Collaborative Research: CIF: Medium: Group testing for Real-Time Polymerase Chain Reactions: From Primer Selection to Amplification Curve Analysis

合作研究：CIF：中：实时聚合酶链式反应的分组测试：从引物选择到扩增曲线分析

• 批准号: 2107347
• 负责人: Venkatesan Guruswami
• 金额: $ 20万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107347&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Group

Group testing is a screening technique that relies on careful combinatorial mixing and testing of batches of samples. By using group testing instead of individual testing, for most problem settings of practical interest, one is guaranteed significant savings in the number of tests performed and consequently, significant reductions in reporting delays and experimental costs. Group testing is especially desirable when monitoring the spread of infectious diseases such as Covid-19, which requires frequent examinations of massive populations. Although many ad-hoc approaches to group testing for infectious diseases have been put forward, little work has addressed the problem of end-to-end group-testing protocol design, which includes the selection of genetic regions for viral/bacterial identification, mathematical modeling and analysis of the test results and the development of guiding protocols for communal testing strategies. The overarching goals of the project are to determine which group-testing methods can actually mitigate the spread of Covid-19 and other diseases and to what extent, to estimate the reduction in the number of infected individuals achievable through the use of pooled real-time polymerase chain reaction (RT-PCR) tests, and to aid in the employment of Mobile Testing Units that can reach geographically remote regions. Other broader societal impacts include increased readiness for fighting future pandemics and training a new cohort of young researchers on interdisciplinary topics involving machine learning, coding theory and bioinformatics. The project aims to develop specialized machine-learning, combinatorial and information-theoretic methods for (a) identifying genomic regions with predictably low-mutation rates that may be used as amplification primers for gold-standard real-time polymerase chain reactions (RT-PCR) and determining best mixing strategies based on the likelihood of infection; (b) developing adequate models for amplification curves generated by RT-PCR and corresponding test-errors; (c) formulating experimental-protocol-specific non-adaptive and adaptive semiquantitative group testing schemes that account for nonbinary test outcomes; (d) addressing the testing issues associated with high-viral load subjects and heavy-hitter communities; and (e) integrating the mathematical techniques developed into an agent-based model for disease spreading and control in order to assess the potential impact of group testing and recommend effective test-quarantine-retest strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分组测试是一种筛选技术，依赖于对批次样品的仔细组合混合和测试。通过使用小组测试而不是单独测试，对于大多数具有实际意义的问题设置，可以保证显着节省所执行的测试数量，从而显着减少报告延迟和实验成本。在监测 Covid-19 等传染病的传播时，尤其需要进行群体检测，因为这需要对大量人群进行频繁检查。尽管已经提出了许多针对传染病进行分组测试的临时方法，但很少有工作解决端到端分组测试方案设计的问题，其中包括选择用于病毒/细菌识别的遗传区域、数学建模分析测试结果并制定公共测试策略的指导协议。该项目的总体目标是确定哪些群体测试方法实际上可以减轻 Covid-19 和其他疾病的传播，以及在多大程度上通过使用汇总的实时数据来估计可实现的感染人数减少聚合酶链反应（RT-PCR）测试，并帮助使用可以到达地理偏远地区的移动测试装置。其他更广泛的社会影响包括增强应对未来流行病的准备，以及培训一批新的年轻研究人员，研究涉及机器学习、编码理论和生物信息学的跨学科主题。该项目旨在开发专门的机器学习、组合和信息论方法，用于（a）识别具有可预测的低突变率的基因组区域，这些区域可用作金标准实时聚合酶链式反应（RT-PCR）的扩增引物）并根据感染的可能性确定最佳混合策略； (b) 为 RT-PCR 生成的扩增曲线和相应的测试误差开发适当的模型； (c) 制定特定于实验方案的非适应性和适应性半定量组测试方案，以解释非二元测试结果； (d) 解决与高病毒载量受试者和重击社区相关的检测问题； (e) 将开发的数学技术整合到基于代理的疾病传播和控制模型中，以评估群体检测的潜在影响并推荐有效的检测-隔离-再检测策略。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。