Hardening Software for Rule-based Modeling.

用于基于规则的建模的强化软件。

基本信息

  • 批准号:
    8898854
  • 负责人:
  • 金额:
    $ 33.22万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-08-01 至 2018-04-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Approximately 30 software tools have been developed for rule-based modeling of biomolecular interaction networks. These tools enable new types of modeling studies. They are particularly useful for investigating biomolecular site dynamics: changes in the states of the functional sites of biomolecules, such as site-specific phosphorylation dynamics. With few exceptions, available software for rule-based modeling is still in a primitive state and critical capabilities are simply unavailable. Existing tools do not provide capabilities that are routinely used in ODE modeling, such as fitting, sensitivity analysis and bifurcation analysis. Moreover, simulators that implement the most generally applicable simulation methods (direct methods) are not being actively developed, and these simulators need to be updated to properly handle certain classes of important problems as well as to offer greater efficiency. We propose to create a toolbox of software tools that will advance the field of computational systems biology. We have identified gaps in existing rule-based software capabilities and present a systematic approach to fill them. Our plan for developing more efficient direct simulation tools involves a two-pronged approach: enabling use of available simulators in distributed computing environments and developing new equation-free computational methods that offer the promise of greater efficiency and integration with existing data analysis software packages. In developing this toolbox, we will improve software for rule-based modeling; integrate existing software tools, and developing new tools for sensitivity and bifurcation analysis and data fitting. These tools are needed so that rule-based modelers can leverage data suited for calibrating parameters of rule-based models, including high-throughput proteomic data. These tools are also needed for diagnosing the dependence of predicted model behaviors on uncertain model parameters, designing experiments to reduce uncertainty in parameter estimates, and elucidating bifurcations (points in parameter space at which sharp transitions in behavior occur). We will test and validate these tools by building a model of receptor tyrosine kinase (RTK) signaling and using this model to investigate how site-specific tyrosine phosphorylation depends on properties of RTK tyrosines and their binding partners. This focus on a driving biological question will ensure that our software development activities are directed at useful capabilities. Our experience developing software tools for rule-based modeling, as well as novel methods, uniquely qualifies us to carry out this proposed project.
描述(由申请人提供):已经开发了大约30个软件工具,用于生物分子相互作用网络的基于规则的建模。这些工具支持新类型的建模研究。它们对于研究生物分子位置动力学特别有用:生物分子功能位置状态的变化,例如特定位置的磷酸化动力学。除了少数例外,可用的基于规则的建模软件仍然处于原始状态,关键功能根本不可用。现有的工具不提供在常微分方程建模中常规使用的功能,例如拟合、灵敏度分析和分叉分析。此外,没有积极开发实现最普遍适用的模拟方法(直接方法)的模拟器,这些模拟器需要更新以适当地处理某些类别的重要问题并提供更高的效率。我们建议创建一个软件工具工具箱,以推进以下领域 计算系统生物学。我们已经确定了现有基于规则的软件能力中的差距,并提出了一种系统的方法来填补这些差距。我们开发更高效的直接模拟工具的计划包括双管齐下的方法:在分布式计算环境中使用可用的模拟器,以及开发新的无方程计算方法,以提供更高的效率和与现有数据分析软件包的集成。在开发这个工具箱的过程中,我们将改进基于规则的建模软件;整合现有的软件工具,并开发新的敏感性和分叉分析工具以及数据拟合工具。需要这些工具,以便基于规则的建模者能够利用适合于校准基于规则的模型的参数的数据,包括高通量蛋白质组数据。还需要这些工具来诊断预测的模型行为对不确定模型参数的依赖,设计实验以减少参数估计中的不确定性,以及阐明分叉(参数空间中发生行为急剧转变的点)。我们将通过建立受体酪氨酸激酶(RTK)信号的模型并使用该模型来研究位点特异性酪氨酸磷酸化如何依赖RTK酪氨酸及其结合伙伴的性质来测试和验证这些工具。这种对驱动生物学问题的关注将确保我们的软件开发活动针对有用的能力。我们开发基于规则的建模软件工具的经验,以及新颖的方法,使我们完全有资格执行这一拟议的项目。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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William S Hlavacek其他文献

William S Hlavacek的其他文献

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{{ truncateString('William S Hlavacek', 18)}}的其他基金

System Dynamics of PD-1 Signaling in T Cells
T 细胞中 PD-1 信号传导的系统动力学
  • 批准号:
    10399590
  • 财政年份:
    2021
  • 资助金额:
    $ 33.22万
  • 项目类别:
System Dynamics of PD-1 Signaling in T Cells
T 细胞中 PD-1 信号传导的系统动力学
  • 批准号:
    10211871
  • 财政年份:
    2021
  • 资助金额:
    $ 33.22万
  • 项目类别:
Multiscale Modeling to Optimize Inhibition of Oncogenic ERK Pathway Signaling
多尺度建模优化致癌 ERK 通路信号传导的抑制
  • 批准号:
    10558581
  • 财政年份:
    2020
  • 资助金额:
    $ 33.22万
  • 项目类别:
Multiscale Modeling to Optimize Inhibition of Oncogenic ERK Pathway Signaling
多尺度建模优化致癌 ERK 通路信号传导的抑制
  • 批准号:
    10337242
  • 财政年份:
    2020
  • 资助金额:
    $ 33.22万
  • 项目类别:
Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer
自噬介导的化疗耐药肺癌生存的计算模型
  • 批准号:
    9547104
  • 财政年份:
    2017
  • 资助金额:
    $ 33.22万
  • 项目类别:
Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer
自噬介导的化疗耐药肺癌生存的计算模型
  • 批准号:
    9769647
  • 财政年份:
    2017
  • 资助金额:
    $ 33.22万
  • 项目类别:
Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer
自噬介导的化疗耐药肺癌生存的计算模型
  • 批准号:
    9139424
  • 财政年份:
    2015
  • 资助金额:
    $ 33.22万
  • 项目类别:
Hardening Software for Rule-based models-Competitive Revision
基于规则的模型的强化软件 - 竞争性修订
  • 批准号:
    10382135
  • 财政年份:
    2014
  • 资助金额:
    $ 33.22万
  • 项目类别:
Hardening Software for Rule-based Modeling
用于基于规则的建模的强化软件
  • 批准号:
    10615068
  • 财政年份:
    2014
  • 资助金额:
    $ 33.22万
  • 项目类别:
Hardening Software for Rule-based Modeling
用于基于规则的建模的强化软件
  • 批准号:
    10165739
  • 财政年份:
    2014
  • 资助金额:
    $ 33.22万
  • 项目类别:

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