↓ Skip to main content

PLOS

An Analytically Solvable Model for Rapid Evolution of Modular Structure

Overview of attention for article published in PLoS Computational Biology, April 2009
Altmetric Badge

Mentioned by

blogs
1 blog
f1000
1 research highlight platform

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
167 Mendeley
citeulike
9 CiteULike
Title
An Analytically Solvable Model for Rapid Evolution of Modular Structure
Published in
PLoS Computational Biology, April 2009
DOI 10.1371/journal.pcbi.1000355
Pubmed ID
Authors

Nadav Kashtan, Avi E. Mayo, Tomer Kalisky, Uri Alon

Abstract

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 167 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 8%
Japan 5 3%
Spain 4 2%
France 2 1%
Portugal 2 1%
Brazil 2 1%
Canada 2 1%
Sweden 1 <1%
Singapore 1 <1%
Other 5 3%
Unknown 130 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 47 28%
Student > Ph. D. Student 42 25%
Professor > Associate Professor 18 11%
Professor 10 6%
Student > Master 9 5%
Other 28 17%
Unknown 13 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 76 46%
Physics and Astronomy 17 10%
Biochemistry, Genetics and Molecular Biology 16 10%
Computer Science 16 10%
Mathematics 6 4%
Other 18 11%
Unknown 18 11%