Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization—a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during—and not after—translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.

中文翻译：

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合成生物学中可编程非线性的共翻译二聚化建模

非线性在天然和合成生物网络的性能中起着重要作用。活微生物系统中的关键功能基序，例如双稳态或振荡的出现，依赖于非线性分子动力学。尽管其核心重要性，非线性的合理设计仍然是一个未解决的挑战。这主要是由于缺乏解释非线性机械基础的数学模型。我们引入了一个基因调控电路模型，该模型明确模拟蛋白质二聚化——众所周知的非线性动力学来源。具体来说，我们的方法侧重于对共翻译二聚化进行建模：在翻译期间而不是在翻译之后形成蛋白质二聚体。这与普遍的假设相反，即二聚体生成仅在自由扩散的单体之间是可行的（即翻译后二聚化）。我们提供了一种通过平衡共翻译与翻译后二聚化的影响来按需微调非线性的方法。此外，我们建议设计规则，例如蛋白质长度或基因之间的物理分离，可用于调整体内二聚化动力学。具有按需非线性动力学的遗传电路的设计、构建和测试将大大提高合成生物系统的可编程性。例如蛋白质长度或基因之间的物理分离，可用于调整体内二聚化动力学。具有按需非线性动力学的遗传电路的设计、构建和测试将大大提高合成生物系统的可编程性。例如蛋白质长度或基因之间的物理分离，可用于调整体内二聚化动力学。具有按需非线性动力学的遗传电路的设计、构建和测试将大大提高合成生物系统的可编程性。