Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications
Kangsan Kim, Minjeong Kang, Sang-Hyeok Cho, Eojin Yoo, Ui-Gi Kim, Suhyung Cho, Bernhard Ø. Palsson, Byung‐Kwan Cho
Abstract
metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.