Network Modeling

五. TR&D3 -摘要 大型项目提供的单细胞成像和生化数据， NIH LINCS强调了对模型的需求，这些模型可以预测信号蛋白在细胞内的动力学。 整个细胞的规模，包括潜在的数百万个单独的大分子， 从几分钟到几小时的时间尺度。虽然TR&D2在空间逼真方面取得了重大进展， 模拟突触事件和相关的树突结构变化，因为我们试图解决 问题在更高的尺度在细胞的多样性，需要开发可扩展的方法，虽然 在较低的分辨率下，已经变得明显。为了满足这些需求，我们提出了一个新的 TR&D，TR & D3，将侧重于开发方法和开发软件， 管理，有效的模拟和分析分子相互作用的网络模型， 牢房由于标准常微分方程（ODE）固有的局限性， 为了处理生物复杂性，我们将采用并进一步发展基于规则的方法 建模（RBM）工具，例如我们广泛使用的BioNetGen软件，它提供了 这是这种努力的理想基础。成果管理制包括基于ODE的动态，但也 更广泛，因为它为高度复杂的系统提供了重要的优势：面向对象的 一种表示生物分子及其相互作用的方法，提供直观的 可视化功能，便于模型注释和比较，并可能支持 在广泛的空间分辨率模拟。规则的无网络随机模拟 的模型提供了一个很好的起点，为进一步发展的高效 模拟方法能够解决空间和分子复杂性的全部范围。 我们的网络建模工作由七个驱动生物医学项目中的六个驱动， 与其他TR& D的努力紧密结合。我们的目标是提供机械的见解， 在多个尺度和许多不同的细胞环境中，包括神经元，免疫细胞， 和癌细胞。我们的目标是（1）推进RBM技术，以开发高效的细胞规模 BioNetGen和NFsim中的模拟，（2）进一步开发RuleBender作为接口， 高效的可视化和模型构建、管理和分析，以及（3）提供一个强大的 将RBM技术与MMBioS开发的其他技术相结合的软件基础设施， 使社区能够广泛使用，提供对匹兹堡超级计算的访问 Center的Bridges系统用于高性能计算（HPC）。

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).