Agrawal Deepak K, Tang Xun, Westbrook Alexandra, Marshall Ryan, Maxwell Colin S, Lucks Julius, Noireaux Vincent, Beisel Chase L, Dunlop Mary J, Franco Elisa
Biomedical Engineering Department , Boston University , Boston , Massachusetts 02215 , United States.
Department of Mechanical Engineering , University of California at Riverside , Riverside , California 92521 , United States.
ACS Synth Biol. 2018 May 18;7(5):1219-1228. doi: 10.1021/acssynbio.8b00040. Epub 2018 May 8.
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.
反馈使生物系统能够通过感知和处理环境变化，即使在存在不确定性的情况下，也能精确且可靠地控制基因表达。受自然结构的启发，合成生物学家设计了反馈回路来调节动态过程，并提高基因表达的稳健性和可预测性。然而，生物分子控制系统的实验实现仍远未达到电气或机械控制系统通常能实现的性能规格。为了弥补这一差距，我们提出了生物分子控制器的数学模型，该模型能够实现参考跟踪、干扰抑制以及对基因表达时间响应的调节。这些控制器采用RNA转录调节因子来实现闭环控制，其中反馈是通过分子隔离引入的。对模型的敏感性分析使我们能够确定哪些参数会影响目标基因表达过程的瞬态和稳态响应，以及哪些生物学上合理的参数值能够实现完美的参考跟踪。我们使用典型的控制理论指标来量化性能，以表征响应特性，并为实际应用提供明确的选择指南。我们的结果表明，RNA调节因子非常适合构建用于基因表达的稳健且精确的反馈控制器。此外，我们的方法还展示了几种用于评估生物分子反馈控制系统性能的定量方法。
