DISTRIBUTED SIMULATION AND OPTIMIZATION OF GENE NETWORK MODELS

基因网络模型的分布式仿真与优化

• 批准号: 7956340
• 负责人: Jean M Peccoud
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956340
• 关键词: Address; Algorithms; Biomedical Research; Client; Communities; Computer Retrieval of Information on Scientific Projects Database; Data; Development; Environment; Equation; Funding; Genes; Grant; High Performance Computing; Hour; Institution; Manuscripts; Methods; Modeling; Research; Research Personnel; Resources; Running; Simulate; Solutions; Source; Stochastic Processes; United States National Institutes of Health; Work; biological systems; chemical kinetics; cost; network models; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Our group develops computational and experimental methods to estimate the parameters of gene network models. These models are generalization of chemical kinetics models. They can either represented by differential equations or by stochastic processes. We use optimization algorithms to fit gene network models to experimental data. The size of the parameter space of our models along with their nonlinearity can make them difficult to optimize. In situation where we have to use stochastic models to simulate the biological system that we have characterized experimentally, the computational cost of optimizing the model is even greater. Depending on the models and parameters used, a typical optimization problem can require 100s of CPU hours. In addition to getting us access to a HPC environment for our own use, I want to better understand the resources available through the TeraGrid that could be used to help bring the benefit of HPC to my scientific community. This is especially important because very few people in my field have the expertise to work in a HPC environment even though many people could benefit from it. The attached manuscript describes an application that we are about to release in which we have addressed this need by providing users with a build-in access to a cluster. Users run the application on their desktop and can elect to execute simulations on a remote machine. We have implemented this mechanism on a 8 node dedicated cluster as proof of concept but this solution is not scalable. We would like to see to what extent we could provide people in our community easy access to the TeraGrid through the development of specific client-server applications to analyze gene networks. Once we have a better understanding of the TeraGrid environment, I will submit a separate request for advanced support to analyze with the TeraGrid staff the possibility to develop a specific gateway. I dont have a specific grant associated with that request at this point. However, I need to be able to articulate how the TeraGrid fits in the research plans of many of several grants that I am currently preparing for the NIH and NSF.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 我们的小组开发了计算和实验方法来估计基因网络模型的参数。这些模型是化学动力学模型的推广。它们可以用微分方程或随机过程表示。我们使用优化算法将基因网络模型与实验数据相匹配。我们模型的参数空间的大小及其非线性使得它们难以优化。在我们必须使用随机模型来模拟我们通过实验表征的生物系统的情况下，优化模型的计算成本甚至更大。根据所使用的模型和参数，典型的优化问题可能需要数百个 CPU 小时。除了让我们能够访问 HPC 环境供我们自己使用之外，我还想更好地了解通过 TeraGrid 提供的资源，这些资源可用于帮助将 HPC 的优势带给我的科学界。这一点尤其重要，因为在我的领域中很少有人拥有在 HPC 环境中工作的专业知识，尽管许多人可以从中受益。随附的手稿描述了我们即将发布的一个应用程序，在该应用程序中，我们通过为用户提供对集群的内置访问来满足这一需求。用户在桌面上运行应用程序，并可以选择在远程计算机上执行模拟。我们已在 8 节点专用集群上实现了此机制作为概念验证，但此解决方案不可扩展。我们希望了解通过开发特定的客户端-服务器应用程序来分析基因网络，我们可以在多大程度上为社区中的人们提供轻松访问 TeraGrid 的能力。一旦我们对 TeraGrid 环境有了更好的了解，我将提交一份单独的高级支持请求，与 TeraGrid 员工一起分析开发特定网关的可能性。目前我没有与该请求相关的具体拨款。然而，我需要能够阐明 TeraGrid 如何适应我目前正在为 NIH 和 NSF 准备的多项资助的研究计划。