Bromig Lukas, Weuster-Botz Dirk
Chair of Biochemical Engineering, Technical University of Munich, Boltzmannstraße 15, D-85748 Garching, Germany.
Microorganisms. 2023 Jan 20;11(2):275. doi: 10.3390/microorganisms11020275.
Adaptive laboratory evolution (ALE) is a valuable complementary tool for modern strain development. Insights from ALE experiments enable the improvement of microbial cell factories regarding the growth rate and substrate utilization, among others. Most ALE experiments are conducted by serial passaging, a method that involves large amounts of repetitive manual labor and comes with inherent experimental design flaws. The acquisition of meaningful and reliable process data is a burdensome task and is often undervalued and neglected, but also unfeasible in shake flask experiments due to technical limitations. Some of these limitations are alleviated by emerging automated ALE methods on the μL and mL scale. A novel approach to conducting ALE experiments is described that is faster and more efficient than previously used methods. The conventional shake flask approach was translated to a parallelized, L scale stirred-tank bioreactor system that runs controlled, automated, repeated batch processes. The method was validated with a growth optimization experiment of K-12 MG1655 grown with glycerol minimal media as a benchmark. Off-gas analysis enables the continuous estimation of the biomass concentration and growth rate using a black-box model based on first principles (soft sensor). The proposed method led to the same stable growth rates of with the non-native carbon source glycerol 9.4 times faster than the traditional manual approach with serial passaging in uncontrolled shake flasks and 3.6 times faster than an automated approach on the mL scale. Furthermore, it is shown that the cumulative number of cell divisions (CCD) alone is not a suitable timescale for measuring and comparing evolutionary progress.
适应性实验室进化（ALE）是现代菌株开发中一种有价值的补充工具。ALE实验的见解有助于改进微生物细胞工厂的生长速率和底物利用等方面。大多数ALE实验通过连续传代进行，这种方法涉及大量重复性体力劳动，且存在固有的实验设计缺陷。获取有意义且可靠的过程数据是一项繁重的任务，常常被低估和忽视，而且由于技术限制在摇瓶实验中也不可行。一些限制通过新兴的微升和毫升规模的自动化ALE方法得到缓解。本文描述了一种进行ALE实验的新方法，该方法比以前使用的方法更快、更高效。传统的摇瓶方法被转化为一个平行的、升规模的搅拌罐生物反应器系统，该系统运行受控、自动化的重复分批过程。该方法通过以甘油基本培养基培养K - 12 MG1655的生长优化实验作为基准进行了验证。尾气分析能够使用基于第一原理的黑箱模型（软传感器）连续估计生物量浓度和生长速率。所提出的方法在使用非天然碳源甘油时实现了相同的稳定生长速率，比在不受控摇瓶中进行连续传代的传统手动方法快9.4倍，比毫升规模的自动化方法快3.6倍。此外，研究表明仅细胞分裂累积数（CCD）不是测量和比较进化进程的合适时间尺度。
