Title
ModEDI: An extendable software architecture for examining the effects of developmental interactions on evolutionary trajectories
Document Type
Conference Proceeding
Department or Administrative Unit
Biological Sciences
Publication Date
10-19-2017
Abstract
Quantitative genetics is the study of complex biological traits, or traits controlled by more than one gene. A primary goal of quantitative genetic studies is the development of computational models for predicting the evolution of such traits in response to selection. Most models for analyzing the evolution of multiple traits employ a constant genetic variance-covariance matrix (G-matrix) to describe the distribution of genetic variation. While G-matrix based models provide a reasonable approximation of short term evolution, they may not sufficiently capture all important associations between traits. For example, nonlinear interactions between developmental factors underlying the production of traits can result in dramatic alteration of genetic variances and covariances as evolution proceeds. To aid in investigating this issue, we have developed an object-oriented code base, Models of Evolution with Developmental Interactions (ModEDI), that implements a powerful, general mathematical and conceptual framework developed by Sean Rice. This framework utilizes a phenotypic landscape to explicitly incorporate the effect of development in shaping heritable phenotypic variation. With our program, users can develop custom simulations for analyzing the evolution of multiple phenotypic traits that involve interactions between overlapping sets of developmental factors. Initial results from simulations performed with ModEDI indicate that developmental interactions may substantially alter evolutionary trajectories.
Recommended Citation
Brooks, E., Roberts, G., Scoville, A., & Jagodzinski, F. (2017). ModEDI: An extendable software architecture for examining the effects of developmental interactions on evolutionary trajectories. 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). https://doi.org/10.1109/iccabs.2017.8114300
Journal
2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)
Copyright
© 2017 IEEE
Comments
This article was originally published in 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). The full-text article from the publisher can be found here.
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