Breakthrough Model Reveals Evolution of Ancient Nervous Systems Through Seashell Colors
ScienceDaily (Jan. 12, 2012) — Determining the evolution of pigmentation patterns on mollusk seashells -- which could aid in the understanding of ancient nervous systems -- has proved to be a challenging feat for researchers. Now, however, through mathematical equations and simulations, University of Pittsburgh and University of California, Berkeley, researchers have used 19 different species of the predatory sea snail Conus to generate a model of the pigmentation patterns of mollusk shells.
The predatory sea snail Conus was used to generate a model of the pigmentation patterns of mollusk shells. (Credit: © by-studio / Fotolia)
"There is no evolutionary record of nervous systems, but what you're seeing on the surface of seashells is a space-time record, like the recording of brain-wave activity in an electroencephalogram (EEG)," said project coinvestigator G. Bard Ermentrout, Pitt Distinguished University Professor of Computational Biology and a professor in the Kenneth P. Dietrich School of Arts and Sciences' Department of Mathematics.
Seashells differ substantially between the closely related Conus species, and the complexity of the patterns makes it difficult to properly characterize their similarities and differences. It also has proven difficult to describe the evolution of pigmentation patterns or to draw inferences about how natural selection might affect them. In a paper published in the Jan. 3 issue of the Proceedings of the National Academy of Sciences (PNAS) Online, Ermentrout and his colleagues attempt to resolve this problem by combining models based on natural evolutionary relationships with a realistic developmental model that can generate pigmentation patterns of the shells of the various Conus species.
In order for UC Berkeley scientists to create simulations, Ermentrout and his collaborators developed equations and a neural model for the formation of the pigmentation patterns on shell surfaces. With the equations in hand, Zhenquiang Gong, a UC Berkeley graduate student in engineering, used a computer to simulate the patterns on the shells, hand fitting the parameters to create a basic model for the patterns of a given species.
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Evolution of patterns on Conus shells
Zhenqiang Gonga, Nichilos J. Matzkeb, Bard Ermentroutc, Dawn Songa, Jann E. Vendettib, Montgomery Slatkinb, and George Osterd,1
Author Affiliations
Departments of aElectrical Engineering and Computer Science and
bIntegrative Biology, University of California, Berkeley, CA 94720;
cDepartment of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260; and
dDepartments of Molecular and Cell Biology and Environmental Science, Policy and Management, University of California, Berkeley, CA 94720
Contributed by George Oster, December 12, 2011 (sent for review September 8, 2011)
Abstract
The pigmentation patterns of shells in the genus Conus can be generated by a neural-network model of the mantle. We fit model parameters to the shell pigmentation patterns of 19 living Conus species for which a well resolved phylogeny is available. We infer the evolutionary history of these parameters and use these results to infer the pigmentation patterns of ancestral species. The methods we use allow us to characterize the evolutionary history of a neural network, an organ that cannot be preserved in the fossil record. These results are also notable because the inferred patterns of ancestral species sometimes lie outside the range of patterns of their living descendants, and illustrate how development imposes constraints on the evolution of complex phenotypes.
pattern formation, developmental evolution, phylogenetics, ancestral inference
Footnotes
1To whom correspondence should be addressed. E-mail: goster@berkeley.edu.
Author contributions: Z.G., B.E., D.S., M.S., and G.O. designed research; Z.G., N.J.M., and J.E.V. performed research; Z.G., N.J.M., M.S., and G.O. analyzed data; and Z.G., N.J.M., B.E., M.S., and G.O. wrote the paper.
The authors declare no conflict of interest.
Data deposition: The computational parameters for the pattern formation model for each of the described species are available upon request.
*Hendricks JR, Geological Society of America Annual Meeting, November 2–5, 2003, Seattle, WA.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1119859109/-/DCSupplemental.
Freely available online through the PNAS open access option.
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