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Related Concept Videos

Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
Mismatch Repair01:36

Mismatch Repair

Overview
Mutations in Microorganisms01:18

Mutations in Microorganisms

Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
Translesion DNA Polymerases02:10

Translesion DNA Polymerases

Translesion (TLS) polymerases rescue stalled DNA polymerases at sites of damaged bases by replacing the replicative polymerase and installing a nucleotide across the damaged site. Doing so, TLS allows additional time for the cell to repair the damage before resuming regular DNA replication.
TLS polymerases are found in all three domains of life - archaea, bacteria, and eukaryotes. Of the different classes of TLS polymerases, members of the Y family are fitted with specialized structures that...

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Updated: May 13, 2026

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Pathogen mutation modeled by competition between site and bond percolation.

Laurent Hébert-Dufresne1, Oscar Patterson-Lomba, Georg M Goerg

  • 1Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec, Québec, Canada G1V 0A6.

Physical Review Letters
|March 26, 2013
PubMed
Summary

This study introduces a network science model for epidemic spread and antiviral treatment, revealing hysteresis and phase transitions. It shows how microscopic changes can cause large epidemic size jumps on networks.

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Area of Science:

  • Network science
  • Epidemiology
  • Statistical mechanics

Background:

  • Disease propagation is a key focus in network science.
  • The coevolution of epidemics and treatments on networks remains understudied.
  • Antiviral administration and resistance development are critical factors in disease management.

Purpose of the Study:

  • To analyze the coevolution of epidemic spread and antiviral treatment within a network framework.
  • To investigate the emergence of hysteresis and phase transitions in epidemic models.
  • To explore the existence and implications of first-order phase transitions on networks.

Main Methods:

  • Developed a mean-field and stochastic analysis of an epidemic model.
  • Incorporated antiviral administration and resistance development into the model.
  • Mapped the epidemic model to a coevolutive competition between site and bond percolation.

Main Results:

  • Demonstrated hysteresis and both second- and first-order phase transitions in the model.
  • Showed that first-order phase transitions exist on networks, a long-standing question.
  • Identified that microscopic changes in infection rate can lead to macroscopic jumps in epidemic size.

Conclusions:

  • The coevolution of disease and treatment can exhibit complex phenomena like hysteresis and phase transitions on networks.
  • The existence of first-order phase transitions on networks has significant implications for understanding epidemic dynamics.
  • This framework provides new insights into epidemic control strategies considering treatment resistance.