Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell–Cell Adhesion

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 the formation of multilayered spheroids by modulating cell–cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). 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 parameterized computational framework that, given a cell–cell 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 used a set of available in vitro results (that we call the “training set” in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized 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 parameterized model is used to recommend novel network implementation for the formation of a four-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 with these molecular tools and in this cellular system. 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.合成发育学（Synthetic development）是一门新兴研究领域，其借助合成生物学（synthetic biology）工具，设计可指导高等真核细胞（higher eukaryotic cells，如哺乳动物细胞（mammalian cells））的细胞模式形成与形态发生（cellular patterning and morphogenesis）的遗传程序。此类合成遗传程序的典型案例之一，是基于合成Notch通路（synthetic Notch pathways）的细胞-细胞接触依赖性信号传导（cell–cell contact-dependent signaling），该程序已被证实可通过调控钙粘蛋白（cadherin）家族蛋白的差异表达改变细胞-细胞黏附（cell–cell adhesion），进而在小鼠成纤维细胞系（mouse fibroblast cell line, L929）中驱动多层球状体的形成。此前，这类遗传程序的设计依赖试错法（trial and error），这极大限制了可探索的电路类型与参数范围。本研究构建了一款参数化计算框架（parameterized computational framework），该框架以驱动细胞黏附变化的细胞-细胞通信网络（cell–cell communication network）及初始条件作为输入，可预测发育轨迹（developmental trajectories）。我们首先搭建了通用计算框架，能够以模块化方式设计接触依赖性细胞间信号网络与细胞黏附变化。随后，我们借助一组已公开的体外实验结果（in vitro results，类比机器学习（machine learning）领域的同类研究流程，我们将其命名为"训练集"（training set）），为该计算模型标定了黏附与信号传导相关的参数值（parameter values）。后续验证结果表明，该参数化模型可定性预测（qualitatively predict）来自"测试集"（testing set）的已有体外实验数据——该测试集的实验变量涵盖黏附组合、初始细胞数量，甚至网络架构（network architecture）的修改。最后，我们利用该参数化模型，为此前尚未见报道的四层结构（four-layered structure）的构建提出了全新的网络实现方案。本研究开发的框架可作为测试平台（testing ground），用于识别通过这类分子工具及该细胞系统中接触依赖性细胞间通信与黏附调控，所能获得的形态学可达空间（reachable space of morphologies）。此外，我们还探讨了如何扩展该模型，以纳入其他形式的通信或效应因子（effectors），从而实现下一代合成发育轨迹（synthetic developmental trajectories）的计算设计。