Deep Curation via an Integrated Whole-Cell Computational Model

通过集成的全细胞计算模型进行深度管理

• 批准号: 10557790
• 负责人: Markus W Covert
• 金额: $ 37.17万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557790
• 关键词: Acceleration; Architecture; Area; Bacteria; Big Data; Biological; Biological Models; Biology; Cell model; Cell physiology; Cells; Communication; Communities; Complex; Computer Models; Data; Data Analyses; Data Set; Databases; Environment; Equation; Escherichia coli; Friction; Gene Expression Profile; Gene Expression Regulation; Generations; Genes; Goals; Growth; Heterogeneity; Individual; Intuition; Investigation; Journals; Knowledge; Life Cycle Stages; Link; Literature; Mathematics; Measurement; Modeling; Names; Nutrient; Organism; Output; Process; Publishing; Reporting; Research; Running; Science; Scientist; Software Tools; Source Code; System; Technology; Time; Update; Validation; Visualization; Visualization software; Whole Organism; Work; biological research; cell behavior; cell growth; cloud based; data curation; data integration; design; experimental study; heterogenous data; innovation; knowledge curation; models and simulation; novel; novel strategies; quantum; simulation; tool

Research Summary/Abstract The generation of biological data is rapidly presenting us with one of the most demanding data analysis challenges the world has ever faced - not only in terms of storage and accessibility, but perhaps more critically in terms of its extensive heterogeneity and variability. In this proposal, we present a new approach to these challenges, which we call “Deep Curation”: a large-scale, integrated modeling approach to simultaneously cross-evaluate millions of heterogeneous data against themselves. The word “deep” reflects the multiple layers of curation we perform, including layers not only for data, but also for parameters derived from these data, the mathematical equations, the unified model, and the simulation output. Thus, the deeply-curated model is an invaluable tool for processing, curating and analyzing data automatically. Our proposed efforts in Deep Curation are based on a computer model of Escherichia coli that accounts for the function of roughly 40% of the well-annotated genes, and is based on an extensive set of diverse measurements compiled from thousands of reports (currently in 2nd round of review at Science). The goal of this proposal is to expand this model to enable Deep Curation of data related to growth on >100 currently-unincorporated environments. We can then assess the cross-consistency of the data sets simultaneously, as a unified whole, identifying critical areas in which datasets are not cross-consistent and therefore further experimental investigation is needed. The Significance of this proposal is that Deep Curation represents a first-in-kind quantum leap forward in our ability to exploit massively heterogeneous, variable and complex biological datasets; that it automates and accelerates transformative biomedical discovery; that we will create a bi-directional pipeline between EcoCyc, the most comprehensive database on any organism, and the most complex biological model in existence; and that whole-cell modeling is a rapidly-growing field with transformative potential as it advances towards more complex cells and groups of cells. The Innovation associated with this proposal is that Deep Curation is a brand-new and highly innovative approach that is not currently available to any other lab in the world; that the proposed work will produce a dramatically expanded whole-cell model of previously-unseen complexity; as well as novel and highly innovative modeling technology; that we include explicit curation of knowledge regarding mechanism in addition to data; and that the automated communication between the EcoCyc database and the E. coli model will dramatically expand the capacity, scope and visibility of both in a synergistic way. Our Specific Aims are: Aim 1 (Curation), build the Data and Parameter layers related to E. coli growth on diverse environments; Aim 2 (Modeling), implement the Equation, Model and Simulation layers; Aim 3 (Deep Curation), use the integrated model to cross-evaluate the unified data set at the whole-organism scale; and Aim 4 (Distribution), make the model available to the broader community via GitHub (software tools), EcoCyc (data and parameters), and Google Cloud (simulations and interactive visualizations).

研究摘要/摘要 生物数据的产生迅速向我们介绍了最苛刻的数据分析之一 世界曾经面临的挑战 - 不仅在存储和可及性方面，而且更重要的是 就其广泛的异质性和可变性而言。在此提案中，我们提出了一种新的方法 我们称之为“深度策划”的挑战：一种简单的大规模，集成的建模方法 跨评估数百万的异质数据对自己。 “深”一词反映了倍数 我们执行的策展层，不仅包括数据层，而且还包括从这些层中得出的参数 数据，数学方程，统一模型和仿真输出。那是深切的 模型是自动处理，策展和分析数据的宝贵工具。我们提出的努力 深度策划基于大肠杆菌的计算机模型，该模型大致说明了功能 40％的被宣布的基因的40％，是基于一组广泛的潜水员测量。 成千上万的报告（目前在科学的第二轮审查中）。该提议的目的是扩大 模型以深度策划与> 100个目前未纳入环境的增长有关的数据。我们 然后可以简单地评估数据集的跨矛盾，以确定关键 数据集不是跨矛盾的领域，因此需要进一步的实验研究。 该提议的意义在于，深度策划代表了我们的第一个量子飞跃 能够探索大量异质，可变和复杂的生物学数据集的能力；它是自动的，并且 加速变革性的生物医学发现；我们将在Ecocyc之间创建双向管道 任何组织上最全面的数据库，也是存在的最复杂的生物学模型；和 全细胞建模是一个快速增长的领域，随着它朝着更多 复杂的细胞和一组细胞。与该提议相关的创新是深层策划是 崭新的和高度创新的方法目前尚不适用于世界上任何其他实验室；那是 拟议的工作将产生动态扩展的以前未见复杂性的全细胞模型。也是如此 作为新颖而高度创新的建模技术；我们包括有关知识的明确策划 除了数据外；并且Ecocyc数据库与 大肠杆菌模型将以协同的方式大大扩展两者的容量，范围和可见性。我们的 具体目的是：AIM 1（策展），构建与潜水员大肠杆菌增长有关的数据和参数层 环境； AIM 2（建模），实现方程，模型和仿真层；目标3（深度策划）， 使用集成模型在整个有生物量表上交叉评估统一数据集；和目标4 （分发），通过GitHub（软件工具），Ecocyc（数据 和参数）和Google云（仿真和交互式可视化）。