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相关论文: Stochastic Kinetics of mRNA Molecules in a General…

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In this manuscript we propose a mathematical framework to couple transcription and translation in which mRNA production is described by a set of master equations while the dynamics of protein density is governed by a random differential…

亚细胞过程 · 定量生物学 2020-07-30 Guilherme C. P. Innocentini , Michael Forger , Ovidiu Radulescu , Fernando Antoneli

Transcriptional pulsing has been observed in both prokaryotes and eukaryotes and plays a crucial role in cell to cell variability of protein and mRNA numbers. The issue is how the time constants associated with episodes of transcriptional…

定量方法 · 定量生物学 2009-09-29 Srividya Iyer-Biswas , F. Hayot , C. Jayaprakash

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

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to…

定量方法 · 定量生物学 2018-12-10 Yen Ting Lin , Nicolas E. Buchler

In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of…

生物大分子 · 定量生物学 2009-11-11 T. Ochiai , J. C. Nacher , T. Akutsu

Based on the theory of stochastic chemical kinetics, the inherent randomness and stochasticity of biochemical reaction networks can be accurately described by discrete-state continuous-time Markov chains. The analysis of such processes is,…

数值分析 · 数学 2014-10-14 Andreychenko Alexander , Mikeev Linar , Wolf Verena

Under certain cellular conditions, transcription and mRNA translation in prokaryotes appear to be "coupled," in which the formation of mRNA transcript and production of its associated protein are temporally correlated. Such…

亚细胞过程 · 定量生物学 2026-05-12 Xiangting Li , Tom Chou

In this paper we study an important global regulation mechanism of transcription of biological cells using specific macro-molecules, 6S RNAs. The functional property of 6S RNAs is of blocking the transcription of RNAs when the environment…

概率论 · 数学 2022-08-22 Vincent Fromion , Philippe Robert , Jana Zaherddine

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 processes, resulting in the transcription of RNA, are intrinsically noisy. It was observed experimentally that the synthesis of mRNA molecules is driven by short, burst-like, events. An accurate prediction of the protein level often…

种群与进化 · 定量生物学 2009-10-14 Vlad Elgart

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

The bulk of stochastic gene expression models in the literature do not have an explicit description of the age of a cell within a generation and hence they cannot capture events such as cell division and DNA replication. Instead, many…

亚细胞过程 · 定量生物学 2020-03-11 Casper H. L. Beentjes , Ruben Perez-Carrasco , Ramon Grima

Within systems biology there is an increasing interest in the stochastic behavior of genetic and biochemical reaction networks. An appropriate stochastic description is provided by the chemical master equation, which represents a continuous…

生物物理 · 物理学 2011-06-23 E. Giampieri , D. Remondini , L. de Oliveira , G. Castellani , P. Lió

Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This…

分子网络 · 定量生物学 2018-08-07 Yuta Sakurai , Yutaka Hori

Stochastic modeling of gene expression is a classic problem in theoretical biophysics, and the burst approximation is widely used to simplify gene expression models formulated via the chemical master equation. However, the approximation…

生物物理 · 物理学 2026-03-31 Yuntao Lu , Yunxin Zhang

The rate of mRNA translation depends on the initiation, elongation, and termination rates of ribosomes along the mRNA. These rates depend on many "local" factors like the abundance of free ribosomes and tRNA molecules in the vicinity of the…

亚细胞过程 · 定量生物学 2021-02-25 Michael Margaliot , Wasim Huleihel , Tamir Tuller

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

We present a theoretical framework to analyze the dynamics of gene expression with stochastic bursts. Beginning with an individual-based model which fully accounts for the messenger RNA (mRNA) and protein populations, we propose a novel…

分子网络 · 定量生物学 2016-03-23 Yen Ting Lin , Charles R. Doering

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

Recent experiments at the level of a single cell have shown that gene expression occurs in abrupt stochastic bursts. Further, in an ensemble of cells, the levels of proteins produced have a bimodal distribution. In a large fraction of…

软凝聚态物质 · 物理学 2009-11-07 Siddhartha Roy , Indrani Bose , Subhrangshu Sekhar Manna
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