Models for exploring genome-fitness mapping in an introductory course
- Published
- Accepted
- Subject Areas
- Computational Biology, Science and Medical Education
- Keywords
- NetLogo, education, simulation, fitness, model, sequence, undergraduate, agent-based, genetic algorithm, evolutionary algorithm
- Copyright
- © 2016 Thompson
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Models for exploring genome-fitness mapping in an introductory course. PeerJ Preprints 4:e2647v1 https://doi.org/10.7287/peerj.preprints.2647v1
Abstract
Evolutionary theory presents a number of conceptual hurdles for the undergraduate student. In addition, the extended timespan over which many evolutionary processes occur, coupled with the time constraints of the typical undergraduate laboratory, restricts the range of relevant laboratory exercises available to augment instruction in an undergraduate course. Computer-based simulations have the potential to overcome this barrier as they allow interaction with models of natural processes which occur on long timescales. Such simulations allow the student to pursue what would be impossible at the bench: to investigate multiple scenarios and to collect, visualize, and analyze large amounts of data. Here, we describe a set of agent-based models, developed using the NetLogo platform, designed to facilitate developing a better understanding of the interplay between random processes, organismal fitness, encoding, and genomic sequence space.
Author Comment
This is a preprint submission to PeerJ Preprints. This is a draft form of this paper. Any questions, comments, or suggested revisions are welcome and are highly appreciated.