StochSS: A Next-Generation Toolkit for Simulation-Driven Biological Discovery

StochSS：用于模拟驱动的生物发现的下一代工具包

• 批准号: 9789865
• 负责人: Linda R. Petzold
• 金额: $ 54.02万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789865
• 关键词: Address; Algorithms; Behavior; Biological; Biological Process; Chemical Models; Chemicals; Cloud Computing; Complex; Computer software; Data; Development; Differential Equation; Disease; Drug Targeting; Ensure; Environment; Evaluation; Funding; Generations; Geometry; Infrastructure; Institutes; Intervention; Lifting; Modeling; Molecular; Movement; Occupations; Outcome; Process; Pythons; Reporting; System; Testing; Time; Uncertainty; United States National Institutes of Health; Variant; Work; biochemical model; chemical kinetics; community involvement; data format; design; experimental study; file format; flexibility; improved; mathematical model; model building; model development; models and simulation; next generation; novel; simulation; simulation software; software as a service; tool; usability

Project Summary The development of a mathematical model is critical to the understanding of complex biological processes because it codifies current understanding so that it can be tested against existing data. A good model with sufficient detail can be used to identify potential points of intervention (for example, drug targets) at which an undesired outcome (for example, effects of disease) of the process might be altered. Model development proceeds through a cycle of model building, simulation of the model under numerous conditions, and comparison to experimental data. The cycle is repeated and often augmented by new experiments to capture additional data, until the resulting model can plausibly explain the data. Tremendous amounts of time and effort must be devoted to finding and/or developing tools to analyze the model and compare it to the data, fit the parameters and assess the effects of typically large amounts of uncertainty in both the data and the parameters, simulate the model and analyze the simulation data, refine the model to better capture our increased understanding at each stage of the process, decide which additional experiments would add most to our understanding, etc. Our objective in the proposed work is to facilitate and accelerate the modeling process by providing state of the art, well-integrated tools to report complete and informative results at each stage, enabling the modeler and the experimentalist to focus on what they do best: scientific discovery. This is a renewal proposal that builds on the capabilities and infrastructure developed in the current project. In that work we developed StochSS, a novel Software-as-a-Service offering for quantitative modeling of biochemical networks capable of seamless deployment in public cloud environments. StochSS does an excellent job of supporting two of the major steps of the modeling process: Model Building - taking your model description and putting it into a form that the StochSS simulation engines can work with, and Simulation - performing the simulations to produce the results. The proposed project has three complementary Aims. The first is to further develop StochSS's core capabilities and to take the steps that will ensure its long-term sustainability; the second is to develop a Model Development Toolkit, and the third is to develop a Model Exploration Toolkit. Both of these toolkits will be integrated into our existing StochSS Model Building and Simulation environment and will leverage our existing software infrastructure for cloud computing. Aim 1. Core Capabilities and Long-Term Sustainability This aim has three sub-aims: (1) instituting practices that will help ensure community involvement and better long-term sustainability of StochSS beyond NIH funding, (2) extending core StochSS functional capabilities, and (3) improving compatibility with other software via support for standard data formats. Aim 2. Model Development Toolkit Develop and integrate tools to facilitate and accelerate the process of Model Development: the iterations of (modeling, simulation, experiment) that are typically required to converge on the most plausible model that can explain the data. The Model Development Toolkit will address parameter estimation and quantification of uncertainty, generation and evaluation of the set of plausible models, and optimal design of experiments. Aim 3. Model Exploration Toolkit Develop and integrate tools for Model Exploration: the process of exploring the parameter space to ensure that the model is robust to variations in uncertain and/or undetermined parameters, to find the regions of parameter space in which the model is capable of yielding a given behavior, and to discover all of the qualitatively distinct behaviors that the model is capable of within the space of uncertain and/or undetermined parameters.

项目概要 数学模型的开发对于理解复杂的生物过程至关重要，因为它整理了当前的理解，以便可以根据现有数据进行测试。一个好的模型 足够详细的信息可用于确定潜在的干预点（例如药物靶标） 该过程的不良结果（例如疾病的影响）可能会被改变。模型开发通过模型构建、在多种条件下模拟模型的循环进行，以及 与实验数据的比较。这个循环不断重复，并且经常通过新的实验来增强，以捕获 额外的数据，直到生成的模型能够合理地解释数据。大量的时间和 必须致力于寻找和/或开发工具来分析模型并将其与数据进行比较， 拟合参数并评估数据和模型中通常存在大量不确定性的影响 参数，模拟模型并分析模拟数据，完善模型以更好地捕捉我们的 增加对过程每个阶段的理解，决定哪些额外的实验将增加最多 根据我们的理解等。我们拟议工作的目标是促进和加速建模 通过提供最先进的、集成良好的工具来报告每个过程的完整和信息丰富的结果 阶段，使建模者和实验者能够专注于他们最擅长的事情：科学发现。 这是一项基于当前开发的能力和基础设施的更新提案。 项目。在这项工作中，我们开发了 StochSS，一种用于定量建模的新型软件即服务产品 能够在公共云环境中无缝部署的生化网络。 StochSS 做了一个 出色地支持建模过程的两个主要步骤：模型构建 - 采取您的 模型描述并将其转化为 StochSS 模拟引擎可以使用的形式，以及 模拟 - 执行模拟以产生结果。 拟议项目具有三个互补的目标。首先是进一步开发StochSS的核心 能力并采取措施确保其长期可持续性；第二是开发一个 模型开发工具包，第三是开发模型探索工具包。这两个工具包 将集成到我们现有的 StochSS 模型构建和仿真环境中，并将利用 我们现有的云计算软件基础设施。 目标 1. 核心能力和长期可持续性 该目标包含三个子目标： (1) 建立 有助于确保社区参与和 StochSS 更好的长期可持续性的实践 NIH 资助，(2) 扩展 StochSS 的核心功能，以及 (3) 提高与其他系统的兼容性 软件通过支持标准数据格式。 目标 2. 模型开发工具包 开发和集成工具以促进和加速该过程 模型开发：通常需要的迭代（建模、模拟、实验） 集中于可以解释数据的最合理的模型。模型开发工具包将 解决参数估计和不确定性的量化，生成和评估一组 合理的模型和实验的优化设计。 目标 3. 模型探索工具包 开发和集成模型探索工具：流程 探索参数空间以确保模型对不确定和/或不确定的变化具有鲁棒性 未确定的参数，找到模型能够产生参数空间的区域 给定的行为，并发现模型能够实现的所有性质不同的行为 不确定和/或未确定参数的空间。