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Rapid Transition towards the Division of Labor via Evolution of Developmental Plasticity

Overview of attention for article published in PLoS Computational Biology, June 2010
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153 Mendeley
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Title
Rapid Transition towards the Division of Labor via Evolution of Developmental Plasticity
Published in
PLoS Computational Biology, June 2010
DOI 10.1371/journal.pcbi.1000805
Pubmed ID
Authors

Sergey Gavrilets

Abstract

A crucial step in several major evolutionary transitions is the division of labor between components of the emerging higher-level evolutionary unit. Examples include the separation of germ and soma in simple multicellular organisms, appearance of multiple cell types and organs in more complex organisms, and emergence of casts in eusocial insects. How the division of labor was achieved in the face of selfishness of lower-level units is controversial. I present a simple mathematical model describing the evolutionary emergence of the division of labor via developmental plasticity starting with a colony of undifferentiated cells and ending with completely differentiated multicellular organisms. I explore how the plausibility and the dynamics of the division of labor depend on its fitness advantage, mutation rate, costs of developmental plasticity, and the colony size. The model shows that the transition to differentiated multicellularity, which has happened many times in the history of life, can be achieved relatively easily. My approach is expandable in a number of directions including the emergence of multiple cell types, complex organs, or casts of eusocial insects.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 7%
United Kingdom 6 4%
Italy 2 1%
Portugal 2 1%
France 1 <1%
Brazil 1 <1%
Unknown 131 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 31%
Researcher 34 22%
Student > Master 15 10%
Professor > Associate Professor 9 6%
Professor 8 5%
Other 28 18%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 86 56%
Biochemistry, Genetics and Molecular Biology 6 4%
Physics and Astronomy 6 4%
Mathematics 4 3%
Computer Science 4 3%
Other 27 18%
Unknown 20 13%