The effects of low-impact mutations in digital organisms
Chase W Nelson1* and John C Sanford2
Corresponding author: Chase W Nelson cwnelson88@gmail.com
Author Affiliations
1 Rainbow Technologies, Inc., 877 Marshall Rd., Waterloo, NY 13165, USA
2 Department of Horticulture, NYSAES, Cornell University, Geneva, NY 14456, USA
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Theoretical Biology and Medical Modelling 2011, 8:9 doi:10.1186/1742-4682-8-9
The electronic version of this article is the complete one and can be found online at: http://www.tbiomed.com/content/8/1/9
Received: 27 January 2011
Accepted: 18 April 2011
Published: 18 April 2011
© 2011 Nelson and Sanford; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background
Avida is a computer program that performs evolution experiments with digital organisms. Previous work has used the program to study the evolutionary origin of complex features, namely logic operations, but has consistently used extremely large mutational fitness effects. The present study uses Avida to better understand the role of low-impact mutations in evolution.
Results
When mutational fitness effects were approximately 0.075 or less, no new logic operations evolved, and those that had previously evolved were lost. When fitness effects were approximately 0.2, only half of the operations evolved, reflecting a threshold for selection breakdown. In contrast, when Avida's default fitness effects were used, all operations routinely evolved to high frequencies and fitness increased by an average of 20 million in only 10,000 generations.
Conclusions
Avidian organisms evolve new logic operations only when mutations producing them are assigned high-impact fitness effects. Furthermore, purifying selection cannot protect operations with low-impact benefits from mutational deterioration. These results suggest that selection breaks down for low-impact mutations below a certain fitness effect, the selection threshold. Experiments using biologically relevant parameter settings show the tendency for increasing genetic load to lead to loss of biological functionality. An understanding of such genetic deterioration is relevant to human disease, and may be applicable to the control of pathogens by use of lethal mutagenesis.
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