Transitions: Deep Learning Models for Microbial Image Analysis and Time-Series Predictions

本奖项全部或部分由《2021年美国救援计划法案》（公法117- 2）资助。深度学习是分析生物数据的一种强大的计算策略。例如，可以训练模型来识别图像中细胞的位置，这是一项繁琐的任务，需要大量的人工输入。尽管深度学习工具为生物数据分析提供了巨大的潜力，但利用最先进的方法并避免潜在的陷阱需要大量的专业知识。这个项目的一个主要目标是为PI和研究团队提供应用培训，以利用这些授权技术。该项目分为三个阶段。第一个阶段是专业发展期，PI将通过课程作业、教程和实践项目的结合来学习最先进的深度学习技术。该项目的第二部分侧重于应用这些新的深度学习方法来开发用于图像分析和时间序列预测的新工具。第三部分涵盖所有项目年份，涉及教育和外展活动。其中包括与工程生物学研究联盟合作，为系统和合成生物学的研究人员和教育工作者提供机器学习的教育模块。它还将引入新的课程内容，这些内容将整合到工程课程中。此外，该项目还为本科生提供了培训机会。美国救援计划的资金为这位调查员在职业生涯的关键阶段提供了支持。该项目技术创新的主要来源是开发用于分析延时显微镜数据和网络推理算法的新工具。支撑这些工具的是深度学习模型，包括那些基于卷积和循环神经网络和变压器的模型，它们代表了当前图像处理和时间序列预测的最新技术。这些努力将产生两类工具。首先，研究人员将开发代码，以在一系列延时显微镜图像中准确地分割、跟踪和确定细胞命运。第二种方法使用时间序列数据来推断网络连接。深度学习模型可以处理信号之间的复杂关系，如时间延迟和反馈交互，这表明它们可能是比经典方法更准确的系统识别工具。总的来说，通过利用新的深度学习算法，研究人员将开发与微生物图像分析和时间序列预测相关的新型建模方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117- 2).Deep learning is a powerful computational strategy for analyzing biological data. For example, models can be trained to identify the location of cells within an image, a task that has historically been cumbersome and required significant manual input. Although deep learning tools offer great potential for the analysis of biological data, taking advantage of state-of-the-art methods and avoiding potential pitfalls requires significant expertise. A major goal of this project is to provide the PI and research team applied training to take advantage of these empowering technologies. The project is divided into three periods. The first is a professional development period where the PI will learn state-of-the-art deep learning techniques through a combination of coursework, tutorials, and hands-on projects. The second part of the project focuses on applying these new deep learning methods to develop new tools for image analysis and time-series predictions. The third part spans all project years and involves education and outreach initiatives. These include partnering with the Engineering Biology Research Consortium to generate education modules on machine learning for researchers and educators in systems and synthetic biology. It will also introduce new curricular content that will be integrated into engineering coursework. In addition, the project provides training opportunities for undergraduate students. American Rescue Plan funding provides support for this investigator at a critical stage in her career.The primary sources of technical innovation for this project are the development of new tools for the analysis of time-lapse microscopy data and network inference algorithms. Underpinning these tools are deep learning models, including those based on convolutional and recurrent neural networks and transformers, which represent the current state-of-the-art for image processing and time-series predictions. The efforts will produce two classes of tools. In the first, the researchers will develop code to accurately segment, track, and determine cell fate within a series of time-lapse microscopy images. The second method uses time-series data to infer network connectivity. Deep learning models can handle complex relationships between signals such as time delays and feedback interactions, suggesting they may be a more accurate system identification tool than classical approaches. Overall, by taking advantage of new deep learning algorithms, the researchers will develop novel modeling approaches that are relevant to microbial image analysis and time-series predictions.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.