Embedded control of cell growth using tunable genetic systems

Virginia Fusco, Davide Salzano, Davide Fiore, Mario di Bernardo*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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.
Original languageEnglish
Number of pages15
JournalInternational Journal of Robust and Nonlinear Control
DOIs
Publication statusPublished - 7 Jan 2022

Bibliographical note

Funding Information:
Mario di Bernardo, Davide Fiore, and Davide Salzano wish to acknowledge support from the European Union's Horizon 2020 research and innovation programme under grant agreement No 766840 (COSY‐BIO). Davide Fiore also wishes to acknowledge support by the research grant “BIOMASS” from the University of Naples Federico II–“Finanziamento della Ricerca di Ateneo (FRA)–Linea B”.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Keywords

  • cell population control
  • cybergenetics
  • embedded control
  • gene regulatory networks
  • syntheticbiology

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