Superstatistical model of bacterial DNA architecture

Abstract

Understanding the physical principles that govern the complex DNA structural organization as well as its mechanical and thermodynamical properties is essential for the advancement in both life sciences and genetic engineering. Recently we have discovered that the complex DNA organization is explicitly reflected in the arrangement of nucleotides depicted by the universal power law tailed internucleotide interval distribution that is valid for complete genomes of various prokaryotic and eukaryotic organisms. Here we suggest a superstatistical model that represents a long DNA molecule by a series of consecutive ~150 bp DNA segments with the alternation of the local nucleotide composition between segments exhibiting long-range correlations. We show that the superstatistical model and the corresponding DNA generation algorithm explicitly reproduce the laws governing the empirical nucleotide arrangement properties of the DNA sequences for various global GC contents and optimal living temperatures. Finally, we discuss the relevance of our model in terms of the DNA mechanical properties. As an outlook, we focus on finding the DNA sequences that encode a given protein while simultaneously reproducing the nucleotide arrangement laws observed from empirical genomes, that may be of interest in the optimization of genetic engineering of long DNA molecules.

Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

“…We use the superstatistical approach for this purpose. One major trend for describing complex systems is to adopt the superstatistical approach, which recently becomes a perspective tool in physics, engineering, biology and other fields [23][24][25]. The basic idea of the superstatistical approach is to describe a considered complex system as a superposition of distributed statistics each of which represents a locally equilibrated subsystem.…”

First-passage time for superstatistical Fokker-Planck models

“…At the beginning of its formulation, superstatistics was applied in the context of turbulent Lagrangian dynamics [4][5][6]. Nevertheless, since that, it has been also used for studying diverse kind of complex systems such as ultracold gases [7], quantum entanglement in Ising-like models [8], bacterial DNA architecture [9], migration of tumor cells [10], plasma physics [11], nanoscale electromechanical systems [12], work fluctuation theorems [13], and market signals [14] just to name a few [15][16][17][18][19][20][21].…”

Beyond Boltzmann–Gibbs–Shannon in Physics and Elsewhere

“…Also worth mentioning are selected entropic applications beyond BG in other areas of knowledge: complex networks [ 16 , 17 , 18 ]; economics [ 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ]; geophysics (earthquakes, atmosphere) [ 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 ]; general and quantum chemistry [ 139 , 167 , 168 , 169 , 170 , 171 ]; hydrology and engineering (water engineering [ 172 ] and materials engineering [ 173 , 174 ]); power grids [ 175 ]; the environment [ 176 ]; medicine [ 177 , 178 , 179 ]; biology [ 180 , 181 ]; computational processing of medical images (microcalcifications in mammograms [ 182 ] and magnetic resonance for multiple sclerosis [ 183 ]) and time series (e.g., ECG in coronary disease [ 184 ] and EEG in epilepsy [ 185 , 186 ]); train delays [ 187 ]; citations of scientific publications and scientometrics [ 188 , 189 ]; global optimization techniques [ 190 ...…”