Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
The Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel.
J Phys Chem B. 2023 Jul 13;127(27):6113-6124. doi: 10.1021/acs.jpcb.3c01376. Epub 2023 Jul 4.
Many biological systems rely on the ability to self-assemble target structures from different molecular building blocks using nonequilibrium drives, stemming, for example, from chemical potential gradients. The complex interactions between the different components give rise to a rugged energy landscape with a plethora of local minima on the dynamic pathway to the target assembly. Exploring a toy physical model of multicomponents nonequilibrium self-assembly, we demonstrate that a segmented description of the system dynamics can be used to provide predictions of the first assembly times. We show that for a wide range of values of the nonequilibrium drive, a log-normal distribution emerges for the first assembly time statistics. Based on data segmentation by a Bayesian estimator of abrupt changes (BEAST), we further present a general data-based algorithmic scheme, namely, the stochastic landscape method (SLM), for assembly time predictions. We demonstrate that this scheme can be implemented for the first assembly time forecast during a nonequilibrium self-assembly process, with improved prediction power compared to a naïve guess based on the mean remaining time to the first assembly. Our results can be used to establish a general quantitative framework for nonequilibrium systems and to improve control protocols of nonequilibrium self-assembly processes.
许多生物系统依赖于使用非平衡驱动力从不同的分子构建块自组装目标结构的能力，例如源自化学势梯度。不同组件之间的复杂相互作用导致了一个崎岖的能量景观，在动态路径上有大量的局部极小值到达目标组装。通过探索多组分非平衡自组装的玩具物理模型，我们证明了系统动力学的分段描述可用于提供首次组装时间的预测。我们表明，对于非平衡驱动力的广泛值，首次组装时间统计数据呈现对数正态分布。基于贝叶斯突变估计器（BEAST）的突发数据分段，我们进一步提出了一种通用基于数据的算法方案，即随机景观方法（SLM），用于组装时间预测。我们证明，该方案可以用于非平衡自组装过程中的首次组装时间预测，与基于首次组装的剩余时间的平均的简单猜测相比，具有改进的预测能力。我们的结果可用于为非平衡系统建立通用的定量框架，并改进非平衡自组装过程的控制协议。
