Plesa Tomislav, Stan Guy-Bart, Ouldridge Thomas E, Bae Wooli
Department of Bioengineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK.
J R Soc Interface. 2021 Apr;18(177):20200985. doi: 10.1098/rsif.2020.0985. Epub 2021 Apr 14.
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 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，该控制器能够操纵一个最多只有部分已知的给定生化网络的动态变化。当集成到更小的隔室中，如活细胞或合成细胞时，控制器必须进行校准以考虑内在噪声。在这种情况下，文献中提出的生化控制器主要集中于操纵目标分子物种的均值（一阶矩）并降低其方差（二阶矩）。然而，许多关键的生化过程是通过高阶矩实现的，特别是概率分布模式（最大值）的数量和配置。为了弥合这一差距，我们提出了一种控制器，在适当的时间尺度分离条件下，该控制器能够将目标分子物种的概率分布转变为预定义的形式。这种转变可以在较低分辨率下进行，从而实现所需的多模态/多稳定性；也可以在较高分辨率下进行，从而实现任意概率分布。我们严格确立了该控制器的诸如稳健性和收敛性等特性，并在各种示例中进行了展示。此外，还提出了一个随机变形器的实验实施方案蓝图。
