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Stochastic dynamics of chemical reactions in a mutually repressing two-gene circuit is numerically simulated. The circuit has a rich variety of different states when the kinetic change of DNA status is slow. The stochastic switching…

无序系统与神经网络 · 物理学 2009-11-11 Tomohiro Ushikubo , Wataru Inoue , Mitsumasa Yoda , Masaki Sasai

Gene expression has a stochastic component owing to the single molecule nature of the gene and the small number of copies of individual DNA binding proteins in the cell. We show how the statistics of such systems can be mapped on to quantum…

无序系统与神经网络 · 物理学 2009-11-10 Masaki Sasai , Peter G. Wolynes

Biochemical reaction networks are subjected to large fluctuations attributable to small molecule numbers, yet underlie reliable biological functions. Most theoretical approaches describe them as purely deterministic or stochastic dynamical…

分子网络 · 定量生物学 2013-06-11 Jingkui Wang , Marc Lefranc , Quentin Thommen

Multiple phenotypic states often arise in a single cell with different gene-expression states that undergo transcription regulation with positive feedback. Recent experiments have shown that at least in E. coli, the gene state switching can…

分子网络 · 定量生物学 2015-10-28 Hao Ge , Hong Qian , Sunney Xiaoliang Xie

Stochastic oscillations in individual cells are usually characterized by a non-monotonic power spectrum with an oscillatory autocorrelation function. Here we develop an analytical approach of stochastic oscillations in a minimal hybrid…

分子网络 · 定量生物学 2024-02-08 Chen Jia , Hong Qian , Michael Q. Zhang

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic methods (rate equations) and stochastic methods (the master equation and Monte Carlo simulations). These systems exhibit bistability,…

分子网络 · 定量生物学 2007-05-23 Adiel Loinger , Azi Lipshtat , Nathalie Q. Balaban , Ofer Biham

Discrete-state stochastic models are a popular approach to describe the inherent stochasticity of gene expression in single cells. The analysis of such models is hindered by the fact that the underlying discrete state space is extremely…

偏微分方程分析 · 数学 2021-01-28 Pavel Kurasov , Delio Mugnolo , Verena Wolf

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

Auto-regulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic…

亚细胞过程 · 定量生物学 2020-04-22 James Holehouse , Zhixing Cao , Ramon Grima

Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the…

分子网络 · 定量生物学 2017-10-25 Chen Jia , Peng Xie , Min Chen , Michael Q. Zhang

We study by mean-field analysis and stochastic simulations chemical models for genetic toggle switches formed from pairs of genes that mutually repress each other. In order to determine the stability of the genetic switches, we make a…

分子网络 · 定量生物学 2007-05-23 Patrick B. Warren , Pieter Rein ten Wolde

In biology phenotypic switching is a common bet-hedging strategy in the face of uncertain environmental conditions. Existing mathematical models often focus on periodically changing environments to determine the optimal phenotypic response.…

种群与进化 · 定量生物学 2018-03-14 Peter G. Hufton , Yen Ting Lin , Tobias Galla

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play…

分子网络 · 定量生物学 2015-06-22 Niraj Kumar , Thierry Platini , Rahul V. Kulkarni

A general class of stochastic gene expression models with self regulation is considered. One or more genes randomly switch between regulatory states, each having a different mRNA transcription rate. The gene or genes are self regulating…

分子网络 · 定量生物学 2014-12-30 Jay Newby

A detailed stochastic model of single-gene auto-regulation is established and its solutions are explored when mRNA dynamics is fast compared with protein dynamics and in the opposite regime. The model includes all the sources of randomness…

生物物理 · 物理学 2015-06-03 Tomás Aquino , Elsa Abranches , Ana Nunes

We analyze three simple genetic circuits which involve transcriptional regulation and feedback: the autorepressor, the switch and the repressilator, that consist of one, two and three genes, respectively. Such systems are commonly simulated…

分子网络 · 定量生物学 2007-05-23 Ofer Biham , Nathalie Q. Balaban , Adiel Loinger , Azi Lipshtat , Hagai B. Perets

Cells use genetic switches to shift between alternate gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Here, we study the dynamics of switching in a generic-feedback on/off switch. Unlike…

分子网络 · 定量生物学 2015-05-27 Michael Assaf , Elijah Roberts , Zaida Luthey-Schulten

Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copy number of a given gene is heterogeneous both between cells and across time. We present a framework to model gene transcription in populations of cells…

定量方法 · 定量生物学 2017-01-10 Justine Dattani , Mauricio Barahona

The well-known issue of reconstructing regulatory networks from gene expression measurements has been somewhat disrupted by the emergence and rapid development of single-cell data. Indeed, the traditional way of seeing a gene regulatory…

分子网络 · 定量生物学 2021-10-01 Ulysse Herbach

We introduce a stochastic model of coupled genetic oscillators in which chains of chemical events involved in gene regulation and expression are represented as sequences of Poisson processes. We characterize steady states by their…

生物物理 · 物理学 2018-03-28 David J. Jörg , Luis G. Morelli , Frank Jülicher
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