We present an embedded feedback control strategy to regulate the density of a microbial population, that is, the number of cells into a given environment, allowing cells to self-regulate their growth rate so as to reach a desired density at steady state. We consider a static culture condition, where cells are provided with a limited amount of space and nutrients. The control strategy is built using a tunable expression system (TES), which controls the production of a growth inhibitor protein, complemented with a quorum sensing mechanism for the sensing of the population density. We show via a simplified population-level model that the TES endows the control system with additional flexibility by allowing the set-point to be changed online. Finally, we validate the effectiveness of the proposed control strategy by means of realistic in silico experiments conducted in BSim, an agent-based simulator explicitly designed to simulate bacterial populations, and we test the robustness of our design to disturbances and parameters' variations due, for instance, to cell-to-cell variability.
Embedded control of cell growth using tunable genetic systems
Salzano D.;di Bernardo M.
2023-01-01
Abstract
We present an embedded feedback control strategy to regulate the density of a microbial population, that is, the number of cells into a given environment, allowing cells to self-regulate their growth rate so as to reach a desired density at steady state. We consider a static culture condition, where cells are provided with a limited amount of space and nutrients. The control strategy is built using a tunable expression system (TES), which controls the production of a growth inhibitor protein, complemented with a quorum sensing mechanism for the sensing of the population density. We show via a simplified population-level model that the TES endows the control system with additional flexibility by allowing the set-point to be changed online. Finally, we validate the effectiveness of the proposed control strategy by means of realistic in silico experiments conducted in BSim, an agent-based simulator explicitly designed to simulate bacterial populations, and we test the robustness of our design to disturbances and parameters' variations due, for instance, to cell-to-cell variability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
