Exact results for noise power spectra in linear biochemical reaction networks.
We present a simple method for determining the exact noise power spectra and related statistical properties for linear chemical reaction networks. The method is applied to reaction networks which are representative of biochemical processes such as gene expression. We find, for example, that a post-translational modification reaction can reduce the noise associated with gene expression. Our results also indicate how to coarse grain networks by the elimination of fast reactions. In this context we have discovered a breakdown of the sum rule which relates the noise power spectrum to the total noise. The breakdown can be quantified by a sum rule deficit, which is found to be universal, and can be attributed to the high-frequency noise in the fast reactions.
Warren
Patrick B
PB
Unilever R&D Port Sunlight, Bebington, Wirral CH63 3JW, United Kingdom. patrick.warren@unilever.com
Tănase-Nicola
Sorin
S
ten Wolde
Pieter Rein
PR
D015964:Gene Expression Regulation, Bacterial; D016014:Linear Models; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D011499:Protein Processing, Post-Translational; D013057:Spectrum Analysis