Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes

用于分析动态和异质微生物组的概率深度学习模型和集成生物实验

• 批准号: 10622713
• 负责人: Georg Kurt Gerber
• 金额: $ 44.75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10622713
• 关键词: Acceleration; Address; Affect; Assessment tool; Bacteriophages; Biological; Biological Assay; Biological Markers; Chemical Structure; Clinic; Clostridium difficile; Collection; Communities; Complex; Computer Models; Computer software; Computing Methodologies; Data; Data Set; Diabetes Mellitus; Disease; Engraftment; Food Hypersensitivity; Gene Expression; Genes; Gnotobiotic; Goals; Health; Heart Diseases; Human; Human Microbiome; Hypersensitivity; Image; Infection; Kidney Diseases; Knowledge; Liver diseases; Machine Learning; Malignant Neoplasms; Measurement; Microbe; Modality; Modeling; Mus; Output; Play; Population Dynamics; Population Projection; Recurrence; Research; Role; Series; Software Tools; Speech; Statistical Models; Technology; Testing; Therapeutic; Time; Vision; computerized tools; deep learning; deep learning model; design; experimental study; human disease; improved; machine learning method; microbial; microbiome; microbiome alteration; microbiome components; microbiota; microorganism; multimodal data; nervous system disorder; new technology; novel; open source; success; targeted treatment; therapy development; tool; translational applications; trend

Our microbiomes, or the collections of trillions of micro-organisms that live on and within us, are highly dynamic and have been implicated in a variety of human diseases. Sophisticated computational approaches are critical for analyzing increasing quantities and types of microbiome data, including time-series, assays for non-bacterial components of the microbiome, and multiple measurement modalities such as metabolite and gene expression levels. Another exciting recent trend in the field has been translational applications, particularly live bacterial therapies for treating human diseases. In parallel, the field of machine learning has been advancing with deep learning technologies that have dramatically improved applications such as speech and image recognition. My lab develops novel machine learning methods and experimental approaches for understanding the microbiome, with a particular focus on microbial dynamics and bacteriotherapies. In the past five years, we have developed new computational methods and released open-source software tools for assessing the consistency of changes in the microbiome induced by therapeutics, forecasting population dynamics of microbiomes, and predicting the status (e.g., presence of disease) of the human host from changes in the microbiome over time. I have also led experimental efforts to delineate the role of bacteriophages in microbiome dynamics and to develop gut metabolite-based biomarker assays to predict recurrence of C. difficile infection. Additionally, with collaborators, we have developed candidate bacteriotherapies for C. difficile infection and food allergies. My overall vision for my lab in the next five years is to leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. I plan to accomplish this by developing new deep learning models that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. My plan is to directly couple computational models and biological experiments through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. Approaches I will pursue include incorporating into deep learning models probability, embeddings of microbes and other entities using rich information (such as gene content or chemical structure), decomposition of multi-modal data into interpretable and interacting groups, and automated design of new biological experiments in gnotobiotic mice that seek to maximize information for computational models and ultimately improve engraftment and efficacy of candidate bacteriotherapies. An important objective will also be to make computational tools that my lab develops widely available to the research community, through release of quality open-source software.

我们的微生物组或生活在我们内部和内部的微生物的集合是高度动态的 并与各种人类疾病有关。复杂的计算方法至关重要 用于分析增加数量和类型的微生物组数据，包括时间序，非细菌的测定 微生物组的成分以及多种测量方式，例如代谢产物和基因表达 水平。该领域的另一个令人兴奋的趋势是翻译应用，尤其是活细菌 治疗人类疾病的疗法。同时，机器学习领域一直在深入发展 大大改善了语音和图像识别等应用程序的学习技术。我的 实验室开发了新颖的机器学习方法和实验方法，以理解微生物组， 特别关注微生物动力学和细菌疗法。在过去的五年中，我们已经发展 新的计算方法并发布了用于评估更改一致性的开源软件工具 在由治疗剂诱导的微生物组中，预测微生物组的种群动力学，并预测 随着时间的推移，微生物组的变化，人类宿主的状态（例如，疾病的存在）。我也带领 实验性的努力来描述噬菌体在微生物组动力学中的作用并发展肠道 基于代谢物的生物标志物测定，以预测艰难梭菌感染的复发。此外，与合作者一起 我们已经开发了用于艰难梭菌感染和食物过敏的候选细菌。我对 我未来五年的实验室是利用深度学习技术将微生物组领域推进 在数据中找到关联，以准确预测扰动对微生物群的影响，阐明 微生物群会影响宿主的机制，并改善细菌疗法以使其能够 在诊所的成功。我计划通过开发新的深度学习模型来实现这一目标，以解决特定的 微生物组的挑战，包括嘈杂/小数据集，高度异源的人类微生物组， 需要直接解释模型输出，复杂的多模式数据集以及由 生物原理。我的计划是直接通过计算模型和生物学实验 加强预测，通过新实验测试预测的周期以及改进模型。接近i 将追求包括纳入深度学习模型的概率，微生物的嵌入和其他 使用丰富信息（例如基因含量或化学结构）的实体，多模式数据的分解 进入可解释的和相互作用的组，以及在Gnotobiotic中的新生物学实验的自动设计 试图最大化计算模型信息的小鼠，并最终改善植入和 候选细菌疗法的功效。一个重要的目标还将是制造我的计算工具 通过发布质量开源软件，LAB开发了研究社区的广泛使用。