合成生物学中控制器选择的实验设计
Experimental Design for Controller Selection in Synthetic Biology
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- Boston University（波士顿大学）
- Massachusetts Institute of Technology（麻省理工学院）
- University of Maryland, College Park（马里兰大学帕克分校）
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中文总结 AI 辅助
本文针对合成生物学中控制器选择问题，提出一种决策导向的实验设计算法，通过最小化后验控制器选择风险，在更少实验轮次内达到停止标准，同时保持高成功率。
中文摘要 AI 辅助
合成生物学使得设计作为反馈控制器的基因电路成为可能。这些控制器通常使用计算模型进行设计，但模型与真实动力学之间的不匹配可能导致控制器在实践中失效。尽管已有方法来解决这一问题，但合成生物学引入了额外的结构约束。基因电路通常受到实验限制的高度约束，将控制器设计简化为在有限的可实现电路集中进行选择，而不是在连续空间上进行优化。因此，多个系统假设可能导致在可实现集内产生相同的最优控制器。当模型导致相同的最优控制器时，减少模型不确定性可能无关紧要。在本文中，我们利用这一结构开发了一种用于合成生物学中控制器选择的算法，将该问题表述为在有限控制器集上的决策导向实验设计问题。我们使用一组假设来表示植物不确定性，并选择实验以最小化后验控制器选择风险，而不是全局模型不确定性。在三个机制性案例研究中，我们的方法比模型不确定性和随机实验选择策略在更少的实验轮次内达到停止标准，同时保持了相当的成功率。
英文摘要
Synthetic biology enables the design of genetic circuits that act as feedback controllers. These controllers are typically designed using computational models, but mismatch between model and real dynamics can lead to controllers that fail in practice. While methods to address this issue exist, synthetic biology introduces additional structural constraints. Genetic circuits are often highly constrained by experimental limitations, reducing controller design to selection among a limited set of implementable circuits rather than an optimization over a continuous space. As a result, multiple system hypotheses may lead to the same optimal controller within the implementable set. Reducing model uncertainty may therefore be irrelevant when the models lead to the same optimal controller. In this paper, we exploit this structure to develop an algorithm for controller selection in synthetic biology, formulating the problem as a decision-oriented experimental design problem over a finite controller set. We represent plant uncertainty using a set of hypotheses and select experiments to minimize the posterior controller selection risk, rather than global model uncertainty. Across three mechanistic case studies, our method reaches the stopping criterion in fewer experimental rounds than model uncertainty and random experiment selection policies, while maintaining a comparable success rate.
