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Related Concept Videos

Aging01:26

Aging

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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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Computational systems biology for aging research.

Mark T Mc Auley1, Kathleen M Mooney

  • 1Faculty of Science and Engineering, Thornton Science Park, University of Chester, Chester, UK.

Interdisciplinary Topics in Gerontology
|October 25, 2014
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Summary
This summary is machine-generated.

Computational modeling in systems biology uses data to simulate biological systems, particularly for aging research. This approach helps investigate healthy aging and age-related disorders, with future applications in aging studies.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Gerontology

Background:

  • Computational modeling is integral to systems biology.
  • It leverages diverse data to quantitatively represent and simulate biological systems.
  • This chapter explores its application in understanding aging.

Purpose of the Study:

  • To describe computational modeling in systems biology.
  • To explain its rationale and appropriateness for aging research.
  • To showcase models for healthy and unhealthy aging.

Main Methods:

  • Discussion of computational modeling principles.
  • Explanation of model assembly and theoretical frameworks.
  • Presentation of specific computational models applied to aging.

Main Results:

  • Demonstration of computational approaches for healthy aging trajectories.
  • Showcasing models for complex age-related disorders.
  • Highlighting the effectiveness of various computational methods.

Conclusions:

  • Computational systems modeling is a powerful tool for aging research.
  • It aids in investigating both healthy aging and age-related diseases.
  • Future applications promise advancements in understanding and intervening in the aging process.