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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

基本信息

批准号：
10244992
负责人：
Linda R. Petzold
金额：
$ 53.72万
依托单位：
UNIVERSITY OF CALIFORNIA SANTA BARBARA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-05-15 至 2023-06-30
项目状态：
已结题

项目摘要

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.

项目摘要数学模型的开发对于理解复杂的生物过程至关重要，因为它将当前的理解编入法典，以便可以根据现有数据进行测试。一个很好的模型可用于识别潜在的干预点（例如，药物靶点），该过程的不希望的结果（例如，疾病的影响）可能被改变。模型开发通过模型构建、在多种条件下模拟模型以及对比实验数据。这一循环不断重复，并经常通过新的实验来增强，额外的数据，直到产生的模型可以合理地解释数据。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，并评估数据和参数，模拟模型并分析模拟数据，改进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我们的目标是在拟议的工作是促进和加快建模通过提供最先进的、集成良好的工具来报告每个阶段的完整和信息丰富的结果，这一阶段，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。这是一个更新建议，建立在当前开发的能力和基础设施的基础上，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执行出色地支持了建模过程的两个主要步骤：模型构建-将您的模型描述并将其转换为StochSS仿真引擎可以使用的形式，以及模拟-执行模拟以产生结果。拟议的项目有三个相辅相成的目标。首先是进一步发展StochSS的核心第一，提高能力，并采取措施，确保其长期可持续性;第二，制定一个模型开发工具包，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这一目标有三个次级目标：有助于确保社区参与和StochSS更好的长期可持续性 NIH资助，（2）扩展核心StochSS功能能力，（3）提高与其他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，以促进和加速这一过程模型开发：通常需要的（建模、仿真、实验）迭代，集中在最合理的模型上来解释这些数据。模型开发工具包将解决参数估计和量化的不确定性，生成和评估的一套合理的模型和最优的实验设计。目标3.模型探索工具包开发和集成模型探索工具：过程探索参数空间，以确保模型对不确定和/或未确定的参数，以找到参数空间的区域，其中模型能够产生给定的行为，并发现模型能够实现的所有定性不同的行为不确定和/或不确定参数的空间。