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Phase Transitions02:31

Phase Transitions

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Speciation Rates01:07

Speciation Rates

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Overview
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Phase Diagram01:19

Phase Diagram

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The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).
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Related Experiment Video

Updated: Jun 29, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Quasi-Equilibrium States and Phase Transitions in Biological Evolution.

Artem Romanenko1, Vitaly Vanchurin1,2

  • 1Artificial Neural Computing, Weston, FL 33332, USA.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for biological system evolution using Shannon entropy (S) and Hamming distance (H). The findings reveal how systems transition between stable states, offering potential early pandemic warning capabilities.

Keywords:
Hamming distanceShannon entropybiological evolutionmultilevel learningphase transitionsquasi-equilibrium statesstatistical ensemblesthermodynamics

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Area of Science:

  • Evolutionary dynamics
  • Complex systems theory
  • Bioinformatics

Background:

  • Biological systems exhibit complex evolutionary dynamics.
  • Understanding transitions between stable states is crucial for predicting system behavior.
  • Previous models may not fully capture macroscopic evolutionary shifts.

Purpose of the Study:

  • To develop a macroscopic description of evolutionary dynamics.
  • To investigate the relationship between Shannon entropy (S) and average Hamming distance (H).
  • To explore the potential of this framework as an early warning system for pandemics.

Main Methods:

  • Following temporal dynamics of total Shannon entropy (S) and average Hamming distance (H).
  • Analyzing correlations between S and H to identify quasi-equilibrium states.
  • Statistical analysis of SARS-CoV-2 genomic data from the UK (March 2020 - December 2023).

Main Results:

  • Biological systems can persist in quasi-equilibrium states characterized by strong S-H correlations.
  • Phase transitions between quasi-equilibrium states involve discontinuous changes in thermodynamic parameters.
  • The analysis of SARS-CoV-2 data demonstrated the practical application of the model.

Conclusions:

  • The developed macroscopic description provides insights into biological system evolution and phase transitions.
  • The framework offers a theoretical basis for understanding system stability and change.
  • The model shows promise as an early warning system for emerging infectious diseases like pandemics.