Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

以依赖于历史的方式运行的遗传程序在自然界中无处不在，并控制着发育和分化等复杂的过程。系统地、可预测地编码此类程序的能力将促进具有丰富信号处理能力的合成生物体和生态系统的工程。在这里，我们通过在细胞群中分配计算劳动来实现强大的、可扩展的历史依赖程序。我们的设计基于标准化重组酶驱动的 DNA 支架，根据输入的出现顺序表达不同的基因。这些多细胞计算系统是高度模块化的，不需要细胞间的通信通道，并且任何程序都可以通过包含良好表征的逻辑支架的菌株的差异组合来构建。我们开发了自动化工作流程，研究人员可以使用它来简化程序设计和优化。我们预计这里提出的依赖于历史的程序将支持许多使用细胞群进行材料工程、生物制造和医疗保健的应用。