Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA; Synthetic Biology Center, MIT, Cambridge, MA 02139, USA.
Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA.
Cell Syst. 2017 Jan 25;4(1):109-120.e11. doi: 10.1016/j.cels.2016.12.001. Epub 2017 Jan 5.
To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cell's phenotype. In practice, reprogramming is often performed by constant overexpression of specific transcription factors (TFs). This process can be unreliable and inefficient. Here, we address this problem by introducing a new approach to reprogramming based on mathematical analysis. We demonstrate that reprogramming GRNs using constant overexpression may not succeed in general. Instead, we propose an alternative reprogramming strategy: a synthetic genetic feedback controller that dynamically steers the concentration of a GRN's key TFs to any desired value. The controller works by adjusting TF expression based on the discrepancy between desired and actual TF concentrations. Theory predicts that this reprogramming strategy is guaranteed to succeed, and its performance is independent of the GRN's structure and parameters, provided that feedback gain is sufficiently high. As a case study, we apply the controller to a model of induced pluripotency in stem cells.
为了人工重编程细胞命运，实验人员操纵维持细胞表型的基因调控网络（GRN）。在实践中，重编程通常通过特定转录因子（TF）的持续过表达来完成。这个过程可能不可靠且效率低下。在这里，我们通过引入一种基于数学分析的新方法来解决这个问题。我们证明，使用持续过表达来重编程 GRN 一般可能不会成功。相反，我们提出了一种替代的重编程策略：一种合成遗传反馈控制器，它可以动态地将 GRN 的关键 TF 浓度引导到任何所需的值。该控制器通过根据所需和实际 TF 浓度之间的差异来调整 TF 表达来工作。理论预测，这种重编程策略是有保证成功的，并且其性能独立于 GRN 的结构和参数，只要反馈增益足够高。作为一个案例研究，我们将控制器应用于干细胞中的诱导多能性模型。
