Dreyer Kathleen S, Nguyen Anh V, Bora Gauri G, Redus Lauren E, Edelstein Hailey I, Zonnefeld Jocelyn, Anastasia Eleftheria, Dray Kate E, Leonard Joshua N, Mangan Niall M
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States.
bioRxiv. 2025 Aug 23:2025.08.23.671908. doi: 10.1101/2025.08.23.671908.
Genetic programs can direct living systems to perform diverse, pre-specified functions. As the library of parts available for building such programs continues to expand, computation-guided design is increasingly helpful and necessary. Yet key gaps exist for designing programs for use in mammalian cells in particular. Predictive models aid the challenging design process, but iterative simulation and experimentation are intractable for complex functions. Computer-aided design accelerates this process, but existing tools do not yet capture the behavior of mammalian-specific parts and population-level effects needed for mammalian synthetic biologists. To address these needs, we developed a framework for mammalian genetic program computer-aided design. Starting with a user-defined design specification to quantify circuit performance, the framework uses a genetic algorithm to search through possible designs. Circuit space is defined by a library of experimentally characterized parts and dynamical systems models for gene expression in a heterogeneous cell population. We developed this genetic algorithm using a directed graph-based formulation with biologically constrained rules to explore regulatory connections and parts. We evaluated the framework for design problems of varying complexity, including programs we describe as an amplifier, signal conditioner, and pulse generator, demonstrating that the algorithm can successfully find optimal circuit designs. Finally, we experimentally evaluated selected circuits, demonstrating the path from a predicted circuit design to experimental testing and highlighting the importance of characterization for enabling predictive design. Overall, this framework establishes general approaches that can be refined and expanded, accelerating the design and implementation of mammalian genetic programs.
遗传程序能够引导生命系统执行各种预先设定的功能。随着可用于构建此类程序的元件库不断扩展，计算引导设计变得越来越有帮助且必要。然而，尤其在为哺乳动物细胞设计程序方面仍存在关键差距。预测模型有助于应对具有挑战性的设计过程，但对于复杂功能而言，迭代模拟和实验难以处理。计算机辅助设计加速了这一过程，但现有工具尚未捕捉到哺乳动物合成生物学家所需的哺乳动物特异性元件的行为以及群体水平效应。为满足这些需求，我们开发了一个用于哺乳动物遗传程序计算机辅助设计的框架。该框架从用户定义的设计规范开始以量化电路性能，使用遗传算法在可能的设计中进行搜索。电路空间由一个经过实验表征的元件库和用于异质细胞群体中基因表达的动态系统模型定义。我们使用基于有向图的公式和生物学约束规则开发了这种遗传算法，以探索调控连接和元件。我们针对不同复杂度的设计问题评估了该框架，包括我们描述为放大器、信号调节器和脉冲发生器的程序，证明该算法能够成功找到最优电路设计。最后，我们对选定的电路进行了实验评估，展示了从预测电路设计到实验测试的路径，并强调了表征对于实现预测性设计的重要性。总体而言，该框架建立了可改进和扩展的通用方法，加速了哺乳动物遗传程序的设计和实施。
