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Related Experiment Videos

Quantitative evolutionary design.

Jared Diamond1

  • 1Department of Physiology, University of California Medical School, Los Angeles, CA 90095-1751, USA. jdiamond@mednet.ucla.edu

The Journal of Physiology
|July 18, 2002
PubMed
Summary
This summary is machine-generated.

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Quantitative evolutionary design explains biological safety factors, which minimize failure by balancing capacity and load. These safety factors, ranging from 1.2-10, are shaped by natural selection and have associated costs.

Area of Science:

  • Evolutionary biology
  • Quantitative biology
  • Engineering design

Background:

  • Quantitative evolutionary design analyzes biological reserve capacities (excesses over natural loads).
  • Safety factors, the ratio of capacity to load, range from 1.2-10 in engineered and biological systems.
  • Unlike engineered safety factors, biological ones evolve via natural selection.

Purpose of the Study:

  • To understand the magnitudes of biological reserve capacities using evolutionary reasoning.
  • To explore the factors influencing safety factors in biological and engineered systems.
  • To identify unsolved questions regarding safety factors in complex biological systems.

Main Methods:

  • Analysis of safety factors as ratios of capacity to load.

Related Experiment Videos

  • Comparison of engineered and biologically evolved safety factors.
  • Examination of factors influencing safety factor magnitudes, including variation, deterioration, and costs.

    • Main Results:

    • Safety factors minimize the overlap between capacity and load distributions, preventing performance failure.
    • Safety factors increase with load/capacity variation, time-dependent deterioration, and cost of failure.
    • Adaptive regulation can lead to decreasing safety factors towards 1.0 with increasing load.

    Conclusions:

    • Modest safety factor sizes suggest costs associated with excess capacity, such as wasted energy, space, or opportunity costs.
    • Further research is needed on safety factors in series, parallel, and multi-functional biological systems.
    • Evolutionary principles provide a framework for understanding safety factors across diverse systems.