College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.
Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia.
Genes (Basel). 2024 Sep 11;15(9):1196. doi: 10.3390/genes15091196.
The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.
PCR 的应用非常广泛，在生物领域尤其如此。在某些领域，如法医学，由于需要在短时间内进行 DNA 分析，起始材料的量可能非常少，并且可能需要在存在抑制化合物的情况下进行，因此需要将 PCR 发挥到极致。由于在法医学中使用 PCR 对样品进行高通量处理，因此，PCR 能力的任何微小提高都可能对该领域产生巨大影响。至少部分改进 PCR 性能的方法可能是通过改变 PCR 循环条件来实现。这些改变可以是一般性的，即应用于所有样品，也可以针对个别样品进行定制，以达到最大的靶向效果。此外，还有可能实时响应样品中 PCR 的条件，并在飞行中更改循环参数。要实现这一目标，既需要一种实时跟踪 PCR 条件的方法，又需要了解在当前条件下应如何改变循环参数。在我们工作的第一部分中，我们为创建一个能够实时适应当前条件下个别样品中特征的智能 PCR 系统的雄心勃勃的目标奠定了理论基础。我们使用开放式 qPCR 仪器来实现这一目标，该仪器提供实时反馈和机器学习功能，以识别不同阶段 PCR“看起来像”什么。我们描述了设置实时反馈系统的基本步骤，设计了一种从一个循环到另一个循环控制 PCR 循环条件的方法，并开发了一种定义 PCR 目标的系统，对系统实现这些目标的性能进行评分。然后，我们提出了三项概念验证研究，证明了这种整体方法的可行性。在我们工作的第二部分中，我们在一个大规模的 PCR 循环条件改变实验中展示了本文中概述的理论的性能。其目的是利用机器学习，以便在整个 PCR 过程中自动调整循环条件，以最佳方式朝着用户定义的目标改变。智能 PCR 系统的实现将对使用 PCR 的生物领域产生广泛的影响。
