Adjusting Phenotypes by Noise Control

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.基因完全一致的细胞可表现出表型异质性。此类现象往往由随机事件引发，这些随机事件源自基因表达与其他细胞外在生化过程中的内在随机性。从工程视角出发，学界已开展诸多理论与实验研究，通过扰动或替换基因网络组分来调控噪声水平。然而，由于反应网络中噪声传播的数学结构极为复杂难解，目前仍缺乏系统的噪声调控方法。本研究提出一种基于线性噪声近似（linear noise approximation）的数值分析方法，通过量化噪声特性的参数敏感性实现噪声调控。该分析方法可便捷应用于多种噪声调控场景与不同类型的系统：例如，我们能够实现均值与噪声水平的正交调控，还可对含噪声振荡等系统动力学行为进行调控。作为示例，我们将该方法应用于人类免疫缺陷病毒（HIV, Human Immunodeficiency Virus）、酵母基因表达系统以及代谢网络，并将振荡信号调控方法应用于DNA损伤引发的p53振荡过程。此外，我们证实，通过引入外在噪声与反馈机制，可提升正交调控的效率。本研究提出的噪声调控分析方法可应用于所有属于连续时间马尔可夫系统（continuous time Markovian systems）的随机模型，涵盖生物与化学反应系统，乃至计算机网络与社交网络。我们期望本研究所提出的分析方法能够成为设计与调控人工合成基因网络的实用工具。