Abstract
Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks.
Highlights
There have been numerous experiments conducted on a wide range of organisms such as prokaryotic [1,2,3] and eukaryotic [4,5] cells including mammalian cells [6,7], to study gene expression noise
Stochastic sensitivity For the purpose of noise control, we introduce stochastic sensitivities [21] called control coefficients (CCs) similar to the control coefficients in metabolic control analysis (MCA) [27,28,29]
CCs have been widely used in MCA for metabolic networks in the deterministic framework [27,28,29]
Summary
There have been numerous experiments conducted on a wide range of organisms such as prokaryotic [1,2,3] and eukaryotic [4,5] cells including mammalian cells [6,7], to study gene expression noise. Stochastic gene expression can lead to significant phenotypic cell-to-cell variation. The measured noise is often explained by mathematical models [1,2,3,4,5,6,7], a systematic analysis on parametric control of noise has been lacking. This is attributed to the fact that noise propagation through pathway connections generates correlations between the pathway species [17], which make analysis difficult. Complicated feedback and feedforward structures in real biological networks hamper modular noise analysis
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