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，依赖于仔细的组合混合和测试批次的样品。通过使用组测试而不是单独测试，对于大多数实际感兴趣的问题设置，可以保证显著节省执行的测试数量，从而显著减少报告延迟和实验成本。在监测新型冠状病毒（COVID-19）等传染病的传播时，群体检测尤其可取，因为这需要对大量人群进行频繁检查。虽然已经提出了许多特设的方法来组检测传染病，很少有工作已经解决的问题，端到端的组检测协议的设计，其中包括选择的遗传区域的病毒/细菌的识别，数学建模和分析的测试结果和开发的指导协议的公共测试策略。该项目的总体目标是确定哪些群体检测方法可以实际减缓COVID-19和其他疾病的传播，以及在多大程度上，估计通过使用混合实时聚合酶链反应（RT-PCR）检测可以减少感染人数，并帮助使用可以到达地理偏远地区的移动的检测单位。其他更广泛的社会影响包括提高对抗未来大流行病的准备程度，以及在涉及机器学习、编码理论和生物信息学的跨学科课题上培训一批新的年轻研究人员。该项目旨在开发专门的机器学习、组合和信息理论方法，用于：（a）确定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反应的扩增引物，并根据感染的可能性确定最佳混合策略;（B）为RT-PCR产生的扩增曲线和相应的测试误差开发适当的模型;（c）制定实验方案特定的非适应性和适应性半定量组测试方案，其说明非二元测试结果;（d）解决与高病毒载量受试者和高感染群体有关的检测问题;以及（e）将所开发的数学技术整合到基于病原体的疾病传播和控制模型中，以评估群体测试的潜在影响，并推荐有效的测试-检疫-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的评估支持影响审查标准。