Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression

Deepak K. Agrawal, Xun Tang, Alexandra Westbrook, Ryan Marshall, Colin S. Maxwell, Julius Lucks, Vincent Noireaux, Chase L. Beisel, Mary J. Dunlop, Elisa Franco

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Feedback allows biological systems to control gene expression precisely and reliably, even in the presence of uncertainty, by sensing and processing environmental changes. Taking inspiration from natural architectures, synthetic biologists have engineered feedback loops to tune the dynamics and improve the robustness and predictability of gene expression. However, experimental implementations of biomolecular control systems are still far from satisfying performance specifications typically achieved by electrical or mechanical control systems. To address this gap, we present mathematical models of biomolecular controllers that enable reference tracking, disturbance rejection, and tuning of the temporal response of gene expression. These controllers employ RNA transcriptional regulators to achieve closed loop control where feedback is introduced via molecular sequestration. Sensitivity analysis of the models allows us to identify which parameters influence the transient and steady state response of a target gene expression process, as well as which biologically plausible parameter values enable perfect reference tracking. We quantify performance using typical control theory metrics to characterize response properties and provide clear selection guidelines for practical applications. Our results indicate that RNA regulators are well-suited for building robust and precise feedback controllers for gene expression. Additionally, our approach illustrates several quantitative methods useful for assessing the performance of biomolecular feedback control systems.

Original languageEnglish (US)
Pages (from-to)1219-1228
Number of pages10
JournalACS Synthetic Biology
Issue number5
StatePublished - May 18 2018

Bibliographical note

Publisher Copyright:
Copyright © 2018 American Chemical Society.


  • RNA
  • control
  • feedback
  • gene expression
  • mathematical modeling
  • sensitivity analysis


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