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

分组测试是一种筛选技术，它依赖于对批次样本进行仔细的组合混合和测试。通过使用集体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次数，从而显著减少报告延迟和实验成本。在监测新冠肺炎等传染病的传播时，群体检测尤其可取，因为这需要对大量人群进行频繁的检查。虽然已经提出了许多针对传染病群体检测的特别方法，但很少涉及端到端群体检测方案设计的问题，这包括选择用于病毒/细菌鉴定的遗传区、对检测结果进行数学建模和分析以及为公共检测策略制定指导方案。该项目的总体目标是确定哪些群体检测方法实际上可以减少新冠肺炎和其他疾病的传播，以及在多大程度上减轻这些疾病的传播，估计通过使用集中实时聚合酶链式反应(RT-PCR)检测可实现的感染人数的减少，并帮助使用可以到达地理偏远地区的移动检测单位。其他更广泛的社会影响包括加强抗击未来大流行的准备，以及培训一批新的年轻研究人员，涉及机器学习、编码理论和生物信息学等跨学科主题。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)确定可用作黄金标准实时聚合酶链式反应(RT-PCR)扩增引物的具有可预测低突变率的基因组区域，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错误开发适当的模型；(C)制定考虑非二元测试结果的针对实验方案的非适应性和适应性半定量群体测试方案；(D)解决与高病毒载量受试者和重击群体相关的测试问题；以及(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，以评估群体测试的潜在影响，并建议有效的测试-隔离-重新测试策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.