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Updated: May 23, 2026
09:17
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
Published on: March 1, 2022
An automaton approach for waiting times in DNA evolution
Sarah Behrens1, Cyril Nicaud, Pierre Nicodème
1Westfälische Wilhelms-Universität, Institute for Evolution and Biodiversity, Münster, Germany.
Summary
This study enhances DNA evolution models by incorporating overlapping k-mers, improving transcription factor binding site emergence predictions. Highly autocorrelated k-mers show longer waiting times, crucial for understanding regulatory DNA sequence evolution.
Area of Science:
- Computational Biology
- Bioinformatics
- Evolutionary Genetics
Background:
- Previous models for k-mer emergence in DNA evolution assumed non-overlapping occurrences.
- This assumption is limiting for studying regulatory DNA sequences and transcription factor (TF) binding site evolution.
Purpose of the Study:
- To relax the non-overlapping k-mer assumption using an automata approach.
- To improve the accuracy of predicting waiting times for TF binding site emergence.
- To extend waiting time computations to promoters of any size.
Main Methods:
- Utilized an automata-based approach to model k-mer occurrences, allowing for overlaps.
- Performed computations under the Bernoulli (M0) model, with considerations for Markov model of order 1 (M1).
- Analyzed the impact of k-mer autocorrelation on waiting times.
Main Results:
- Confirmed previous findings for low-autocorrelation k-mers.
- Demonstrated that high autocorrelation significantly increases waiting times (up to 40%) by reducing initial probability.
- Identified a significant proportion of autocorrelated k-mers in existing TF binding sites.
- Showed a linear relationship between promoter length and the probability of k-mer occurrence at generation 1.
- Revealed a hyperbolic relationship between promoter length and k-mer waiting time.
Conclusions:
- The automata approach provides more accurate predictions for TF binding site emergence waiting times, especially for autocorrelated k-mers.
- The findings have implications for understanding the evolution of regulatory DNA sequences.
- The study provides a generalized framework for promoter lengths, unlike previous fixed-length models.
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