MULTIPOPULATION INFLUENZA A GENOMIC EVOLUTION

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 我们已经调试了甲型流感病毒(IVA)遗传进化的随机模拟。该模型的目的是构建一个现实的模型，以便在目前作为皮特·迈达斯项目(Burke等人)一部分运行的全美人口模型中引入特定的病毒株。该模型考虑了三个遗传物质池(鸟类、猪和人类)，所有池中都发生了遗传进化。一些毒株将在池之间传播，从而在人类中引入新的毒株。代码是用Fortran 95编写的。我们目前在工作站上的单次运行时间约为48小时，因为我们希望模拟大约40年的进化。由于我们需要根据数据校准模型，因此我们希望进行大量(如果可能，则为10000)较短(5-7年)的运行，以构建真实的参数集。Run最初将独立运行。因此，最初的需求是一个没有共享内存要求的分布式平台。我们正在为MPI实施开发软件，以支持自适应参数学习--但这还没有准备好进入黄金时间。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We have debugged a stochastic simulation of genetic evolution of the influenza A virus (IVA). The purpose of the model is to a construct a realistic model to seed the introduction of specific viral strains in US-wide population models currently run as part of the Pitt MIDAS project (Burke et al.). The model considers three pools of genetic material (birds, swine and humans), with genetic evolution occurring in all pools. Some strains will get transmitted between pools, thus introducing new strains in humans. The code is written in Fortran95. Our current single-run time on a workstation is about 48 hours as we wish to simulate evolution over about 40 years. Since we need to calibrate the model to data, we wish to conduct a large number (>10000 if possible) of shorter (5-7 years) runs to construct realistic parameter sets. Runs would initially be run independently. Thus the initial need is for a distributed platform, without shared memory requirement. We are developing the software for an MPI implementation to support adaptive parameter learning- but this is not ready for prime time yet.