Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Appl Environ Microbiol. 2010 Aug;76(16):5541-6. doi: 10.1128/AEM.00828-10. Epub 2010 Jun 25.
Random searches have been the hallmark of directed evolution and have been extensively employed in the improvement of complex or poorly understood phenotypes such as tolerance to toxic compounds in the context of cellular engineering. While genome-wide mutagenesis followed by selection or screening has been a traditional means of phenotype improvement, the list of experimental methods for cellular engineering based on random searches is rapidly expanding. Adding to the confusion is the element of chance, which lengthens the process and most notably adds to the cost of phenotypic improvement programs. Here we present a method to systematize the effort of finding superior mutants by successively improving random libraries. The method, based on the quantification of phenotypic diversity, is then used to isolate more-robust strains.
随机搜索一直是定向进化的标志，并已广泛应用于复杂或理解不佳的表型的改进，例如在细胞工程中对有毒化合物的耐受。虽然全基因组诱变后进行选择或筛选一直是表型改善的传统手段，但基于随机搜索的细胞工程实验方法的清单正在迅速扩大。增加混乱的是机会元素，这延长了过程，最显著的是增加了表型改善计划的成本。在这里，我们提出了一种通过连续改进随机文库来系统地寻找优良突变体的方法。该方法基于表型多样性的量化，然后用于分离更稳健的菌株。
