Coupled Intermittent Maps Modelling the Statistics of Genomic Sequences: A Network Approach

TL;DR

This paper introduces a network-based model using coupled intermittent maps to replicate the long-range correlations observed in genomic sequences, providing a new tool for analyzing complex biological data.

Contribution

It presents a novel network approach employing coupled intermittent polynomial maps to model and reproduce statistical features of genomic sequences.

Findings

Degree and link size distributions follow power laws similar to real genomes

The method effectively captures long-range correlations in symbolic sequences

Applicable to both artificial and natural sequences

Abstract

The dynamics of coupled intermittent maps is used to model the correlated structure of genomic sequences. The use of intermittent maps, as opposed to other simple chaotic maps, is particularly suited for the production of long range correlation features which are observed in the genomic sequences of higher eucaryotes. A weighted network approach to symbolic sequences is introduced and it is shown that coupled intermittent polynomial maps produce degree and link size distributions with power law exponents similar to the ones observed in real genomes. The proposed network approach to symbolic sequences is generic and can be applied to any symbol sequence (artificial or natural).