Figure 5

Noise of the signaling molecule as a function of the diffusion coecient. Noise ${\eta }_{c_A}^2$ as a function of diffusion coefficient $\tilde{D}$ for the set of parameters γ₂ (see Figure 2B): stochastic simulations (circles) and analytical expression. Eq. (18), (solid line). By using the decomposition ${\eta }_{c_A}^2={\eta }_{c_A,\mathrm{int}}^2+{\eta }_{c_A,\text{tran}}^2$ the differences between the computational and the theoretical distributions quantifies the amount of intrinsic noise (squares). As evidenced by the linear regression (blue short-dashed line) the later remains constant and is the main contribution to the total noise only for large diffusion values, $\tilde{D}$ > 10⁴ (see text).