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Fig 1.

Overview of the mechanisms assumed in the model.

Cancer cells divide at a rate α(1 + g), where g is the local growth factor (GF) concentration, and die at a constant rate μ. The GF is produced at rate ρ by all cancer cells and decays at rate δ. Lastly, cells migrate at rate ν. Parameter values are provided in Table 1.

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Fig 1 Expand

Table 1.

Model parameters.

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Table 1 Expand

Fig 2.

Growth rates.

(A) The population growth rate (3) as a function of the population density for three different values of the death rate μ. The inset shows the entire range of growth rates. (B) The per-capita growth rate f(n)/n as a function of the population density for three different values of the death rate μ. Here the Allee effect is evident as an increasing per-capita growth rate at low densities. All parameter values are given in Table 1 and the critical death rate is calculated according to (8).

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Fig 2 Expand

Fig 3.

Comparison of the ODE-model and IB-model under long range dispersal.

(A) The dynamics of the IB-model (dashed) and the numerical solution of (3) (solid) under long range dispersal for three different initial conditions. All parameter values are given in Table 1 and the death rate μ = 3.75 × 10−5. (B) The population density after 11 days of the IB-model (circles) and the numerical solution of (3) (solid line) under long range dispersal and no cell migration when the death rate μ is varied. All other parameter values are given in Table 1.

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Fig 3 Expand

Fig 4.

The population density after 11 days as a function of the death rate μ.

Results from the IB-model (circles) and the numerical solution of (3) (solid line) under (A) short range dispersal and no cell migration and (B) short range dispersal and a cellular diffusion coefficient of Dc = 1.6 × 10−10 cm2/s. All other parameter values are given in Table 1.

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Fig 5.

Least squares fit of the Allee ODE-models to in vitro growth of the glioblastoma cell lines U3013MG, U3123MG and U3289MG.

Panels A-C show the best fit of the Allee model whereas D-F show the best fit for a logistic growth model (where ρ = 0). Visual inspection suggests that the Allee model outperforms the logistic model. This was confirmed using Akaike Information Criterion (see Table 2).

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Fig 5 Expand

Table 2.

Details of model fit to data.

The parameter values (A, B, μ) refer to the optimal fit for the Allee model. For a visual comparison see Fig 5.

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Table 2 Expand