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: 10357850
• 负责人: Markus W Covert
• 金额: $ 37.11万
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10357850
• 关键词: 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; base; biological research; cell behavior; cell growth; cloud based; data curation; 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).

研究概要/摘要 生物数据的产生正在迅速地向我们呈现一个最苛刻的数据分析 这是世界面临的最大挑战--不仅是在存储和可访问性方面， 就其广泛的异质性和可变性而言。在本建议中，我们提出了一种新的方法， 挑战，我们称之为“深度策展”：一种大规模的集成建模方法， 交叉评估数百万的异构数据。“深”字反映了多重 我们执行的管理层，不仅包括数据层，还包括从这些数据中导出的参数层。 数据、数学方程、统一模型和仿真输出。因此， 模型是自动处理、管理和分析数据的宝贵工具。我们建议的努力， 深度固化是基于大肠杆菌的计算机模型，该模型大致解释了 40%的注释良好的基因，并基于一组广泛的不同的测量汇编从 数千份报告（目前正在《科学》杂志进行第二轮审查）。该提案的目标是扩大这一点， 该模型支持对与超过100个当前未合并环境的增长相关的数据进行深度管理。我们 然后可以同时评估数据集的交叉一致性，作为一个统一的整体， 数据集不具有交叉一致性的区域，因此需要进一步的实验研究。 这一提议的意义在于，深度策展代表了我们人类历史上第一次的量子飞跃。 能够利用大量异构，可变和复杂的生物数据集;它自动化， 加速变革性的生物医学发现;我们将在EcoCyc， 关于任何生物体的最全面的数据库，以及现有的最复杂的生物模型; 全细胞建模是一个快速发展的领域，随着它向更高水平的发展， 复杂的细胞和细胞群。与此建议相关的创新是，深度策展是一个 一种全新的、高度创新的方法，目前世界上任何其他实验室都无法使用; 拟议的工作将产生一个显着扩大的全细胞模型，以前看不见的复杂性;以及 作为新颖和高度创新的建模技术;我们包括明确的知识策展， 除了数据之外，还有机制; EcoCyc数据库和 E.大肠杆菌模式将以协同的方式极大地扩大两者的能力、范围和可见性。我们 具体目标是：目标1（策展），建立与E相关的数据和参数层。大肠杆菌生长在不同的 目标2（建模），实现方程、模型和仿真层;目标3（深度固化）， 使用综合模型在整个生物体尺度上对统一数据集进行交叉评价;以及 （分发），通过GitHub（软件工具），EcoCyc（数据） 和参数）以及Google Cloud（模拟和交互式可视化）。