Engineering of next-generation synthetic biology tools for biological applications

合成生物学通过设计具有特定功能的生物系统，可以解决许多社会需求，无论是在医学、环境修复还是在化学品的绿色生产方面。然而，在合成生物学能够有效地解决这些挑战之前，基础工作仍有待完成。例如，虽然由表征良好的元件制成的合成电路已经能够实现各种功能，但它们通常在精度、稳健性和稳定性方面都比天然电路低得多。我的研究项目的长期目标是设计适合于有影响力的应用的下一代合成基因电路，并将它们作为模型和工具来学习更多的生物学知识。最近，我们成功地设计出了迄今为止最精确、最坚固的合成电路。这是通过使用定量方法和微流体装置实现的，该装置能够在精确控制的生长条件下精确表征数百代数千个单个细菌的动力学。然而，这种精度需要表达大量的蛋白质，这可能会消耗稀缺的细胞资源，给细胞带来代谢负担。这种负担使得以前的合成电路在工业环境中无法使用，因为自然选择会立即破坏这种电路。在本研究计划中，我们将推广我们之前的方法，以生成具有特定属性的广泛合成振荡器（振荡器工具箱，Obj. 1）：精度，鲁棒性，周期，振幅，代谢负担等。这些电路的数量及其特定特性的范围将比目前可用的少数合成振荡器大几个数量级。这些工具箱将使我们能够研究细胞如何在其蛋白质水平的动态中编码信息，并使诸如动态药物传递（即药物水平的时间变化）等应用成为可能。我们还将通过测量朊病毒蛋白的记忆特性，将其开发为超稳定的合成记忆元素，从而为合成生物学家工具箱增加新的部分（Obj. 2）。最近发现，朊病毒蛋白作为一种非基因遗传形式在生命的各个领域都具有功能性作用，在我们的微流体装置中测量它们的动态特性将使我们能够测试关于目前未知的朊病毒形成和丢失机制的假设。最后，我们将使用最近开发的负荷传感器来优化我们的合成电路，使其精确，同时对其细胞宿主（Obj. 3）承担低负荷，从而在有影响力的应用中开放其使用。这些目标是我们实现长期目标的第一步，为动态给药等技术提供基础，同时教会我们生物学知识，例如理解基于朊病毒的信息传递机制。

Synthetic biology, by engineering biological systems for specific functions, could address many societal needs, whether in medicine, in environmental remediation, or in the green production of chemicals. However, foundational work remains to be done before synthetic biology can effectively address these challenges. For example, while synthetic circuits made of well-characterized components have been able to achieve a variety of functions, they generally have done so at a much lower precision, robustness, and stability than their natural counterparts. The long-term goals of my research program are to engineer next-generation synthetic gene circuits suitable for impactful applications, and to use them as models and tools to learn more about biology. Recently, we succeeded in engineering by far the most precise and robust synthetic circuit to date. This was achieved by using a quantitative approach and a microfluidic device enabling precise characterization of the dynamics of thousands of individual bacteria for hundreds of generations under precisely controlled growth conditions. However, this precision required expressing high number of proteins, which can impose a metabolic burden on the cells by using up scarce cellular resources. Such burden has made previous synthetic circuits unusable in industrial contexts, because natural selection immediately breaks such circuits. In this research proposal, we will generalize our previous approach to generate a wide range of synthetic oscillators with specific properties (toolbox of oscillators, Obj. 1): precision, robustness, period, amplitude, metabolic burden, etc. The number of these circuits and the range of their specific properties will be orders of magnitude greater than the few synthetic oscillators currently available. These toolboxes will enable us to study how cells can encode information in the dynamics of their protein levels and enable applications such as dynamic drug delivery (i.e. temporal variation of the level of drugs). We will also add new parts to the synthetic biologist toolkit, by measuring the memory properties of prion proteins in order to develop them as ultra-stable synthetic memory elements (Obj. 2). Prion proteins have recently been found to have a functional role across domains of life as a form of non-genetic inheritance, and measuring their dynamic properties in our microfluidic device will enable us to test hypotheses about the currently unknown mechanisms of prion formation and loss. Finally, we will use a recently developed burden sensor to optimize our synthetic circuits to be precise while carrying a low burden on their cellular host (Obj. 3), opening up their use in impactful applications. These objectives represent the first step towards our long-term goals, by providing the foundation for technologies such as dynamical drug delivery while teaching us about biology, for example by understanding mechanisms of prion-based information transfer.