Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories

Abstract

Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TFbased extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

Perfect adaptation in biology

“…Controlling biological networks that are uncertain with precision and robustness despite noise, external disturbances, and/ or dynamic perturbations opens up lots of opportunities for applications in industrial biotechnology and medical therapy. Early areas of application include biofuels and dynamic pathway regulation for cell factories (Boada et al, 2020), burden mitigation (Huang et al, 2018a), and growth control (Aoki et al, 2019). In medical therapy, RPA circuits may be used to restore homeostasis in the treatment of metabolic diseases, as well as for applications in immunotherapy and precise drug delivery.…”

Gene variant space for biosensor-based dynamic regulation

“…The construction of viable genetic circuits capable of producing significant amounts of naringenin must consider several factors. In [2], the circuit itself considers the possibility of monitoring naringenin production by synthesizing a biosensor that consumes naringenin to provide a control signal. Therefore, there must be a trade-off between the amount of naringenin consumed and the kaempferol produced.…”

“…Since experimental of enzyme parameters is not complete for all known enzyme variants, in this study we intend to sample the design space defined by the parameter ranges of each enzyme and evaluate their dynamic responses. In summary, the object of this study is to assess the effect produced on a regulated system described in [2] by varying the three parameters mentioned above, in order to establish a relationship between enzymatic efficiency (independent of phylogeny) and gene expression efficiency (directly dependent on taxonomic distance).…”

“…This is due to the time-varying and non-homogeneous conditions of these production pathways inside the cell, which are difficult to control by dynamic adjustments. In [2], a biosensor based model was proposed to measure the naringenin production as a target metabolite using bacteria as a host microorganim. The biosensor is based on kaempferol, which is another metabolite used as a proxy of the naringenin level.…”

Trade-offs in biosensor optimization for dynamic pathway engineering

“…Such analyses can be done on the basis of kinetic models based on ordinary differential equations (ODEs). Works in this space have primarily focused on the impact of parameters on metabolic flux using dynamical systems 26,27 , model simulation [28][29][30] , and control theory [31][32][33] . Other works have looked at how various control architectures, i.e.…”