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Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development
SLAS Technology: Translating Life Sciences Innovation ( IF 2.7 ) Pub Date : 2021-11-23 , DOI: 10.1177/24726303211053782
Sam Elder 1 , Carleen Klumpp-Thomas 2 , Adam Yasgar 2 , Jameson Travers 2 , Shayne Frebert 2 , Kelli M Wilson 2 , Alexey V Zakharov 2 , Jayme L Dahlin 2 , Christoph Kreisbeck 1 , Dennis Sheberla 1 , Gurusingham S Sittampalam 2 , Alexander G Godfrey 2 , Anton Simeonov 2 , Sam Michael 2
Affiliation  

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

Graphical Abstract



中文翻译:

用于自主生物检测开发的跨平台贝叶斯优化系统

当前的高通量筛选分析优化通常是一个手动且耗时的过程,即使使用实验设计方法也是如此。作为国家推进转化科学中心 (NCATS) ASPIRE(创新研究探索专业平台)计划的一部分,开发了一种基于云的基于贝叶斯优化的跨平台算法,以加速临床前药物发现。木瓜蛋白酶酶活性的无细胞测定被用作生物测定开发和系统操作的概念证明。与连续测试所有 294 个测定条件以找到全局最优的蛮力方法相比,贝叶斯优化算法可以通过平均测试 21 个测定条件来找到最佳测定性能的合适条件,最多同时测试 20 个条件,这通过重复模拟得到证实。该算法可以将实验室用品和高通量实验运行时间的成本降低七倍,同时通过安全连接从远程站点进行控制。基于这一概念验证,该技术有望应用于 NCATS 更复杂的生物测定和自动化化学反应筛选,并应可转移到其他机构。

图形概要

更新日期:2021-11-24
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