scholarly journals DNA energy constraints shape biological evolutionary trajectories

2019 ◽  
Author(s):  
Piero Fariselli ◽  
Cristian Taccioli ◽  
Luca Pagani ◽  
Amos Maritan

AbstractMost living systems rely on double-stranded DNA (dsDNA) to store their genetic information and perpetrate themselves. Thus, the biological information contained within a dsDNA molecule, in terms of a linear sequence of nucleotides, has been considered the main target of the evolution. However, in this information-centred perspective, certain DNA sequence symmetries are difficult to explain. Here we show that these patterns can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. Our predictions are valid for both prokaryotes and eukaryotes, and also inform the interpretation of observed codon biases and context-dependent mutation patterns in human populations. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the biological selective processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.

Author(s):  
Piero Fariselli ◽  
Cristian Taccioli ◽  
Luca Pagani ◽  
Amos Maritan

Abstract Most living organisms rely on double-stranded DNA (dsDNA) to store their genetic information and perpetuate themselves. This biological information has been considered as the main target of evolution. However, here we show that symmetries and patterns in the dsDNA sequence can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. The randomness justifies the human codon biases and context-dependent mutation patterns in human populations. Thus, the DNA ‘exceptional symmetries,’ emerged from the randomness, have to be taken into account when looking for the DNA encoded information. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the selective biological processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.


1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.


Recent advances in nucleic acid technology have facilitated the detection and detailed structural analysis of a wide variety of genes in higher organisms, including those in man. This in turn has opened the way to an examination of the evolution of structural genes and their surrounding and intervening sequences. In a study of the evolution of haemoglobin genes and neighbouring sequences in man and the primates, we have investigated gene arrangement and DNA sequence divergence both within and between species ranging from Old World monkeys to man. This analysis is beginning to reveal the evolutionary constraints that have acted on this region of the genome during primate evolution. Furthermore, DNA sequence variation, both within and between species, provides, in principle, a novel and powerful method for determining inter-specific phylogenetic distances and also for analysing the structure of present-day human populations. Application of this new branch of molecular biology to other areas of the human genome should prove important in unravelling the history of genetic changes that have occurred during the evolution of man.


Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.


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