High-performance weighted ensemble software for simulation of complex bio-events

用于模拟复杂生物事件的高性能加权集成软件

基本信息

批准号：
10448253
负责人：
LILLIAN T CHONG
金额：
$ 28.36万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10448253
关键词：
Algorithmic Software Algorithms Amber Architecture Binding Proteins Biological Biological Process Biomedical Computing Biomedical Research Budgets Capsid Cell model Cells Cloud Computing Communication Complex Computer software Computers Data Analyses Data Storage and Retrieval Development Drug Design Educational workshop Effectiveness Environment Equilibrium Event Funding Future Heterogeneity Investigation Kinetics Malignant Neoplasms Manuals Maps Modeling Molecular Multiprotein Complexes Pathologic Pathway interactions Performance Pharmaceutical Preparations Phenotype Phosphorylation Physiological Process Proteins Research Research Personnel Running Sampling Series Signal Transduction Silicon Statistical Computing System Time Training Uncertainty Viral Virus base cell behavior combinatorial conformational conversion cost design gene network improved innovation insight interoperability knowledge base molecular dynamics molecular scale open source parallelization particle protein complex protein folding residence simulation simulation software single-cell RNA sequencing tool usability

项目摘要

High-performance weighted ensemble software for simulation of complex bio-events (Renewal) There is a “silicon ceiling” that ultimately limits many, if not most, types of dynamical biological simulations. That is, even the world’s most powerful computers cannot generate sufficiently long simulations, whether for atomistic models of proteins or for realistic models of cell behavior. In many cases, the key events may occur beyond simulation timescales – such as protein folding, conformational transitions of proteins, assembly of protein complexes, or transitions of cell behavior from healthy to pathological states. The WESTPA software package is a powerful “meta tool” which can make possible computations which otherwise would be impossible within a given computing budget - while letting researchers continue to use simulation engines and models of their choice. WESTPA controls existing dynamics engines by orchestrating up to thousands of trajectories run natively by those packages at any scale (e.g., Gromacs, Amber, BioNetGen, MCell) using a “weighted ensemble” strategy. Not only does WESTPA automatically parallelize the use of dynamics engines – but because of the statistical process by which trajectories are added and removed, WESTPA can obtain estimates of key kinetic and equilibrium observables in significantly less computing time than would be required in ordinary parallelization. The aims of the proposal are (i) to optimize WESTPA for cloud-computing, improving its ease of use, and developing highly scalable data storage analysis; (ii) to expand WESTPA’s base-computing power via algorithmic improvements; and (iii) to demonstrate the effectiveness of WESTPA through a series of “showcase” examples from molecular to cellular scale using a variety of dynamics engines. Completion of the aims will enable the investigators, their experimental and computational collaborators, and users throughout the world to make a wide range of contributions to the burgeoning field of biological simulation at multiple scales.

高性能加权合奏软件，用于模拟复杂的生物事件（续订）有一个“硅天花板”最终限制了许多（即使不是大多数）动态生物学类型的类型模拟。也就是说，即使是世界上最强大的计算机也无法产生足够长的时间模拟，无论是用于蛋白质的原子模型还是用于现实的细胞行为模型。许多人情况，关键事件可能会发生在模拟时间尺度之外 - 例如蛋白质折叠，蛋白质的构象转变，蛋白质复合物的组装或细胞行为的转变从健康到病理状态。 Westpa软件包是一个强大的“元工具”，可以做出可能在给定的计算预算中不可能的计算 - 同时让研究人员继续使用模拟引擎及其选择的模型。韦斯特帕控制现有的动态引擎，通过编排多达数千个轨迹本地运行这些包装在任何规模上（例如Gromacs，Amber，Bionetgen，McEll）都使用“加权合奏” 战略。 Westpa不仅会自动使使用动态引擎的使用并行，还因为在添加和删除轨迹的统计过程中，Westpa可以获得估计值在计算时间中，关键动力学和平等brium可观察到的计算时间明显少于普通的征服。该提案的目的是（i）优化Westpa进行云计算，提高其易用性，并开发高度可扩展的数据存储分析；（ii）扩展 Westpa通过算法改进的基础计算能力；（iii）证明 WESTPA通过一系列“展示”示例从分子到细胞量表的有效性使用各种动力引擎。目标的完成将使调查人员及其实验和计算合作者以及全球用户在多个尺度上对生物模拟的残骸领域的贡献。