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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：生物学前沿的高性能模拟框架（更新）