Noise-attenuation in artificial genetic networks

Dynamics of gene expressions is quite noisy because of intrinsic noise originated from the smallness of the number of related molecules. Noise-attenuation and system-stabilization in artificial genetic networks are important problems for various applications in engineering and medical areas. In this study, we propose a plausible method to control fluctuation in artificial genetic networks. The main idea is an addition of the molecules designed to specifically bind to synthesized proteins with fast equilibrium. This fast interaction between those molecules and the proteins absorbs and compensates for the variation from the average. We demonstrate that, by this method, we can stabilize not only single gene expression, but also system dynamics with multi-stable states.