Table of Contents
ISRN Computational Biology
Volume 2013 (2013), Article ID 467943, 7 pages
Research Article

The ABCs of Experimental Evolution

1Departments of Statistics, University of Idaho, Moscow, ID 83844, USA
2Department of Mathematics, University of Idaho, Moscow, ID 83844, USA
3Program of Bioinformatics and Computational Biology (BCB), University of Idaho, Moscow, ID 83844, USA
4USDA-ARS South Atlantic Area, 950 College Station Road, Athens, GA 30605-2720, USA
5Department of Computer Engineering, University of Idaho, Moscow, ID 83844, USA
6Micron Technology Inc., Boise, ID 83716, USA
7Department of Animal Sciences, Washington State University, Pullman, WA 99164, USA
8Fogarty International Center, National Institutes of Health, 31 Center Drive, MSC 2220, Bethesda, MD 20892-2220, USA
9Department of Biology, Colorado State University, Campus Delivery 1878, Fort Collins, CO 80523, USA

Received 15 January 2013; Accepted 14 February 2013

Academic Editors: F. Castiglione and A. Qiao

Copyright © 2013 Zaid Abdo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Microbial evolution is complex and is influenced by many sources of variation. Experimental evolution is no exception, although it is more controlled, easily replicated, and typically devoid of interactions between species. Mathematical modeling of the evolutionary process can help in understanding the underlying mechanisms that drive outcome of such experiments. These models can be complex and parameter rich, limiting their feasibility for statistical inference. In this paper, we introduce the use of Approximate Bayesian Computation (ABC) as a tool for statistical inference in the study of experimental evolution. ABC is a fast and simple method for fitting complex models to data. We utilize this method, coupled with a mechanistic model of experimental evolution, to study the evolution process of bacteriophage ϕX174 under benign selection pressure. Our results highlight three mutation-selection scenarios that could explain this process: high mutation/low selection pressure, low mutation/high selection pressure, and low mutation/low selection pressure, with posterior support of 19%, 9.5%, and 71.5% for each of these scenarios, respectively. Sequence data support the first candidate. Though surprising, this scenario was not improbable based on our analysis.