Chuanbo Liu, Yu Fu, Lu Lin, Elliot L. Elson, Jin Wang
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Our system exhibits exceptional sensitivity to light, reacting consistently\nto pulsed light signals, distinguishing it from other photoreceptor-based\npromoter systems that respond to a single light wavelength. To characterize our system, we developed a stochastic model for phytochromes\nthat accounts for photoactivation/deactivation, thermal reversion, and the\ndynamics of the light-activated gene promoter system. To precisely control our system, we determined the rate constants for this\nmodel using an omniscient deep neural network that can directly map rate\nconstant combinations to time-dependent state joint distributions. By adjusting the activation rates through light intensity and degradation\nrates via N-terminal mutagenesis, we illustrate that out optical-controlled\nperturbation can effectively modulate molecular expression level as well as\nnoise. 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引用次数: 0
摘要
噪声在调控细胞和生物体的功能与行为方面起着至关重要的作用。探索噪声的影响是理解基因表达、信号转导以及发育和进化机制等基本生物过程的关键。目前,还缺乏一种全面的方法来量化这些生化系统中细胞噪声的动态行为。在这项研究中,我们利用拟南芥中对光敏感的植物色素B(PhyB)引入了一种光控扰动系统,该系统能以高时空分辨率对噪声进行精确调制。我们的系统对光的敏感度极高,能对脉冲光信号做出一致的反应,这使它有别于其他只对单一波长的光做出反应的基于光感受器的启动子系统。为了描述我们的系统,我们开发了一个植物色素随机模型,该模型考虑了光激活/去激活、热还原以及光激活基因启动子系统的动力学。为了精确控制我们的系统,我们使用全知的深度神经网络确定了该模型的速率常数,该网络可以直接将速率常数组合映射到随时间变化的状态联合分布上。通过光照强度调整激活率,并通过 N 端突变调整降解率,我们证明了光控扰动可以有效调节分子表达水平和噪声。我们的研究结果凸显了采用光控基因扰动系统作为噪声控制刺激源的潜力。这种方法与复杂的深度神经网络的分析能力相结合,可以在广泛的生化反应网络中从观测数据中准确估计速率常数。
Understanding Cellular Noise with Optical Perturbation and Deep Learning
Noise plays a crucial role in the regulation of cellular and organismal
function and behavior. Exploring noise's impact is key to understanding fundamental biological
processes, such as gene expression, signal transduction, and the mechanisms of
development and evolution. Currently, a comprehensive method to quantify dynamical behavior of cellular
noise within these biochemical systems is lacking. In this study, we introduce an optically-controlled perturbation system
utilizing the light-sensitive Phytochrome B (PhyB) from \textit{Arabidopsis
thaliana}, which enables precise noise modulation with high spatial-temporal
resolution. Our system exhibits exceptional sensitivity to light, reacting consistently
to pulsed light signals, distinguishing it from other photoreceptor-based
promoter systems that respond to a single light wavelength. To characterize our system, we developed a stochastic model for phytochromes
that accounts for photoactivation/deactivation, thermal reversion, and the
dynamics of the light-activated gene promoter system. To precisely control our system, we determined the rate constants for this
model using an omniscient deep neural network that can directly map rate
constant combinations to time-dependent state joint distributions. By adjusting the activation rates through light intensity and degradation
rates via N-terminal mutagenesis, we illustrate that out optical-controlled
perturbation can effectively modulate molecular expression level as well as
noise. Our results highlight the potential of employing an optically-controlled gene
perturbation system as a noise-controlled stimulus source. This approach, when combined with the analytical capabilities of a
sophisticated deep neural network, enables the accurate estimation of rate
constants from observational data in a broad range of biochemical reaction
networks.