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ParCell: A Parallel Computation Framework for Scalable and Mechanistic Modeling and Simulation of Multicellular Systems

ParCell：用于多细胞系统可扩展和机械建模与仿真的并行计算框架

基本信息

批准号：
1609642
负责人：
Dipak Barua
金额：
$ 35万
依托单位：
Missouri University of Science and Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609642&HistoricalAwards=false
关键词：
ParCell Parallel Computation Framework Scalable

项目摘要

PI: Barua, DipakProposal Number: 1609642This multidisciplinary proposal aims to model multicellular biological systems based on modeling of the behavior of each individual cell. This research will relate sub-cellular protein interactions to full scale cell behavior, and to the behavior of a system of many cells using computational methods. Cell division, growth, death and function will be modeled. Results from this work can have an impact both on Bioengineering and on Computer Science. Traditional computational modeling and simulation methods are inadequate to address the biological complexity of cells. In recent years, considerable efforts have been devoted to developing multiscale models that capture biological complexity at distinct time and spatial resolutions. However, little progress has been made in developing multiscale models capable of integrating high resolution biological details with long-time cellular behavior. The primary goal of this proposal is to develop a parallel computation framework, called ParCell, for multiscale cell population modeling and simulation. ParCell will link subcellular biochemistry to long-time cell behavior determined by cell death, division, fate decision, and other cellular functions. Current multiscale population models mostly rely on serial computation-based simulation techniques. Such limitations and challenges prohibit the understanding and analysis of many important biological systems, such as tissue regeneration, clonal expansion of antigen-exposed immune cells, cell migration in wound healing, evolution of drug resistance in cells, and cellular phenotypes under disease and treatment conditions. ParCell will be the framework for modeling of heterogeneous multicellular systems that will link high resolution molecular details of signaling and gene transcription to evolutionary cell fate decisions and population dynamics. Current cell population models are mostly based on agent-based modeling (ABM) technique, where cells are represented as software objects or agents. Instead, it is proposed that cells will be represented as stand-alone parallel simulations (i.e., threads) rather than software objects. Using parallel computation, ParCell will systematically expand a single-cell biochemical network model, created using other software or languages, into a population model. Specifically, it will launch parallel simulations on a single-cell biochemical network model, and treat each simulation thread as an independent cell. It will also use a message passing interface (MPI) to link subcellular network dynamics (parallel thread corresponding to each cell) to cellular fate decisions and phenotypes based on model-specific (user-supplied) rules. Such distributed structure of the models combined with parallel computation will enable unprecedented scalability and mechanistic abstraction. Additionally, ParCell will use a novel load-balancing scheme for arbitrary model scalability in dynamic and heterogeneous cloud environments. The models can be made as mechanistic as any single-cell reaction network without adding model complexity or programming efforts. The PIs will leverage various summer camp programs organized by the Diversity, Outreach, and Women's Programs to recruit female and underrepresented minority students into the project.

PI：Barua，Dipak Proposal number：1609642这项多学科提案旨在基于对每个单独细胞的行为的建模来对多细胞生物系统进行建模。这项研究将使用计算方法将亚细胞蛋白质相互作用与完整的细胞行为以及由许多细胞组成的系统的行为联系起来。将对细胞分裂、生长、死亡和功能进行建模。这项工作的结果可能会对生物工程和计算机科学产生影响。传统的计算建模和模拟方法不足以解决细胞的生物学复杂性。近年来，人们致力于开发能够在不同的时间和空间分辨率下捕捉生物复杂性的多尺度模型。然而，在开发能够将高分辨率生物学细节与长期细胞行为相结合的多尺度模型方面进展甚微。该方案的主要目标是开发一个并行计算框架，称为Parcell，用于多尺度细胞种群建模和模拟。PARCEL将亚细胞生物化学与由细胞死亡、分裂、命运决定和其他细胞功能决定的长期细胞行为联系起来。目前的多尺度种群模型大多依赖于基于序列计算的模拟技术。这些限制和挑战阻碍了对许多重要生物系统的理解和分析，如组织再生、抗原暴露的免疫细胞的克隆性扩增、伤口愈合过程中的细胞迁移、细胞耐药性的演变以及疾病和治疗条件下的细胞表型。PARCEL将成为异质多细胞系统建模的框架，将信号和基因转录的高分辨率分子细节与进化的细胞命运决定和种群动态联系起来。当前的单元群体模型大多基于基于代理的建模(ABM)技术，将单元表示为软件对象或代理。相反，建议将单元表示为独立的并行模拟(即线程)，而不是软件对象。利用并行计算，Parcell将系统地将使用其他软件或语言创建的单细胞生化网络模型扩展为种群模型。具体地说，它将在单细胞生化网络模型上启动并行模拟，并将每个模拟线程视为独立的细胞。它还将使用消息传递接口(MPI)将亚蜂窝网络动态(对应于每个蜂窝的并行线程)与基于特定型号(用户提供的)规则的蜂窝命运决策和表型联系起来。这种模型的分布式结构与并行计算相结合，将实现前所未有的可扩展性和机械抽象。此外，Parcell将使用一种新颖的负载平衡方案，以在动态和异构云环境中实现任意模型的可扩展性。这些模型可以像任何单细胞反应网络一样机械化，而不需要增加模型的复杂性或编程工作。PIS将利用多样性、外展和妇女方案组织的各种夏令营计划，招募女性和代表性不足的少数族裔学生加入该项目。