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: 2107344
• 负责人: Olgica Milenkovic
• 金额: $ 60万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107344&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.

小组测试是一种筛选技术，依赖于仔细的组合混合和样品批次测试。通过使用小组测试而不是单个测试，对于大多数实际兴趣的问题设置，可以保证在执行的测试数量中节省大量资金，因此，报告延迟和实验成本的大幅减少。当监测传染病（例如Covid-19）的传播时，小组测试尤其是可取的，该传染病需要经常检查大量人群。尽管已经提出了许多用于传染病的小组测试的临时方法，但很少有工作解决了端到端组测试方案设计的问题，其中包括选择用于病毒/细菌鉴定的遗传区域，数学识别，数学建模和测试结果的分析以及用于共同测试策略的指导协议的开发。该项目的总体目标是确定哪种团体测试方法实际上可以减轻COVID-19和其他疾病的传播，并在多大程度上估算通过使用汇集的实时聚合酶链反应（RT-PCR）测试的受感染者数量减少，并帮助您可以在移动测试中雇用地理位置图。其他更广泛的社会影响包括与未来的大流行有关的准备就绪，并培训了一批新的年轻研究人员，涉及涉及机器学习，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，组合和信息理论方法，以（a）识别具有可预测的低妇女率的基因组区域，可以用作金标准实时聚合酶链反应（RT-PCR）的放大引物（RT-PCR），并根据感染的可能性确定最佳混合策略； （b）开发足够的模型，用于由RT-PCR和相应的测试轨道产生的扩增曲线； （c）制定实验性特异性非自适应和适应性半定量组测试方案，以解释非二元测试结果； （d）解决与高病毒负荷受试者和重击社区相关的测试问题； （e）将开发到基于疾病的疾病传播和控制的基于代理的模型中，以评估小组测试的潜在影响，并建议有效的测试质量重新测试策略。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估来审查Criteria通过评估的支持。