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Mutation, Gene Flow, and Genetic Drift01:09

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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).
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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Modeling dynamics for oncogenesis encompassing mutations and genetic instability.

Artur C Fassoni1, Hyun M Yang2

  • 1Instituto de Matemática e Computação, UNIFEI, Itajubá, Minas Gerais, Brazil.

Mathematical Medicine and Biology : a Journal of the IMA
|June 28, 2018
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Summary
This summary is machine-generated.

This study models cancer development, revealing apoptosis and tissue repair as key early barriers. Genetic instability and mutation rates significantly influence tumor progression and detection time.

Keywords:
avascular tumour growthbifurcationsmulti-step tumorigenesisstability

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Area of Science:

  • Oncology
  • Mathematical Biology
  • Computational Biology

Background:

  • Tumorigenesis is a multistep process involving genetic alterations.
  • Cancer development is characterized by hallmarks like uncontrolled growth and apoptosis evasion.
  • Understanding these steps is crucial for cancer research.

Purpose of the Study:

  • To propose a mathematical model for cancer onset and development.
  • To incorporate key cancer hallmarks and genetic instability into the model.
  • To analyze the dynamics of normal, premalignant, and cancer cells.

Main Methods:

  • Developed a mathematical model with three cell populations: normal, premalignant, and cancer.
  • Included nonlinear dynamics for mutation from premalignant to cancer cells, representing genetic instability.
  • Performed mathematical analysis and numerical simulations using breast cancer data.

Main Results:

  • Apoptosis and tissue repair are identified as primary barriers against tumor progression.
  • Corruption of these systems is necessary for cancer to develop from a single mutant cell.
  • Aggressive cancer cells can promote the survival of less adapted premalignant cells.

Conclusions:

  • The rates of apoptosis and mutations critically affect tumor progression speed.
  • These factors also influence the time required for a tumor to become clinically detectable.
  • Mathematical modeling provides insights into cancer development dynamics and progression timelines.