基于数据的相似生物系统聚类与控制
Data-Based Clustering and Control of Similar Biological Systems
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中文总结 AI 辅助
针对传统控制策略在大量异质生物系统控制中计算负担大、可扩展性受限的问题，提出基于相似性的聚类分层主从控制框架，经仿真验证可实现对异质生物系统的可扩展可靠控制。
中文摘要 AI 辅助
基因表达的 cybergenetic 控制可应用于合成生物学、药物开发和生物制造领域。微流控平台支持对大量细胞群体的并行控制，但由此产生的计算负担以及固有的生物异质性限制了传统控制策略的可扩展性。本研究提出一种基于相似性的框架，以降低控制大量动态系统的计算需求。该框架基于现有数据驱动方法，从输入输出数据中量化与控制相关的相似性，在无需明确系统辨识的情况下对具有相似动态特性的系统进行聚类。基于此分组，开发了分层主从控制架构，其中为每个簇设计一个控制器并应用于该簇的所有成员，这显著减少了需要求解的控制问题数量。此外，分析了簇内的闭环行为，并开发了数据驱动条件，确保聚类后的闭环系统保持适定性。通过基因表达动态的仿真验证了所提方法，结果表明基于相似性的分组可实现对异质生物系统的可扩展且可靠的控制。
英文摘要
Cybergenetic control of gene expression enables applications in synthetic biology, drug development, and biomanufacturing. Microfluidic platforms allow the parallel control of large cell populations. However, the resulting computational burden and intrinsic biological heterogeneity limit the scalability of conventional control strategies. In this work, we propose a similarity-based framework to reduce the computational requirement of controlling large numbers of dynamical systems. Building on existing data-driven methods for quantifying control-relevant similarity from input-output data, we cluster systems with similar dynamics without requiring explicit system identification. Based on this grouping, we develop a hierarchical leader-follower control architecture, where a single controller is designed for each cluster and applied to all members. This significantly reduces the number of control problems that need to be solved. Furthermore, we analyse the closed-loop behaviour within clusters and develop data-driven conditions under which the clustered closed-loop systems remain well-posed. The proposed approach is demonstrated in simulations of gene expression dynamics, showing that similarity-based grouping enables scalable and reliable control of heterogeneous biological systems.
发表机构
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
