Figure 2.

Effect of feedback regulation on the protein noise by numerical simulations. (a) The protein noise $\eta$ versus the protein mean $\langle n\rangle$ in positive-feedback networks under different choices of model parameters. The functional forms of $a_n$ and $b_n$ are chosen as $a_n=an$ and $b_n=b$. (b) The protein noise $\eta$ versus the protein mean $\langle n\rangle$ in negative-feedback networks under different choices of model parameters. The functional forms of $a_n$ and $b_n$ are chosen as $a_n=a$ and $b_n=bn$. In both (a) and (b), the red curve represents the Poisson noise and the model parameters are randomly chosen as $s\sim U\mathrm{[10,}\mathrm{500],}r\sim U\mathrm{[0,}\mathrm{10],}d=\mathrm{1,}p\sim U\mathrm{[0,}\mathrm{1],}a\sim U\mathrm{[0,}\mathrm{1000]}$, $b\sim U\mathrm{[0,}\mathrm{1000]}$, where $U[x,y]$ denotes the uniform distribution on the interval $[x,y]$.