Software tools for reproducibly building biomodels

用于可重复构建生物模型的软件工具

• 批准号: 10676067
• 负责人: Jonathan Ross Karr
• 金额: $ 39.54万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-13 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10676067
• 关键词: Acceleration; Automation; Bacteria; Beds; Behavior; Biochemical; Biological; Biological Models; Biological Process; Biology; Biomedical Engineering; Cell model; Cells; Collaborations; Computer software; Data; Data Sources; Disease; Ecology; Educational workshop; Electrophysiology (science); Feedback; Future; Genomics; Genotype; Human; Individual; Industrialization; Manuals; Mathematics; Medical; Medicine; Metadata; Methods; Modeling; Organ; Organism; Pathway interactions; Pharmaceutical Preparations; Phenotype; Physicians; Process; Pythons; Reader; Records; Reproducibility; Research Personnel; Resolution; Resources; Scientist; Services; Software Engineering; Software Tools; System; Testing; Tissues; Training; Work; behavior prediction; biochemical model; biological systems; design; experimental study; genomic data; improved; in silico; microorganism; model building; model design; multi-scale modeling; open source; outreach; personalized medicine; predictive modeling; predictive tools; prototype; public database; rational design; simulation; technology research and development; tool; user-friendly; web site

TECHNOLOGY RESEARCH AND DEVELOPMENT 1: PROJECT SUMMARY Despite substantial effort, we cannot comprehensively predict the behavior of biological systems. Consequently, we cannot explain how genotype influences phenotype, design cells, or treat many diseases. Improved dynamical models are needed to understand biology and accelerate bioengineering and medicine. Model building is one of the bottlenecks to better models because our existing model building tools require extensive manual input and obscure the data and assumptions behind models. As a result, authors cannot precisely describe how they constructed models, readers cannot review this information, and models cannot be reproduced. This makes it hard to understand and extend models and, in turn, build accurate models. Recently, we piloted a method for transparently and reproducibly building whole-cell models from diverse genomic and other data. Further work is needed to extend and generalize this method for other domains. We will develop the first software tool for reproducibly and transparently building SBML-compatible dynamical biochemical models of intracellular pathways. The tool will include modules for aggregating model input data, organizing this data for model design, and designing models from this data. The tool will make model building reproducible by tracking every data source and assumption. We will use biochemical models as a test bed for developing broadly-applicable methods for reproducibly building biomodels. This approach will allow us to leverage the large amount of data available to build biochemical models, concretely test our ideas, and integrate our tool into the center's reproducible biochemical modeling workflow. To enable future support for other domains, such as multiscale modeling, electrophysiology, and ecology, we will make our tool as modular and extensible as possible. To ensure that our tool advances biomodeling, we will develop our tool in conjunction with several CPs which aim to develop whole-cell models of bacteria and human cells. These CPs will push us to develop practical tools for constructing models, and we will pull the CPs to construct models that are understandable, reusable, and extensible. To help researchers use our software, we will work with TR&Ds 2 and 3 to combine our software into a reproducible modeling workflow. We will also extensively document our software and distribute it open-source. In addition, as part of the Training and Dissemination Core, we will develop tutorials and organize workshops. We anticipate that our tool will help researchers build more predictive models, and we anticipate that these models will help scientists discover new biology by enabling them to perform unprecedented in silico experiments with complete control, infinite resolution, and unlimited scope; help physicians interpret personal genomic data and personalize therapy; and help bioengineers rationally design microorganisms for a wide range of industrial and medical applications such as detecting disease and synthesizing drugs.

技术研究与开发1：项目概要 尽管付出了巨大的努力，我们仍然无法全面预测生物系统的行为。 因此，我们无法解释基因型如何影响表型、设计细胞或治疗许多疾病。 需要改进的动力学模型来理解生物学和加速生物工程和医学。 模型构建是改进模型的瓶颈之一，因为我们现有的模型构建工具需要 大量的人工输入和模糊模型背后的数据和假设。因此，作者不能 精确地描述他们如何构建模型，读者无法查看这些信息，模型也无法 复制。这使得很难理解和扩展模型，进而构建准确的模型。 最近，我们试验了一种方法，用于透明和可重复地从不同的细胞构建全细胞模型。 基因组和其他数据。需要进一步的工作来扩展和推广这种方法到其他领域。 我们将开发第一个软件工具，用于可重复和透明地构建与SBML兼容的动态 细胞内途径的生化模型。该工具将包括汇总模型输入数据的模块， 组织这些数据用于模型设计，并根据这些数据设计模型。该工具将使模型的建立 通过跟踪每一个数据源和假设来重现。 我们将使用生化模型作为测试平台，开发广泛适用的可重复方法 建造生物模型。这种方法将使我们能够利用大量可用数据来构建 生物化学模型，具体测试我们的想法，并将我们的工具整合到中心的可再生生物化学模型中。 建模工作流。为了使未来支持其他领域，如多尺度建模， 电生理学和生态学，我们将使我们的工具尽可能模块化和可扩展。 为了确保我们的工具能够推进生物建模，我们将与几个CP一起开发我们的工具， 旨在开发细菌和人类细胞的全细胞模型。这些CP将推动我们开发实用的 构建模型的工具，我们将拉动CP来构建可理解的，可重用的， 并且可扩展。 为了帮助研究人员使用我们的软件，我们将与TR& D 2和3合作，将我们的软件联合收割机组合成一个 可重复的建模工作流程。我们还将广泛记录我们的软件，并将其开源。 此外，作为培训和传播核心的一部分，我们将开发教程并组织研讨会。 我们预计，我们的工具将帮助研究人员建立更多的预测模型，我们预计，这些 模型将帮助科学家发现新的生物学，使他们能够进行前所未有的计算机模拟， 完全控制，无限分辨率和无限范围的实验;帮助医生解释个人 基因组数据和个性化治疗;并帮助生物工程师合理设计微生物， 工业和医疗应用的范围，如检测疾病和合成药物。