Using single-cell models to predict the functionality of synthetic circuits at the population scale

Proc Natl Acad Sci U S A. 2022 Mar 15;119(11):e2114438119. doi: 10.1073/pnas.2114438119. Epub 2022 Mar 10.

Abstract

SignificanceAt the single-cell level, biochemical processes are inherently stochastic. For many natural systems, the resulting cell-to-cell variability is exploited by microbial populations. In synthetic biology, however, the interplay of cell-to-cell variability and population processes such as selection or growth often leads to circuits not functioning as predicted by simple models. Here we show how multiscale stochastic kinetic models that simultaneously track single-cell and population processes can be obtained based on an augmentation of the chemical master equation. These models enable us to quantitatively predict complex population dynamics of a yeast optogenetic differentiation system from a specification of the circuit's components and to demonstrate how cell-to-cell variability can be exploited to purposefully create unintuitive circuit functionality.

Keywords: chemical master equation; composability; optogenetics; population dynamics; synthetic differentiation circuits.

MeSH terms

  • Biological Variation, Population*
  • Gene Regulatory Networks*
  • Optogenetics* / methods
  • Saccharomyces cerevisiae* / genetics
  • Saccharomyces cerevisiae* / growth & development
  • Single-Cell Analysis* / methods
  • Stochastic Processes
  • Synthetic Biology