APPLICATION OF HIGH-PERFORMANCE COMPUTING TO THE RECONSTRUCTION, ANALYSIS, AND

高性能计算在重建、分析和预测中的应用

• 批准号: 8171929
• 负责人: RICK L. STEVENS
• 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/8171929
• 关键词: Algorithms; Animal Model; Anti-Bacterial Agents; Area; Biochemical Pathway; Biology; Chemicals; Collection; Communities; Computer Retrieval of Information on Scientific Projects Database; Computer software; Conditioned Culture Media; Custom; Data; Data Collection; Engineering; Environment; Equilibrium; Flow Cytometry; Funding; Future; Genes; Genome; Grant; Growth; High Performance Computing; Hour; Institution; Knock-out; Manuals; Metabolic; Methods; Microfluidic Microchips; Modeling; Organism; Phenotype; Process; Prokaryotic Cells; Publishing; Research; Research Personnel; Resources; Shotgun Sequencing; Simulate; Source; Technology; United States National Institutes of Health; Update; computing resources; drug development; genome sequencing; high throughput technology; microorganism; open source; reconstruction; 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. The field of biology is undergoing a fundamental shift from a data poor field to a data rich field thanks to the advent of numerous new high-throughput technologies for the collection of experimental data: Shotgun sequencing, Pyrosequencing, Microarrays, ChIP-chip, Biolog phenotyping arrays, microfluidic devices, and flow cytometry. Today simulation is struggling to keep pace with data collection in many areas, and nowhere is this more evident than in genome-sequencing versus genome-scale modeling. While over 800 prokaryotic genomes have been sequenced in the past ten years, only 30 genome-scale metabolic models have been published, and the pace of genome-sequencing continues to increase threatening to extend this already massive gap. Today, the paradigm of the genome-scale metabolic modeling community is that it requires a year or more of manual effort to produce a new model of a microorganism. However, technologies have emerged in recent years that make it possible to automate or expedite various steps of the genome-scale reconstruction process, and we have recently tied these technologies together into an automated genome-scale model reconstruction pipeline. While this pipeline makes it possible to construct a single model in one to five days, extensive computation is required in this reconstruction process. We are proposing to use the computational resources in the TerraGrid to apply this reconstruction process to build new genome-scale metabolic models for every prokaryote with a completely sequenced genome. We then plan to use these models in a number of high-impact scientific studies including: (1) simulating the knockout of every metabolic gene to study the robustness of the metabolic networks of these organisms and identify new potential targets for future antibacterial drug development, (2) simulating growth of each microorganism in a variety of chemical conditions to identify the environments in which various communities of microorganisms are capable of surviving, (3) predicting the minimal defined media conditions that are required in order to culture each modeled organism, and (4) simulating the engineering of each modeled organism to produce organic compounds of industrial value from a variety of renewable raw materials. While these studies produce very different results and appeal to fundamentally different application areas, they all can be accomplished by applying the Flux Balance Analysis method to the genome-scale metabolic models we will be constructing. The primary algorithm we will be using in the proposed reconstruction and analysis of genome-scale models is flux balance analysis. The most significant computation involved in this algorithm is the solving of a linear or mixed integer linear optimization problem. Fortunately, numerous open source software is available for solving linear and mixed integer linear optimization problems. We will be applying the GLPK, SCIP, and BCP solvers along with our own custom built MPI-ready FBA software to perform all of the proposed reconstruction and analysis calculations. In total, we expect that 104 distinct mixed integer linear optimization problems and 1012 distinct linear optimization problems will need to be solved in the first year of this project, requiring a total of 1.5 million CPU hours. In the two following years, we anticipate an equal number of simulations will be required due to the release of additional sequenced organism and the update of the annotations in the existing organisms.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 由于众多新的高通量技术的出现，生物学领域正在从数据贫困领域转移到数据丰富的领域，以收集实验数据：shot弹枪测序，pyrosequencing，pyrosequencing，pyrosequencing，chip-chip，chip-chip，chip-chip，chip-chip，生物学表型阵列，微流体阵列，微流体设备和流动细胞仪。如今，模拟正在努力与许多领域的数据收集保持同步，而且在基因组测序与基因组尺度建模中，这一点都没有什么比这更明显的了。尽管在过去十年中已经测序了800多个原核生物基因组，但仅发表了30个基因组级代谢模型，并且基因组测序的速度继续增加威胁，以扩大这一巨大的差距。如今，基因组规模的代谢建模社区的范式是，它需要一年或更多的手动努力来生产微生物的新模型。但是，近年来已经出现了技术，使得可以自动化或加快基因组规模重建过程的各个步骤，并且我们最近将这些技术捆绑在一起，成为自动化的基因组规模模型重建管道。尽管该管道可以在一到五天内构建单个模型，但在此重建过程中需要进行大量计算。我们建议使用Terragrid中的计算资源应用此重建过程，以为每个具有完全测序基因组的原核生物构建新的基因组级代谢模型。然后，我们计划在许多高影响力的科学研究中使用这些模型，包括：（1）模拟每个新陈代谢基因的敲除，以研究这些生物体的代谢网络的稳健性，并确定未来抗菌药物发展的新潜在目标，（2）各种化学条件中每个微型有机化的生存，以确定各种环境的成长，以确定各种能力的生存。为了培养每个建模生物体，所需的最小定义媒体条件，以及（4）模拟每个建模生物的工程，以从多种可再生原材料中产生工业价值的有机化合物。尽管这些研究产生了非常不同的结果并吸引了从根本上不同的应用领域，但它们都可以通过将通量平衡分析方法应用于我们将要构建的基因组规模代谢模型来实现。我们将在建议的重建和基因组规模模型的分析中使用的主要算法是通量平衡分析。该算法中涉及的最重要的计算是解决线性或混合整数线性优化问题。幸运的是，可用于求解线性和混合整数线性优化问题的许多开源软件。我们将应用GLPK，SCIP和BCP求解器以及我们自己的自定义MPI Ready FBA软件来执行所有建议的重建和分析计算。总的来说，我们预计将需要在该项目的第一年中解决104个不同的混合整数线性优化问题和1012个不同的线性优化问题，需要总共150万个CPU小时。在接下来的两年中，我们预计由于释放了其他测序有机体以及现有生物体中注释的更新而需要相等数量的仿真。