A Model for Selection of Eyespots on Butterfly Wings.

UNSOLVED PROBLEM: The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins). A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not. KEY IDEA AND MODEL: We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed) boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus point distributions observed in nature. RESULT: We therefore conclude that changes in the proximal boundary conditions are sufficient to explain the empirically observed distribution of eyespot focus points on the entire wing surface. The model predicts, subject to experimental verification, that the source strength of the activator at the proximal boundary should be lower in wing cells in which focus points form than in those that lack focus points. The model suggests that the number and locations of eyespot foci on the wing disc could be largely controlled by two kinds of gradients along two different directions, that is, the first one is the gradient in spatially varying parameters such as the reaction rate along the anterior-posterior direction on the proximal boundary of the wing cells, and the second one is the gradient in source values of the activator along the veins in the proximal-distal direction of the wing cell.

A model for population dynamics of the mimetic butterfly Papilio polytes in the Sakishima Islands, Japan.

We present a mathematical model for population dynamics of the mimetic swallowtail butterfly Papilio polytes in the Sakishima Islands, Japan. The model includes four major variables, that is, population densities of three kinds of butterflies (two female forms f. cyrus, f. polytes and the unpalatable butterfly Pachliopta aristolochiae) and their predator. It is well-known that the non-mimic f. cyrus resembles and attracts the male most, and the mimic f. polytes mimics the model butterfly P. aristolochiae. Based on experimental evidence, we assume that two forms f. cyrus and f. polytes interact under intraspecific competition for resources including the male, and the growth rate of f. cyrus is higher than that of f. polytes. We further assume that both the benefit of mimicry for the mimic f. polytes and the cost for the model are dependent on their relative frequencies, i.e. the motality of the mimic by predation decreases with increase in frequency of the model, while the motality of the model increases as the frequency of the mimic increases. Taking the density-dependent effect through carrying capacity into account, we set up a model system consisting of three ordinary differential equations (ODEs), analyze it mathematically and provide computer simulations that confirm the analytical results. Our results reproduce field records on population dynamics of P. polytes in the Miyako-jima Island. They also explain the positive dependence of the relative abundance (RA) of the mimic on the advantage index (AI) of the mimicry in the Sakishima Islands defined in Section 2.

Modeling parr-mark pattern formation during the early development of Amago trout.

This paper studies the formation of the large dark patterns, known as parr marks, that form on the Amago trout as it grows from the early larval stages to adulthood. The Amago trout, known as Oncorhynchus masou ishikawa, exhibits stripes during the early stages of development that in turn evolve (through reorientation and peak insertion) to form zigzag spot patterns as the fish grows to adulthood. By considering a standard representation of the Turing model for biological self-organization via interacting and diffusing morphogens, we illustrate that a diffusively driven instability can generate transient patterns consistent with those experimentally observed during the process of parr-mark formation in the early development of the Amago trout. Surface evolution is modeled through an experimentally driven growth function. Our studies conclude that the surface evolution profile, the surface geometry, and the curvature are key factors that play a pivotal role in reaction-diffusion systems in a study motivated by observations of Amago trout parr-mark pattern formation.

Pigmentation pattern formation in butterflies: experiments and models.

Butterfly pigmentation patterns are one of the most spectacular and vivid examples of pattern formation in biology. They have attracted much attention from experimentalists and theoreticians, who have tried to understand the underlying genetic, chemical and physical processes that lead to patterning. In this paper, we present a brief review of this field by first considering the generation of the localised, eyespot, patterns and then the formation of more globally controlled patterns. We present some new results applied to pattern formation on the wing of the mimetic butterfly Papilio dardanus.