Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

无细胞蛋白质合成已被广泛用作合成遗传网络设计的“面包板”。然而，由于严重缺乏模块化，遗传网络的正向工程仍然具有挑战性。在这里，我们展示了最佳实验设计和微流体学的结合如何使我们能够设计动态无细胞基因表达实验，为后续的非线性模型识别提供最大的信息内容。重要的是，我们揭示了将这种方法应用于共享共同元素的遗传电路库，进一步增加了数据的信息内容，从而提高了模型参数的准确性。为了显示模型参数的模块化，我们设计了一个脉冲解码器和双稳态开关，并定性和定量地预测它们的行为。最后，我们更新了参数数据库并指出网络拓扑会影响参数估计的准确性。利用我们的方法为我们提供了更准确的模型参数，这是复杂遗传网络正向工程的必要条件。