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: 8171879
• 负责人: Jean M Peccoud
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171879
• 关键词: Address; Algorithms; Client; Communities; Computer Retrieval of Information on Scientific Projects Database; Data; Development; Environment; Equation; Funding; Genes; Grant; 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.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们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。它们既可以用微分方程表示，也可以用随机过程表示。我们使用优化算法将基因网络模型与实验数据进行拟合。我们的模型的参数空间的大小以及它们的非线性使得它们很难优化。在我们不得不使用随机模型来模拟我们已经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，一个典型的优化问题可能需要100秒的CPU小时。除了让我们访问供我们自己使用的HPC环境外，我还想更好地了解通过TeraGrid可用的资源，这些资源可以用来帮助我的科学界带来HPC的好处。这一点尤其重要，因为在我的领域中，很少有人拥有在高性能计算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。用户在他们的桌面上运行该应用程序，并可以选择在远程机器上执行模拟。作为概念验证，我们已在8节点专用群集上实施了此机制，但此解决方案不可扩展。我们希望看到，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我们社区的人们提供对TeraGrid的轻松访问。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工作人员一起分析开发特定网关的可能性。在这一点上，我没有与该请求相关联的特定拨款。然而，我需要能够清楚地说明TeraGrid如何适应我目前为NIH和NSF准备的几项拨款中的许多研究计划。