Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell-cell contact-dependent signaling using synthetic Notch pathways, and was shown to drive formation of multilayered spheroids by modulating cell-cell adhesion via differential expression of cadherin-family proteins. The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here we build a parametrized computational framework that, given a cellcell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell-cell signaling networks and changes in cell-cell adhesion could be designed in a modular fashion. We then use a set of available in vitro results (that we call the “training set” in analogy to similar pipelines in the machine learning field) to parametrize the computational model with values for adhesion and signaling. We then show that this parametrized model can qualitatively predict experimental results from a “testing set” of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells and even changes to the network architecture. Finally, this parametrized model is used to recommend novel network implementation for the formation of a 4-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell-cell communications and adhesion. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

中文翻译：

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基于接触依赖性信号传导和细胞 - 细胞粘附变化的形态发生遗传回路的描述和设计的参数化计算框架

合成开发是一个新兴的研究领域，它使用合成生物学的工具来设计指导高等真核细胞（如哺乳动物细胞）的细胞模式和形态发生的遗传程序。此类合成遗传程序的一个具体例子是基于使用合成 Notch 途径的细胞-细胞接触依赖性信号传导，并显示通过钙粘蛋白家族蛋白的差异表达调节细胞-细胞粘附来驱动多层球体的形成。这些遗传程序的设计方法依赖于反复试验，这限制了可以探索的可能电路和参数范围的数量。在这里，我们构建了一个参数化计算框架，给定一个细胞通信网络驱动细胞粘附和初始条件的变化作为输入，预测发展轨迹。我们首先建立了一个通用的计算框架，其中可以以模块化方式设计依赖于接触的细胞 - 细胞信号网络和细胞 - 细胞粘附的变化。然后我们使用一组可用的体外结果（我们将其称为“训练集”，类似于机器学习领域中的类似管道）以使用粘附和信号传递值对计算模型进行参数化。然后我们表明，这个参数化模型可以从可用的体外“测试集”中定性地预测实验结果。在粘附组合、初始细胞数量甚至网络结构的变化方面改变遗传网络的数据。最后，该参数化模型用于推荐新颖的网络实现，以形成以前未曾报道过的 4 层结构。我们在这里开发的框架可以作为试验场来识别可通过控制依赖于接触的细胞间通讯和粘附获得的形态的可达空间。此外，我们讨论了如何将模型扩展到包括其他形式的交流或效应器，以用于下一代合成发展轨迹的计算设计。