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This paper theoretically analyzes a phenomenological stochastic model for bacterial growth. This model comprises cell division and the linear growth of cells, where growth rates and cell cycles are drawn from lognormal distributions. We…

生物物理 · 物理学 2016-06-28 Ken Yamamoto , Jun-ichi Wakita

Many of the existing stochastic models of gene expression contain the first-order decay reaction term that may describe active protein degradation or dilution. If the model variable is interpreted as the molecule number, and not…

生物物理 · 物理学 2019-05-22 Jakub Jędrak , Maciej Kwiatkowski , Anna Ochab-Marcinek

Gene expression is significantly stochastic making modeling of genetic networks challenging. We present an approximation that allows the calculation of not only the mean and variance but also the distribution of protein numbers. We assume…

分子网络 · 定量生物学 2008-12-18 Vahid Shahrezaei , Peter S. Swain

The burst approximation is a widely used technique to simplify stochastic gene expression models. However, the dynamics and analytical properties of the protein number distribution in gene expression models under the burst approximation are…

生物物理 · 物理学 2026-05-06 Yuntao Lu , Yunxin Zhang

Protein variability in single cells has been studied extensively in populations, but little is known about temporal protein fluctuations in a single cell over extended times. We present here traces of protein copy number measured in…

生物物理 · 物理学 2015-06-16 Naama Brenner , Erez Braun , Anna Yoney , Lee Susman , James Rotella , Hanna Salman

In this paper we analyze the equilibrium properties of a large class of stochastic processes describing the fundamental biological process within bacterial cells, {\em the production process of proteins}. Stochastic models classically used…

分子网络 · 定量生物学 2019-10-17 Philippe Robert

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of…

Cellular phenotype is characterized by different components such as cell size, protein content and cell cycle time. These are global variables that are the outcome of multiple internal microscopic processes. Accordingly, they display some…

细胞行为 · 定量生物学 2018-05-16 Lee Susman , Maryam Kohram , Harsh Vashistha , Jeffrey T. Nechleba , Hanna Salman , Naama Brenner

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations, hence there is considerable interest…

分子网络 · 定量生物学 2015-06-15 Hodjat Pendar , Thierry Platini , Rahul V. Kulkarni

We study a stochastic model of gene expression, in which protein production has a form of random bursts whose size distribution is arbitrary, whereas protein decay is a first-order reaction. We find exact analytical expressions for the time…

生物物理 · 物理学 2016-09-21 Jakub Jędrak , Anna Ochab-Marcinek

Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Bursting in gene expression is known to impact cell-fate in diverse systems ranging from latency in HIV-1…

分子网络 · 定量生物学 2016-02-17 Niraj Kumar , Abhyudai Singh , Rahul V. Kulkarni

Protein domains are found on genomes with notable statistical distributions, which bear a high degree of similarity. Previous work has shown how these distributions can be accounted for by simple models, where the main ingredients are…

基因组学 · 定量生物学 2008-07-14 M. Cosentino Lagomarsino , A. L. Sellerio , P. D. Heijning , B. Bassetti

This paper considers the behavior of discrete and continuous mathematical models for gene expression in the presence of transcriptional/translational bursting. We treat this problem in generality with respect to the distribution of the…

概率论 · 数学 2015-10-15 M. C. Mackey , M. Tyran-Kamińska , R. Yvinec

A key goal of systems biology is the predictive mathematical description of gene regulatory circuits. Different approaches are used such as deterministic and stochastic models, models that describe cell growth and division explicitly or…

分子网络 · 定量生物学 2012-10-12 Rahul Marathe , Veronika Bierbaum , David Gomez , Stefan Klumpp

We introduce two stochastic chemostat models consisting in a coupled population-nutrient process reflecting the interaction between the nutrient and the bacterias in the chemostat with finite volume. The nutrient concentration evolves…

概率论 · 数学 2012-06-19 Pierre Collet , Servet Martinez , Sylvie Meleard , Jaime San Martin

Various rod-shaped bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive Bacillus subtilis divide symmetrically after they approximately double their volume. Their size at division is not constant,…

细胞行为 · 定量生物学 2015-06-18 Ariel Amir

The distributions of many proteins in rod-shaped bacteria are far from homogenous. Often they accumulate at the cell poles or in the cell center. At the same time, the copy number of proteins in a single cell is relatively small making the…

亚细胞过程 · 定量生物学 2014-05-08 L. Wettmann , M. Bonny , K. Kruse

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and…

亚细胞过程 · 定量生物学 2024-05-28 Florian Pflug , Deepak Bhat , Simone Pigolotti

Unfolded protein aggregation in cellular system is a problem causing various types of diseases depending on which type unfolded proteins aggregate. This phenomenon of aggregation may take place during production, storage, shipment or…

亚细胞过程 · 定量生物学 2021-11-09 Utkarsh Upadhyay , Chandrima Barua , Shivani Devi , Jay Prakash Kumar , R. K. Brojen Singh

We model the growth of a cell population using a piecewise deterministic Markov branching tree. In this model, each cell splits into two offspring at a division rate $B(x)$, which depends on its size $x$. The size of each cell increases…

概率论 · 数学 2024-09-06 Nathalie Krell
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