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Open AccessJournal ArticleDOI

Stochastic mechanisms in gene expression

Harley H. McAdams, +1 more
- 04 Feb 1997 - 
- Vol. 94, Iss: 3, pp 814-819
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TLDR
This work has analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links.
Abstract
In cellular regulatory networks, genetic activity is controlled by molecular signals that determine when and how often a given gene is transcribed. In genetically controlled pathways, the protein product encoded by one gene often regulates expression of other genes. The time delay, after activation of the first promoter, to reach an effective level to control the next promoter depends on the rate of protein accumulation. We have analyzed the chemical reactions controlling transcript initiation and translation termination in a single such “genetically coupled” link as a precursor to modeling networks constructed from many such links. Simulation of the processes of gene expression shows that proteins are produced from an activated promoter in short bursts of variable numbers of proteins that occur at random time intervals. As a result, there can be large differences in the time between successive events in regulatory cascades across a cell population. In addition, the random pattern of expression of competitive effectors can produce probabilistic outcomes in switching mechanisms that select between alternative regulatory paths. The result can be a partitioning of the cell population into different phenotypes as the cells follow different paths. There are numerous unexplained examples of phenotypic variations in isogenic populations of both prokaryotic and eukaryotic cells that may be the result of these stochastic gene expression mechanisms.

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Citations
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Stochastic Gene Expression in a Single Cell

TL;DR: This work constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated and reveals how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
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Core transcriptional regulatory circuitry in human embryonic stem cells.

TL;DR: Insight is provided into the transcriptional regulation of stem cells and how OCT4, SOX2, and NANOG contribute to pluripotency and self-renewal and how they collaborate to form regulatory circuitry consisting of autoregulatory and feedforward loops.
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Construction of a genetic toggle switch in Escherichia coli

TL;DR: The construction of a genetic toggle switch is presented—a synthetic, bistable gene-regulatory network—in Escherichia coli and a simple theory is provided that predicts the conditions necessary for bistability.
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Transcriptional Regulatory Networks in Saccharomyces cerevisiae

TL;DR: This work determines how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells, and identifies network motifs, the simplest units of network architecture, and demonstrates that an automated process can use motifs to assemble a transcriptional regulatory network structure.
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Modeling and simulation of genetic regulatory systems: a literature review.

TL;DR: This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equation, stochastic equations, and so on.
References
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Journal ArticleDOI

Exact Stochastic Simulation of Coupled Chemical Reactions

TL;DR: In this article, a simulation algorithm for the stochastic formulation of chemical kinetics is proposed, which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of a given chemical system.
Book

Stochastic processes in physics and chemistry

TL;DR: In this article, the authors introduce the Fokker-planck equation, the Langevin approach, and the diffusion type of the master equation, as well as the statistics of jump events.

Stochastic Processes in Physics and Chemistry

Abstract: Preface to the first edition. Preface to the second edition. Abbreviated references. I. Stochastic variables. II. Random events. III. Stochastic processes. IV. Markov processes. V. The master equation. VI. One-step processes. VII. Chemical reactions. VIII. The Fokker-Planck equation. IX. The Langevin approach. X. The expansion of the master equation. XI. The diffusion type. XII. First-passage problems. XIII. Unstable systems. XIV. Fluctuations in continuous systems. XV. The statistics of jump events. XVI. Stochastic differential equations. XVII. Stochastic behavior of quantum systems.
Book

Fundamentals of Enzyme Kinetics

TL;DR: Basic Principles of Chemical Kinetics Introduction to Enzyme Kinetics "Alternative" Enzymes Practical Aspects of Kinetics Deriving Steady-state Rate Equations Reversible Inhibition and Activation Tight-binding and Irreversible Inhibitors
Journal ArticleDOI

The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation.

TL;DR: A quantitative model for processes in the bacteriophage lambda that control the switchover from lysogenic to lytic modes of growth was found capable of predicting essential physiological characteristics of the system over an extended time.
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