Ultra-rapid full spectral detection optical system facilitating novel PCR based assays- The iPCR approach

该项目将生成一个光学检测系统和相关软件，用于真正实时确定实时聚合酶链式反应过程中产生的荧光水平。Biogene(BG)拥有能够在10分钟内完成实时聚合酶链式反应过程的技术平台，这种速度与独立的反应控制相结合，使其具有许多可能彻底改变诊断领域的新型应用。“真正的实时”聚合酶链式反应是这一领域的一种阶梯式变化--执行反应的能力，使仪器能够跟上化学变化，从而观察和收集当前每周期一个读数方法遗漏的数据。当与超快速聚合酶链式反应相结合时，在毫秒时间尺度上监测所有可见波长的能力促进了以机器学习方法为中心的新分析。基本上，系统可以学习任何反应的理想扩增特性，允许进行因子优化等过程-通过改变保持时间和平板上的浓度等因素，在一次运行中优化任何新的分析。这种方法可以被称为“智能聚合酶链式反应”(IPCR)，因为系统可以在循环荧光中监测，而不是整个反应效率，因此可以通过检测退火、延伸和熔化来自动最小化反应时间，并通过改变保持时间和运行中的温度来单独优化每个反应。好处包括在5分钟内拨打电话，以及循环差分退火和熔融分析。主要的商业好处是机器学习方法的速度和便利性，对于诊断用户来说，在一次运行中优化任何新分析的能力是一个非常强大的工具，此外还可以更快地进行后续的下游诊断调用。这样的系统将允许使用新的化验类型，支持医学界，并从英国政府资助的研究中产生全球增长。

This project will generate an optical detection system and associated software for true realtimedetermination of fluorescence levels generated during real-time PCR. BioGene (BG) hasa technology platform capable of completing the real-time PCR process in 10 minutes and thisspeed combined with independent reaction control lends itself to a number of novelapplications that could revolutionise the diagnostic field. “True real-time” PCR is a stepchange in this sector - the ability to perform reactions whereby the instrumentation can keepup with the chemistry and hence observe and collect data missed by the current one readingper cycle approach. The ability to monitor all visible wavelengths on a milliseconds timescalewhen combined with Ultra-Rapid PCR facilitates new assays centred on a machine learningapproach. Principally, the system can learn the ideal amplification characteristics of anyreaction, allowing processes such as factorial optimisation - the ability to optimise any newassay in a single run - by altering factors such as hold times and concentrations across a plate.This approach can be branded “intelligent PCR” (iPCR) as the system can monitor in cyclefluorescence as opposed to the whole reaction efficiency and so could automatically minimisereaction time, by detecting annealing, completion of extension and melting, and optimise eachreaction individually by altering hold times and temperatures in run. Benefits include makingcalls within 5 minutes and in cycle differential annealing and melt analyses. The majorcommercial benefit is the speed and facilitation of the machine learning approach, fordiagnostic users the ability to optimise any new assay in a single run is a very powerful tooland additionally makes much faster subsequent downstream diagnostic calls. Such a systemwill allow the use of novel assay types, supporting the medical community and generatinggrowth globally from UK government funded research.