Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction
Loading...
Author/Creator
Author/Creator ORCID
Date
2019-03-30
Type of Work
Department
Program
Citation of Original Publication
Yongkun Lv , Shuai Qian , Guocheng Du , Jian Chen , Jingwen Zhou , Peng Xu, Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction, Metabolic Engineering , Volume 54, July 2019, Pages 109-116, https://doi.org/10.1016/j.ymben.2019.03.009
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Access to this item will begin on July 30, 2020.
Attribution-NonCommercial-NoDerivs 3.0 United States
Access to this item will begin on July 30, 2020.
Attribution-NonCommercial-NoDerivs 3.0 United States
Abstract
Metabolic engineering entails target modification of cell metabolism to maximize cell's production potential. Due to the complexity of cell metabolism, feedback genetic circuits have emerged as basic tools to combat metabolic heterogeneity, enhance microbial cooperation as well as boost cell's productivity. This is generally achieved by applying social reward-punishment rules to eliminate cheaters and reward winners in a mixed cell population. With metabolite-responsive transcriptional factors to rewire gene expression, metabolic engineers are well-positioned to integrate feedback genetic circuits with growth fitness and achieve dynamic population control. Towards this goal, we argue that feedback genetic circuits and microbial interactions will be a golden mine for future metabolic engineering. We will summarize the design principles of engineering burden-driven feedback control to combat metabolic stress, implementing population quality control to eliminate cheater cell, applying product addiction to reward productive cell, as well as layering dual dynamic regulation to decouple cell growth from product formation. Collectively, these strategies will be useful to improve community-level cellular performance. Encoding such decision-marking functions and reprogramming cellular logics at population level will enable metabolic engineers to deliver robust cell factories and pave the way for intelligent bioproduction. We envision that various cellular regulation mechanisms and genetic/metabolic circuits could be exploited to achieve self-adaptive or autonomous metabolic function for diverse biotechnological and medical applications. Applying these design rules may offer us a genetic solution beyond bioprocess engineering strategies to further improve the cost-competitiveness of industrial fermentation.