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Simulations in evolution. III. Randomness as a generator of opportunities
Bernard Testa1, Andrzej J Bojarski, Stefan Mordalski
1Department of Pharmacy, Lausanne University Hospital, BH04-CHUV, Rue du Bugnon 41, CH-1011 Lausanne, Switzerland. Bernard.Testa@chuv.ch
Chemistry & Biodiversity
|January 24, 2013
Summary
This study introduces a simulation model incorporating random variation, demonstrating that this factor significantly enhances population adaptability by increasing information content, measured by Shannon entropy (SE). The findings highlight variation
Area of Science:
- Evolutionary biology
- Computational biology
- Information theory
Background:
- Neo-Darwinism posits variation and natural selection as key evolutionary drivers.
- Previous work introduced a histogram model simulating population evolution and Shannon entropy (SE).
- The model maintained constant population size while tracking changes in phenotypic character distribution.
Purpose of the Study:
- To develop a computational tool for simulating evolutionary dynamics.
- To investigate the impact of random variation on population information content and adaptability.
- To analyze how Shannon entropy changes with the introduction of variation.
Main Methods:
- Developed a Perl application to implement a histogram-based evolutionary simulation.
- Incorporated a random factor simulating the percentage of offspring moving to adjacent bins (vicinal bins).
- Monitored Shannon entropy continuously to quantify population information content.
Main Results:
- The random variation factor significantly broadened the range of Shannon entropy values.
- Higher percentages of offspring moving to vicinal bins led to greater increases in SE.
- The simulation demonstrated a correlation between increased SE and enhanced population adaptability.
Conclusions:
- Random variation is crucial for increasing population information content.
- Increased information content, as measured by SE, facilitates adaptability in simulated populations.
- The model provides a framework for understanding the interplay between variation, information, and evolution.
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