WESTPA: A high-performance framework for simulating at the frontiers of biology

WESTPA：用于模拟生物学前沿的高性能框架

基本信息

批准号：
10734236
负责人：
LILLIAN T CHONG
金额：
$ 38.56万
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 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734236
关键词：
2019-nCoV Age Algorithms Amber Biological Biology Budgets Calcium Cell Membrane Permeability Cell model Cells Cloud Computing Communities Computer software Computers Development Diffusion Documentation Drug Design Educational workshop Effectiveness Engineering Environment Equilibrium Event Family Infrastructure Kinetics Mathematics Membrane Methodology Modeling Modernization Molecular Nuclear Pore Complex Pathologic Pathway interactions Peer Review Performance Persons Pharmaceutical Preparations Production Proteins Reaction Research Research Personnel Ribosomes Running Sampling Scheme Scientist Series Silicon Software Tools Statistical Computing Stress Tests Supercomputing Testing Time Training Vesicle Virus Diseases cell behavior computational platform conformational conversion flexibility frontier improved interoperability machine learning framework machine learning pipeline model building molecular scale next generation outreach parallelization simulation software development supercomputer tutorial review virtual

项目摘要

WESTPA: A high-performance framework for simulating at the frontiers of biology (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 large conformational transitions in proteins, drug membrane permeation, or transitions of cell behavior from healthy to pathological states. The WESTPA software package is a powerful ‘meta tool’—software that controls other software–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 appears to be the most commonly used platform for simulating ‘rare events’ at different scales. WESTPA controls existing dynamics engines by orchestrating up to thousands of trajectories run natively by those packages at any scale (e.g., OpenMM, 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 and supercomputing platforms, engineering interoperability with state-of-the-art machine learning frameworks, and improving its ease of use; (ii) to make algorithmic advances to enable applications to ever more challenging problems; (iii) to demonstrate the effectiveness of WESTPA through a series of “showcase” examples from molecular to cellular scale using a variety of dynamics engines; and (iv) to continue offering individualized guidance to researchers, user support, training tutorials, and workshops. 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.

Westpa：在生物学边界模拟的高性能框架（续签）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，关键事件可能会出现在模拟时间表之外 - 例如大构象蛋白质，药物膜渗透或细胞行为从健康到病理状态。 Westpa软件包是一个强大的“元工具” - 控制权其他软件 - 可以进行可能的计算给定计算预算，同时让研究人员继续使用模拟引擎和模型他们的选择。 Westpa似乎是模拟“罕见事件”的最常用平台不同的尺度。 Westpa通过编排多达数千个来控制现有的动态引擎轨迹在任何规模上由这些包装本地运行（例如OpenMM，Amber，Bionetgen，McEll）使用“加权合奏”策略。 Westpa不仅可以自动使使用动态引擎 - 但由于添加轨迹的统计过程，删除后，Westpa可以获得关键动力学和相等溴的观察值的估计值明显更少计算时间比普通并行化所需的时间。提案的目的是（i）优化用于云计算和超级计算平台的WESTPA，工程互操作性最先进的机器学习框架，并提高其易用性；（ii）制作算法促进申请的进步越来越挑战问题；（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；（iv）继续提供个性化的指导研究人员，用户支持，培训教程和研讨会。目标的完成将使调查人员，他们的实验和计算合作者以及世界各地的用户在多个尺度上对生物模拟的毛刺场做出广泛的贡献。