Yong Chentao, Gyorgy Andras
Department of Chemical and Biological Engineering, New York University, New York, NY 10003, USA.
Department of Electrical and Computer Engineering, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates.
Life (Basel). 2021 Mar 24;11(4):271. doi: 10.3390/life11040271.
While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.
虽然合成生物学的愿景是以合理的方式创建复杂的遗传系统,但由于模块的上下文依赖动态性,系统级行为往往令人困惑。上下文依赖的一个主要来源是共享资源的可用性有限,这使得不相关组件的行为相互关联。受toggle开关在从控制细胞命运分化到优化细胞性能的遗传电路中普遍作用的启发,我们在此揭示了稀缺资源竞争如何影响其基本动态特性。结合基于零倾线的稳定性分析和基于势景观的鲁棒性分析的机理模型,我们不仅发现了资源竞争的有害影响,还发现了开关的不平衡如何进一步加剧这些影响。虽然一般来说,这两个因素都会削弱开关的性能(通过将动态推向单稳态并增加对噪声的敏感性),但我们也证明,通过策略性优化资源竞争,可以减轻一些不良影响。我们的结果为toggle开关的上下文感知合理设计提供了明确的指导方针,以减少我们对冗长且昂贵的试错过程的依赖,并且可以无缝集成到复杂遗传系统的计算机辅助合成中。