The ABCs of Experimental Evolution. (7th March 2013)

• Record Type: Journal Article
• Authors: Abdo, Zaid; Stein, Mathew; Wojtowicz, Andrzej; Pepin, Kim M.
• Other Names: Castiglione F. Academic Editor.; Qiao A. Academic Editor.
• Is Part Of: ISRN computational biology. Volume 2013(2013)
• Publication Date: 2013-03-07
• Subjects: Computational biology -- Periodicals; Computational biology; Electronic journals; Periodicals; 570.285
• DOI: 10.1155/2013/467943
• Languages: English
• ISSNs: 2314-5420

Abstract

Abstract : 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 ϕ X174 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.