An object-oriented, individual-based approach for simulating the dynamics of genes in subdivided populations

Kool, Johnathan T. (2009) An object-oriented, individual-based approach for simulating the dynamics of genes in subdivided populations. Ecological Informatics, 4 (3). pp. 136-146.

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Abstract

An object-oriented, individual-based simulation framework was developed for modeling the diffusion of genetic material in subdivided populations. Objects representing individual organisms were defined, each with a unique genotype composed of gene objects. The organisms mate and reproduce, and progeny disperse or recruit back to their native population through the use of a Movement interface. The object-oriented approach is also linked to analytical theory through the development of matrix-based equations. An implementation of the model demonstrates how changes to basic population parameters affect spatial and temporal genetic structure. Scalar changes to the system affect the duration over which processes occur as well as the degree of variance, but appear to leave overall structural patterns unchanged. Object-oriented programming provides some unique advantages for modeling population genetic processes, including the use of abstraction and implementation, as well as the ability to accommodate complex, heterogeneous behavior.

Item ID: 9094
Item Type: Article (Refereed Research - C1)
Keywords: connectivity; individual-based models; object-oriented programming; landscape genetics; autocorrelation
ISSN: 1574-9541
Funders: Australian Research Council, Australian Institute of Marine Science, Maytag Chair, University of Miami
Date Deposited: 23 Mar 2010 00:55
FoR Codes: 01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational Mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified @ 30%
08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080399 Computer Software not elsewhere classified @ 10%
06 BIOLOGICAL SCIENCES > 0604 Genetics > 060411 Population, Ecological and Evolutionary Genetics @ 60%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 20%
97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 60%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 20%
Citation Count from Web of Science Web of Science 5
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