One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.

合成生物学的主要目标之一是开发分子控制器，该控制器可操纵至多为部分已知的给定生化网络的动力学。当集成到较小的隔间（例如活细胞或合成电池）中时，必须对控制器进行校准，以考虑到固有噪声。在这种情况下，文献中提出的生化控制器集中在操纵目标分子种类的均值（第一矩）和减小方差（第二矩）上。但是，许多关键的生化过程是通过高阶矩实现的，尤其是概率分布模式（最大值）的数量和配置。为了弥合差距，我们提出了随机变体控制器可以在适当的时标间隔下将目标分子种类的概率分布变形为预定义的形式。可以在较低的分辨率下执行变形，从而可以实现所需的多模态/多稳定性，而在较高的分辨率下，可以实现任意的概率分布。严格建立控制器的属性，例如鲁棒性和收敛性，并在各种示例中进行演示。还提出了一种随机变质器实验实现的蓝图。