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.

我们的微生物群，或者说生活在我们身上和体内的数万亿微生物的集合，是高度动态的 与多种人类疾病有牵连。复杂的计算方法至关重要 用于分析数量和类型不断增加的微生物组数据，包括时间序列、非细菌的分析 微生物组的组成，以及代谢物和基因表达等多种测量方式 级别。该领域另一个令人兴奋的最新趋势是翻译应用，特别是活细菌 治疗人类疾病的疗法。同时，机器学习领域也在不断深入 极大地改进了语音和图像识别等应用的学习技术。我的 实验室为理解微生物组开发了新的机器学习方法和实验方法， 特别关注微生物动力学和细菌疗法。在过去的五年里，我们发展了 用于评估变更一致性的新计算方法和发布的开源软件工具 在治疗药物诱导的微生物组中，预测微生物组的种群动态，并预测 人类宿主的状态(例如，疾病的存在)不受微生物群随时间的变化的影响。我还领导了 描述噬菌体在微生物群动态和肠道发育中的作用的实验努力 以代谢产物为基础的生物标志物分析预测艰难梭菌感染的复发。此外，对于合作者， 我们已经开发出治疗艰难梭菌感染和食物过敏的候选细菌疗法。我的总体愿景是 我在未来五年的实验室是利用深度学习技术来推动微生物组领域超越 在数据中寻找关联，以准确预测扰动对微生物区系的影响，阐明 微生物区系影响宿主的机制，以及改进细菌疗法以使其能够 在临床上取得了成功。我计划通过开发新的深度学习模型来实现这一点，这些模型解决了特定的 微生物组面临的挑战，包括噪音/小数据集，高度异质的人类微生物组， 需要直接解释模型输出、复杂的多模式数据集和施加的约束 生物学原理。我的计划是通过直接将计算模型和生物实验相结合 加强预测的循环，用新的实验测试预测，并改进模型。方法I 将在深度学习模型中纳入概率、嵌入微生物和其他 实体利用丰富的信息(如基因含量或化学结构)，分解多模式数据 进入可解释和交互的小组，并自动设计新的诺生生物实验 寻求最大化计算模型的信息并最终改进嫁接和 候选细菌疗法的疗效。一个重要的目标也将是使我的计算工具 通过发布高质量的开源软件，Lab开发了广泛适用于研究社区的软件。