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Information management in DNA replication modeled by directional, stochastic chains with memory.

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Introducing memory into stochastic chains reveals sequence-dependent assembly mechanisms. This finding is crucial for understanding information transfer in molecular biology, such as DNA replication fidelity.

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Area of Science:

  • Thermodynamics
  • Information Theory
  • Molecular Biology
  • Biophysics

Background:

  • Stochastic chains are fundamental in science, often modeled as memoryless Markov processes.
  • In molecular biology, these chains are vital for gene expression and DNA replication, processes prone to errors.

Purpose of the Study:

  • To investigate how memory in stochastic chains affects their statistical properties.
  • To develop a theoretical framework for analyzing sequence-dependent assembly mechanisms.
  • To apply this framework to enzyme-mediated information transfer in DNA replication.

Main Methods:

  • Introduction of a sequence-dependent partition function.
  • Analysis of chain statistics in the slow dynamics limit.
  • Comparison with standard statistical physics partition functions.
  • Application to enzyme-mediated DNA replication.

Main Results:

  • Memory in stochastic chains leads to assembly-mechanism-dependent statistics, even without friction.
  • The developed theory accurately reproduces DNA replication error rates.
  • The model explains the significant increase in fidelity due to proofreading and editing mechanisms.
  • A prediction of 1 kT energy cost per order of magnitude increase in fidelity was made.

Conclusions:

  • Sequence-dependent assembly mechanisms are critical for understanding molecular information transfer.
  • The developed theoretical framework provides insights into non-equilibrium processes in molecular systems.
  • The findings have implications for biophysics, materials science, and engineering.