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. 2012 Jan 26;8(1):e1002331. doi: 10.1371/journal.pcbi.1002331

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Hsia et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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The system (1) run with parameters , , , , and . Position, time, and concentrations are scaled to be dimensionless. See Figures S1, S2 for full simulation results. (A) Toy model network with labeled species. Coloring scheme for loops follows that of Figure 1B. (B) With , Turing instability conditions are met for a slight perturbation of the homogeneous initial condition with the second wave (), and growth of the inhomogeneity follows (top row). Solution looks qualitatively different than and the other species due to the “bleeding” effect of diffusion. With , Turing instability conditions are not met for and the initial inhomogeneity decays slowly in time (middle row). With , the initial inhomogeneity decays in time (bottom row).

Inline graphic — The system (1) run with parameters , , , , and . Position, time, and concentrations are scaled to be dimensionless. See Figures S1, S2 for full simulation results. (A) Toy model network with labeled species. Coloring scheme for loops follows that of Figure 1B. (B) With , Turing instability conditions are met for a slight perturbation of the homogeneous initial condition with the second wave (), and growth of the inhomogeneity follows (top row). Solution looks qualitatively different than and the other species due to the “bleeding” effect of diffusion. With , Turing instability conditions are not met for and the initial inhomogeneity decays slowly in time (middle row). With , the initial inhomogeneity decays in time (bottom row).