title: An effective method for computing the noise in biochemical networks.
abstract: We present a simple yet effective method, which is based on power series expansion, for computing exact binomial moments that can be in turn used to compute steady-state probability distributions as well as the noise in linear or nonlinear biochemical reaction networks. When the method is applied to representative reaction networks such as the ON-OFF models of gene expression, gene models of promoter progression, gene auto-regulatory models, and common signaling motifs, the exact formulae for computing the intensities of noise in the species of interest or steady-state distributions are analytically given. Interestingly, we find that positive (negative) feedback does not enlarge (reduce) noise as claimed in previous works but has a counter-intuitive effect and that the multi-OFF (or ON) mechanism always attenuates the noise in contrast to the common ON-OFF mechanism and can modulate the noise to the lowest level independently of the mRNA mean. Except for its power in deriving analytical expressions for distributions and noise, our method is programmable and has apparent advantages in reducing computational cost.
authors: School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors: Jiajun
authors: J
authors: Zhang
authors: Qing
authors: Q
authors: Nie
authors: Miao
authors: M
authors: He
authors: Tianshou
authors: T
authors: Zhou
mesh_terms: D055162:Biocatalysis; D001669:Biochemical Phenomena; D019295:Computational Biology / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D016014:Linear Models; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D011336:Probability; D011401:Promoter Regions, Genetic / Q000235:genetics; D011494:Protein Kinases / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D011494:Protein Kinases