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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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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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Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.

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Cellular protein levels are stabilized by state-dependent delays in activation, reducing fluctuations below baseline. Negative feedback further enhances stability and predictability in gene expression.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Biophysics

Background:

  • Cells require stable protein levels for proper function, but gene expression is inherently stochastic.
  • Excessive protein fluctuations can impair cellular decision-making and disrupt biological processes.
  • Existing regulatory mechanisms like negative feedback buffer these fluctuations, but the role of state-dependent delays is unclear.

Purpose of the Study:

  • To investigate how state-dependent delays in protein activation affect expression variability.
  • To develop a stochastic model quantifying the impact of delay structure and feedback on protein fluctuations.
  • To understand the principles of temporal and state-dependent regulation in stabilizing protein expression.

Main Methods:

  • Development of a stochastic model for protein production with bursty, inactive molecule generation.
  • Inclusion of state-dependent activation delays influenced by active protein levels, forming a feedback loop.
  • Analytical derivation of expressions for protein variability (Fano factor) and confirmation via stochastic simulations.

Main Results:

  • State-dependent delays can significantly reduce protein level fluctuations below baseline levels.
  • Negative feedback integrated into burst production further decreases variability and enhances system predictability.
  • The model provides explicit relationships between delay structure, feedback strength, and protein variability.

Conclusions:

  • Temporal and state-dependent regulation are crucial for stabilizing protein expression in cells.
  • Understanding these mechanisms offers insights into natural cellular control systems.
  • This work guides the design of robust synthetic gene circuits with predictable behavior.