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 basics of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerisation, a well-known source of nonlinear dynamics. Specifically, our approach focusses on modelling co-translational dimerisation: 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 dimerisation. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerisation 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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模拟共翻译二聚化以实现合成生物学中的可编程非线性

非线性在天然和合成生物网络的性能中都起着基本作用。生命微生物系统中的关键功能图案（如双稳态或振荡的出现）依赖于非线性分子动力学。尽管它的核心重要性，但非线性的合理设计仍然是未解决的挑战。这在很大程度上是由于缺乏数学模型来说明非线性的机械基础。我们为基因调控电路引入了一个模型，该模型可显式模拟蛋白质二聚化，这是众所周知的非线性动力学来源。具体而言，我们的方法侧重于模拟共翻译二聚化：翻译过程中（而不是翻译后）蛋白质二聚体的形成。这与普遍的假设相反，该普遍的假设是，二聚体的生成仅在自由扩散的单体之间才是可行的（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微调需求非线性的方法。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，可用于调节体内二聚化动力学。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，可用于调整体内的二聚化动力学。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，可用于调整体内的二聚化动力学。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。