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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Multimodality and flexibility of stochastic gene expression.

Guilherme da Costa Pereira Innocentini1, Michael Forger, Alexandre Ferreira Ramos

  • 1Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão, 1010, Cidade Universitária, São Paulo, SP, Brazil, CEP: 05508-090, ginnocentini@gmail.com.

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Multistate promoters can generate multimodal gene product distributions and reduce expression noise without feedback. A three-state promoter model shows intermediate states minimize fluctuations during expression level changes.

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Area of Science:

  • * Mathematical modeling of biological systems.
  • * Biophysics and stochastic processes.
  • * Gene regulation and expression dynamics.

Background:

  • * Stochastic gene expression is influenced by promoter states.
  • * Traditional models often lack multi-state promoter dynamics.
  • * Understanding noise reduction in gene expression is crucial.

Purpose of the Study:

  • * To develop a general mathematical model for stochastic gene expression with multi-state promoters.
  • * To analyze the stationary limit of master equations for these models.
  • * To investigate the generation of multimodal distributions and noise reduction.

Main Methods:

  • * Solving master equations in the stationary limit.
  • * Factorizing the stochastic transition matrix to separate timescales.
  • * Expressing model entries using interpretable physical and biological parameters.

Main Results:

  • * Multi-state promoters can generate multimodal gene product distributions without feedback.
  • * A three-state promoter model significantly reduces noise in gene expression.
  • * Transitioning through intermediate states minimizes fluctuation increases.

Conclusions:

  • * Multi-state promoter models provide a framework for understanding complex gene expression patterns.
  • * These models offer insights into noise reduction mechanisms in biological systems.
  • * The findings highlight the regulatory potential of promoter state dynamics.