Isolating and quantifying the role of developmental noise in generating phenotypic variation
Fig 3
Automated disk-shaped pattern selection of parietal, post-orbital head region of eight geckos at nine weeks.
For each Gecko ID (numbers on the left): Left: Images of the eight gecko heads at nine weeks; Second column: the disk-shaped parietal, post-orbital (DSPPO) region that was selected for pattern analysis, preserving their relative sizes; Third column: Final pigment pattern identified by image analysis with the skeletonization of the image overlaid. Fourth column: Best phenotype match of 100 patterns simulated by the corresponding LALI-type using the linear model. Right: Best phenotype match of 100 patterns simulated by the corresponding LALI-type using the FitzHugh-Nagumo model. Horizontal bars indicate 0.5 cm. Geckos are ordered by decreasing fractional spot area of the pattern (see Table 2 for definitions). Note that in some cases in the nonlinear model, the spots have a ‘ringed’ appearance. This is the result of morphogen profiles that have a maximum concentration around the border of the spots. We note that the lack of pigment in the interior is contingent upon finely tuned threshold values, which means that the robustness of these patterns to perturbations of the sensing mechanism of the cells is likely quite weak. Our search algorithm identifies sets of parameters that yield matches to specified pattern statistics (here, fractional area and eccentricity). While the algorithm may find that finely tuned thresholds give the best match, in future applications, additional prescriptions can be applied for a match such as requiring that the pattern matches are robust to small percent perturbations or that spots do not have interior holes.