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Quantifying organismal complexity using a population genetic approach.

Olivier Tenaillon1, Olin K Silander, Jean-Philippe Uzan

  • 1Institut National de la Santé et de la Recherche Médicale (INSERM) U722, Faculté de Médecine Xavier Bichat, Université Denis Diderot-Paris VII, Paris, France. Olivier.Tenaillon@bichat.inserm.fr

Plos One
|February 15, 2007
PubMed
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This study introduces phenotypic complexity, a new metric for biological complexity based on natural selection. It offers a more objective measure than gene or cell counts, applicable across diverse organisms.

Area of Science:

  • Evolutionary Biology
  • Systems Biology
  • Genetics

Background:

  • Existing biological complexity metrics (gene count, cell types, metabolic processes) are often incongruent.
  • Increased knowledge of biological systems highlights limitations of current complexity definitions.

Purpose of the Study:

  • Propose a novel, objective metric for biological complexity: phenotypic complexity.
  • Measure complexity based on genetically uncorrelated phenotypic traits influencing fitness.
  • Establish a model linking population fitness (drift load) to phenotypic complexity.

Main Methods:

  • Developed a model to connect equilibrium fitness with phenotypic complexity.
  • Utilized viral evolution experiments for empirical validation.
  • Compared phenotypic complexity of bacteriophage X174 and vesicular stomatitis virus.

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Main Results:

  • Demonstrated the applicability and consistency of the phenotypic complexity metric.
  • Provided a quantitative measure of complexity rooted in natural selection.
  • Showcased the metric's utility in comparing viral species.

Conclusions:

  • Phenotypic complexity offers a more relevant measure of biological complexity than traditional metrics.
  • The metric is grounded in Darwinian evolution and natural selection, a unifying principle in biology.
  • This approach provides a fundamental biological perspective on complexity.