Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch

Andrew E. Blanchard, Chen Liao, Ting Lu

Research output: Contribution to journalArticle

Abstract

Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm—integrated modeling of the circuit-host system—for quantitative understanding of engineered gene networks.

Original languageEnglish (US)
Pages (from-to)737-746
Number of pages10
JournalBiophysical journal
Volume114
Issue number3
DOIs
StatePublished - Feb 6 2018

ASJC Scopus subject areas

  • Biophysics

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