“This is very beautiful. It is neat, it is modern technology, and it is fast. I am just wondering very seriously about the biological validity of what we are doing with this machine.” – Melvin Moss, 1967*

“This machine” to which Moss referred nearly 50 years ago was not a contraption to clone a Neandertal or a Godzilla-like MechaGodzilla, but a computer. Along these lines, a paper came out recently describing a new, automated method for analyzing (biological) shapes, and while I think the method is pretty sweet, I think future researchers employing it should keep Moss’s monition in mind.

Doug Boyer and colleagues (2011) present “Algorithms to automatically quantify the geometric similarity of anatomical surfaces.” It seems the main goals of the study were to make shape analysis [1] faster and [2] easier for people who don’t otherwise study anatomy (such as geneticists), making it possible [3] to amass large phenotypic datasets comparable to the troves of genetic data accumulated in recent years. Using some intense math that’s way over my head, the computer algorithm takes surface data (acquired through CT or laser scans) of a pair of specimens and automatically fits these forms with a “correspondence map” linking geometrically (and not necessarily biologically) homologous features between the two. It then uses the map to fit landmarks (a la geometric morphometrics) which are used to calculate the shape difference metric between individuals in the pairings.

See at the right just how pretty it is! The authors posit that this technique could be used with genetic knock-out studies to assess how certain genes affect the development of bones and teeth, or to model the development of organs. That certainly would be useful in biomedical and evo-devo research.

But while I appreciate the automated-ness of the procedure, I don’t think we can simply write off the role of the biologist in determining what features are homologous, in favor of a computer. The paper itself illustrates this nicely. The authors state that there is debate about the origins of a cusp on the molar tooth of the sportive lemur (Lepilemur) – is it the same as the entoconid of the living mouse lemur, or the enlarged metaconid of the extinct “koala lemur”? Their automated algorithm can map the sportive lemur’s mystery cusp to match either alternative scenario. It is the external paleontological and phylogenetic evidence, not the intrinsic shape information, that renders the alternative scenario more plausible.

So let me reiterate that I think this paper presents an important step for the study of the biology of form, or the form of biology. Automating the analysis of form will certainly expedite studies of large datasets (not to mention freeing up the time of hordes of research assistants). But I hope that researchers employing this procedure will have a little Mossian Angel (poor play on “guardian angel,” sorry) on their shoulders, reminding them that the algorithm won’t necessarily show them homology better than their own experience. And I hope all biologists have this Mossian Angel there, reminding them that even though this method is “neat … modern technology, and … fast,” it may not be the most appropriate method for their research question.

References
Boyer, D., Lipman, Y., St. Clair, E., Puente, J., Patel, B., Funkhouser, T., Jernvall, J., & Daubechies, I. (2011). Algorithms to automatically quantify the geometric similarity of anatomical surfaces Proceedings of the National Academy of Sciences, 108 (45), 18221-18226 DOI: 10.1073/pnas.1112822108

*This quote comes from a discussion at the end of a symposium: Cranio-Facial Growth in Man (1967). RE Moyers and WM Krogman, editors. New York: Pergamon Press.