Network Modeling

网络建模

• 批准号: 9278821
• 负责人: James Faeder
• 金额: $ 17.94万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9278821
• 关键词: Address; Adopted; Atomizer; Automobile Driving; Binding; Biochemical; Biochemistry; Biological; Cell Size; Cell model; Cells; Cereals; Communities; Complement; Complex; Computer software; Data; Data Discovery; Databases; Development; Differential Equation; Event; Exhibits; Foundations; Funding; Generations; Geometry; High Performance Computing; Hour; Imagery; Immune; Individual; Intuition; Knowledge; Language; Libraries; Methods; Modeling; Modernization; Molecular; Molecular Models; Nature; Neurons; Pathway Analysis; Population; Reaction; Research Infrastructure; Resolution; Signaling Protein; Software Tools; Supercomputing; Synapses; System; Technology; United States National Institutes of Health; Visual; application programming interface; base; biological systems; cancer cell; cellular imaging; combinatorial; comparative; computing resources; cost; insight; interest; macromolecule; method development; model building; model development; molecular size; multi-scale modeling; network models; particle; prototype; response; simulation; software development; task analysis; tool; virtual; web portal

V. TR&D3 - Abstract The single-cell imaging and biochemical data being provided by large-scale projects such as NIH LINCS highlight the need for models that can predict the dynamics of signaling proteins on the scale of a whole cell, encompassing potentially millions of individual macromolecules on timescales of minutes to hours. While TR&D2 made major progress in spatially realistic simulations of synaptic events and associated dendritic structural changes, as we seek to tackle problems at higher scales in a diversity of cells, the need to develop scalable approaches, albeit at lower resolution, has become apparent. In response to these needs, we are proposing a new TR&D, TR&D3, that will focus on the development of methods and software for development, management, efficient simulation, and analysis of network models of molecular interactions in the cell. Because of intrinsic limitations of the standard ordinary differential equation (ODE) approach in handling biological complexity, we will adopt and further develop rule-based modeling (RBM) tools, as exemplified by our widely used BioNetGen software, which provides an ideal foundation for such an effort. RBM encompasses ODE-based dynamics but is also much broader as it offers important advantages for highly complex systems: an object-oriented approach to the representation of biomolecules and their interactions that provides intuitive visualization capabilities, facilitates model annotation and comparison, and potentially supports simulation at a wide range of spatial resolutions. Network-free stochastic simulation of rule- based models provides an excellent starting point for further development of highly-efficient simulation methods capable of addressing the full range of spatial and molecular complexity. Our network modeling efforts are driven by six of the seven Driving Biomedical Projects and are tightly integrated with the efforts of the other TR&Ds. We aim to provide mechanistic insights across multiple scales and in many different cellular contexts, including neurons, immune cells, and cancer cells. Our aims are to (1) advance RBM technology to develop efficient cell-scale simulations in BioNetGen and NFsim, (2) further develop RuleBender as an interface to enable efficient visualization and model building, managing, and analyzing, and (3) to provide a robust software infrastructure that integrates RBM technology with others developed at MMBioS and enables broad usage by the community, providing access to Pittsburgh Supercomputing Center’s Bridges system for high-performance computing (HPC).

V. TR&D3 -摘要