一种基于生物反应器的架构用于微生物群落组成的体内模型控制和仿真到现实学习控制
A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition
AI总结：
针对微生物群落工业应用中种群稳定共存的难题，提出一种无需基因工程或剧烈环境变化的生物反应器控制架构，通过混合室共培养与储液罐维持慢生菌株，结合模型控制与仿真到现实学习控制，实现双菌株群落密度和组成的精确鲁棒调节。
摘要：
微生物群落相较于单培养在生物生产中具有显著的生物技术优势。然而，由于缺乏可扩展的架构来确保种群间的稳定共存，其工业应用受到阻碍。现有策略依赖于基因修饰（会增加代谢负荷）或环境变化（可能降低产量）。我们提出一种多功能控制架构，无需基因工程或剧烈环境变化即可调节双菌株群落的密度和组成。该基于生物反应器的控制架构包含一个混合室（两菌株共培养）和一个维持生长较慢菌株的储液罐。我们为两个腔室开发了基于模型的控制和仿真到现实学习控制器。该控制架构在双菌株大肠杆菌群落中进行了体内验证，实现了群落密度和组成的精确且鲁棒的调节，包括跟踪时变参考值和从扰动中恢复。
英文摘要：
Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectures to ensure stable coexistence between populations. Existing strategies rely on genetic modifications, which impose metabolic load, or environmental changes, which can reduce production. We present a versatile control architecture to regulate density and composition of a two-strain consortium without genetic engineering or drastic environmental changes. Our bioreactor-based control architecture comprises a mixing chamber where both strains are co-cultured and a reservoir sustaining the slower-growing strain. For both chambers we develop model-based and sim-to-real learning controllers. The control architecture is then validated in vivo on a two-strain Escherichia coli consortium, achieving precise and robust regulation of consortium density and composition, including tracking of time-varying references and recovery from perturbations.
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