TY - JOUR
T1 - Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction
AU - Lv, Yongkun
AU - Qian, Shuai
AU - Du, Guocheng
AU - Chen, Jian
AU - Zhou, Jingwen
AU - Xu, Peng
N1 - Publisher Copyright:
© 2019 International Metabolic Engineering Society
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Dynamic population control
KW - Genetic circuits
KW - Growth fitness
KW - Metabolic engineering
KW - Synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=85063669400&partnerID=8YFLogxK
U2 - 10.1016/j.ymben.2019.03.009
DO - 10.1016/j.ymben.2019.03.009
M3 - 文献综述
C2 - 30940507
AN - SCOPUS:85063669400
SN - 1096-7176
VL - 54
SP - 109
EP - 116
JO - Metabolic Engineering
JF - Metabolic Engineering
ER -