How Cell-to-Cell Heterogeneity and Scarce Resources Shape the Population-Level Stability Profile of Toggle Switches

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The lack of modularity in synthetic biology presents one of the major bottlenecks in the scalability of complex gene circuits. One source of this context-dependent behavior is the scarcity of shared transcriptional and translational resources. To overcome this issue, predictive computational tools must account for the resulting competition phenomenon both when studying individual cells and at the population-level considering cell-to-cell heterogeneity. Since toggle switches are one of the most widely used genetic modules, here we focus on how shared resources affect the stability profile of toggle switches even in the presence of loading from their context. Modeling the parameters of the toggle switch as random variables reveals how cellular context, noise and correlation between key parameters shape the population-level stability distribution. To demonstrate the relevance of our results, we illustrate that detrimental effects of even unknown contexts can be bounded, thus enabling the design of genetic modules that are robust to disturbances due to unknown loading effects.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6622-6627
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
Country/TerritoryFrance
CityNice
Period12/11/1912/13/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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