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. 2009 Apr 21;106(16):6837-42.
doi: 10.1073/pnas.0810311106. Epub 2009 Apr 7.

The Neural Origins of Shell Structure and Pattern in Aquatic Mollusks

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Free PMC article

The Neural Origins of Shell Structure and Pattern in Aquatic Mollusks

Alistair Boettiger et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

We present a model to explain how the neurosecretory system of aquatic mollusks generates their diversity of shell structures and pigmentation patterns. The anatomical and physiological basis of this model sets it apart from other models used to explain shape and pattern. The model reproduces most known shell shapes and patterns and accurately predicts how the pattern alters in response to environmental disruption and subsequent repair. Finally, we connect the model to a larger class of neural models.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Shell-making machinery. (A) EM of the mollusk mantle. The EM of a nautilus mantle is shown, with secretory epithelial cells stained green and nerve axons stained red. [Images reproduced with permission from ref. (Copyright 2005, Wiley).] (B) Schematic representation of the mantle, showing the neurosensory cells, the circumpallial axons connecting these cells, and the neurons that control shell and pigment secretion. (C) Schematic illustration of the model. The existing edge pattern induces excitatory firing (red); the older, previous pattern is inhibitory (blue), indicated in the upper trace. For simulation purposes we use a Gaussian spatial kernel WE,I = αE,Iexp(−x2E,I2). Excitatory stimulation leads to sharp stimulation of the local region and weak inhibition of an even wider surrounding region. Neural stimuli are passed through saturating sigmoidal filters to determine the spatial pattern of activation or inhibition on the pigment secreting cells. We use SE,I = 1/[1 + exp(νE,IE,IK])], where K is the input from the spatial kernel. The pigment is then Pt+1 = SE(WE·Pt) + SI(WE·Pt) − Rt, with Rt = γPt−1 + δRt−1 (see section A of SI Appendix for derivation, discussion, and alternate forms).
Fig. 2.
Fig. 2.
Explaining structure. (A) The neural model explains how the aperture-growth vectors arise from the neural architecture in the mantle. The bottom plots show neural excitation at 2 different times (4 days apart). The effective growth vectors resulting from this pattern of excitation is shown at the top. (B) Aperture-growth vectors generated by the model to create spiral-shaped gastropods.
Fig. 3.
Fig. 3.
Simple bifurcation patterns. In all images, the real shell is shown on the left, and the simulated shell on the right. (A) The gradual stabilization from random noise (Bottom) into periodic stripes (Top) shows how Turing instabilities give rise to patterns of stable bands perpendicular to the growing shell edge. Note that activation centers separate and shift as each one carves out a domain of influence. (B) Turing patterns. B. fasciata exhibits Turing bands of pigment, and Turritella exhibits structural ridge bands. (C) Phase plot of model variables shows the periodic orbits of a limit cycle created by a Hopf bifurcation. (D) Patterns of periodic stripes. This periodic activity may influence secretion of structural elements instead of pigment, resulting in the periodic flanges seen on E. scalare. The zigzag stripes shown on N. communis were generated by a combination of Hopf bifurcation with wave generation as described in Fig. 4. (E) Hopf bifurcations and Turing instabilities can occur simultaneously, leading to patterns like that of N. tigrina.
Fig. 4.
Fig. 4.
Wave patterns. (A) Patterns formed by traveling waves of excitation. Asymmetric regions of excitation travel toward the stronger side, as illustrated by C. clerii in B. When these waves collide, they may reflect, as shown in this simulation and on the shells of C. clerii and C. marblus. The waves may annihilate, as shown on the shells of Conus viceweei. A wave may also emit “reverse” waves, creating the beautiful tent patterns of Olivia porphyria. (B) Graphs of neural activity across the leading edge and close-up of resulting secretions for select shells. (C) Some traveling waves of excitation leave excited regions behind as they cross the mantle. At a critical width, the cumulative inhibition shuts down signaling, creating a region devoid of pigment. This region is slowly reclaimed by waves traveling back into it, as shown in the simulation of C. innexa and C. marblus in B and C. (D) Some of the diverse patterns produced by the model by combinations of wave collisions and emissions.
Fig. 5.
Fig. 5.
The effects on patterns of shell growth and perturbations. As the shell grows, the width of the pattern domain increases leading to changes in the pattern. (A–E) These patterns include line bifurcations of T. testudinalis (A), collapse of oscillations in Amoria grayi (B), destabilization of waves into patchy dots on M. mitra stictica (C), emergence of a pattern from a uniform field in Babylonia spirita (D), and transition from annihilating to reflecting waves on T. litarus (E). Patterns change in response to scratches, which remove information about the previous pattern. (F) Traveling waves in Strigilla shells slow down and deflect away from the growing edge. [Photo adapted and reproduced with permission from ref. (Copyright 1987, Elsevier.)] (G) B. fasciata goes through repeated stabilizations from dots to stripes. Shells in B–D are from ref. .

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