Shared antithetic integral control for dynamic cell populations

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

  • Lorenzo Duso - , Max Planck Institute of Molecular Cell Biology and Genetics (Author)
  • Tommaso Bianucci - , Faculty of Computer Sciences, Clusters of Excellence PoL: Physics of Life, Max Planck Institute of Molecular Cell Biology and Genetics, TUD Dresden University of Technology (Author)
  • Christoph Zechner - , Max Planck Institute of Molecular Cell Biology and Genetics, TUD Dresden University of Technology, Clusters of Excellence PoL: Physics of Life (Author)

Abstract

Engineering reliable synthetic circuits in living organisms is very challenging because of molecular fluctuations, cell-to-cell variability and metabolic burden, for instance. Recently, the antithetic integral controller (AIC) has been proposed as an effective strategy to design robust synthetic circuits in living cells. In its canonical form, the AIC acts at the single-cell level to regulate the abundance of a certain intracellular component to a prescribed set-point. In this work, we propose a variant of the AIC that allows the control of collective properties of a dynamic cell population, such as the cell number or the total amount of protein expressed across the population. The resulting controller-which we term shared AIC (sAIC)-uses a single controller network that acts on all cells simultaneously through a shared environment. We describe the sAIC mathematically using a stochastic multiscale formalism, which accounts for noisy cell-internal dynamics as well as cell division and death events. We demonstrate the effectiveness of the sAIC approach using two simulation-based case studies.

Details

Original languageEnglish
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2053-2058
Number of pages6
ISBN (electronic)9781665436595
Publication statusPublished - 2021
Peer-reviewedYes

Publication series

SeriesProceedings of the IEEE Conference on Decision and Control
Volume2021-December
ISSN0743-1546

Conference

Title60th IEEE Conference on Decision and Control, CDC 2021
Duration13 - 17 December 2021
CityAustin
CountryUnited States of America