TY - JOUR
T1 - Modelling and visualizing morphology in the fungus Alternaria
AU - Taralova, Ekaterina H.
AU - Schlecht, Joseph
AU - Barnard, Kobus
AU - Pryor, Barry M.
N1 - Funding Information:
This work was supported in part by the University of Arizona College of Science , the University of Arizona College of Agriculture and Life Science , and National Science Foundation, Division of Environmental Biology (NSF-DEB) # 0416283 . The authors also wish to thank Dr Emory Simmons for permission to reproduce some of his exceptional drawings, which have guided mycologists for many years.
PY - 2011/11
Y1 - 2011/11
N2 - Alternaria is one of the most cosmopolitan fungal genera encountered and impacts humans and human activities in areas of material degradation, phytopathology, food toxicology, and respiratory disease. Contemporary methods of taxon identification rely on assessments of morphology related to sporulation, which are critical for accurate diagnostics. However, the morphology of Alternaria is quite complex, and precise characterization can be laborious, time-consuming, and often restricted to experts in this field. To make morphology characterization easier and more broadly accessible, a generalized statistical model was developed for the three-dimensional geometric structure of the sporulation apparatus. The model is inspired by the widely used grammar-based models for plants, Lindenmayer-systems, which build structure by repeated application of rules for growth. Adjusting the parameters of the underlying probability distributions yields variations in the morphology, and thus the approach provides an excellent tool for exploring the morphology of Alternaria under different assumptions, as well as understanding how it is largely the consequence of local rules for growth. Further, different choices of parameters lead to different model groups, which can then be visually compared to published descriptions or microscopy images to validate parameters for species-specific models. The approach supports automated analysis, as the models can be fit to image data using statistical inference, and the explicit representation of the geometry allows the accurate computation of any morphological quantity. Furthermore, because the model can encode the statistical variation of geometric parameters for different species, it will allow automated species identification from microscopy images using statistical inference. In summary, the approach supports visualization of morphology, automated quantification of phenotype structure, and identification based on form.
AB - Alternaria is one of the most cosmopolitan fungal genera encountered and impacts humans and human activities in areas of material degradation, phytopathology, food toxicology, and respiratory disease. Contemporary methods of taxon identification rely on assessments of morphology related to sporulation, which are critical for accurate diagnostics. However, the morphology of Alternaria is quite complex, and precise characterization can be laborious, time-consuming, and often restricted to experts in this field. To make morphology characterization easier and more broadly accessible, a generalized statistical model was developed for the three-dimensional geometric structure of the sporulation apparatus. The model is inspired by the widely used grammar-based models for plants, Lindenmayer-systems, which build structure by repeated application of rules for growth. Adjusting the parameters of the underlying probability distributions yields variations in the morphology, and thus the approach provides an excellent tool for exploring the morphology of Alternaria under different assumptions, as well as understanding how it is largely the consequence of local rules for growth. Further, different choices of parameters lead to different model groups, which can then be visually compared to published descriptions or microscopy images to validate parameters for species-specific models. The approach supports automated analysis, as the models can be fit to image data using statistical inference, and the explicit representation of the geometry allows the accurate computation of any morphological quantity. Furthermore, because the model can encode the statistical variation of geometric parameters for different species, it will allow automated species identification from microscopy images using statistical inference. In summary, the approach supports visualization of morphology, automated quantification of phenotype structure, and identification based on form.
KW - Modelling
KW - Morphology
KW - Morphometrics
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U2 - 10.1016/j.funbio.2011.08.002
DO - 10.1016/j.funbio.2011.08.002
M3 - Article
C2 - 22036294
AN - SCOPUS:80955163636
SN - 1878-6146
VL - 115
SP - 1163
EP - 1173
JO - Fungal Biology
JF - Fungal Biology
IS - 11
ER -