Mechanisms of information decoding in a cascade system of gene expression.
Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However, the mechanism of how this response heterogeneity is generated remains elusive. Here, by systematically analyzing a representative cellular signaling system, we show that (1) the upstream activator always amplifies the downstream burst frequency (BF) but the noiseless activator performs better than the noisy one, remarkably for small or moderate input signal strengths, and the repressor always reduces the downstream BF but the difference in the reducing effect between noiseless and noise repressors is very small; (2) both the downstream burst size and mRNA mean are a monotonically increasing function of the activator strength but a monotonically decreasing function of the repressor strength; (3) for repressor-type input, there is a noisy signal strength such that the downstream mRNA noise arrives at an optimal level, but for activator-type input, the output noise intensity is fundamentally a monotonically decreasing function of the input strength. Our results reveal the essential mechanisms of both signal information decoding and cellular response heterogeneity, whereas our analysis provides a paradigm for analyzing dynamics of noisy biochemical signaling systems.
School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.|Department of Mathematics, College of Information Science and Technology, Hainan University, Haikou 570228, People's Republic of China.
Haohua
H
Wang
School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Zhanjiang
Z
Yuan
School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Peijiang
P
Liu
School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Tianshou
T
Zhou
D015870:Gene Expression; D008954:Models, Biological; D056004:Molecular Dynamics Simulation; D015398:Signal Transduction