an initial histogram and the target histogram.
format Preprint
id arxiv_https___arxiv_org_abs_1811_10176
institution arXiv
publishDate 2018
record_format arxiv
spellingShingle Large Deviations Analysis for Stochastic Models of Bacterial Evolution
Azencott, Robert
Geiger, Brett
Timofeyev, Ilya
Probability
60F10, 60J20, 92D15
Radical shifts in the genetic composition of large cell populations are rare events with quite low probabilities, which direct numerical simulations generally fail to evaluate accurately. In this paper, we develop a theoretical large deviations framework for a class of Markov chains modeling the genetic evolution of bacteria such as E. coli. In particular, we develop the cost function for discrete-time Markov chains which describe the daily evolution of histograms of bacterial populations. We also develop explicit formulas that can be used to numerically quantify the most likely evolutionary trajectories connecting an initial histogram and the target histogram.
title Large Deviations Analysis for Stochastic Models of Bacterial Evolution
topic Probability
60F10, 60J20, 92D15
url https://arxiv.org/abs/1811.10176