Computational Biology in Systems Immunology and Infectious Disease Modeling

系统免疫学和传染病建模中的计算生物学

• 批准号: 9354856
• 负责人: Martin Meier-Schellersheim
• 金额: $ 110.22万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 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/9354856
• 关键词: Affect; Algorithms; Behavior; Binding; Binding Sites; Biochemical; Biological; Biological Models; Cells; Cellular biology; Communication; Complex; Computational Biology; Computational Technique; Computer Simulation; Computer software; Data; Data Set; Databases; Development; Diffusion; Dose; Engineering; Formulation; Geometry; Goals; Imagery; Immune; Immune response; Immune system; Immunology; Individual; Intervention; Laboratories; Language; Measurement; Mediating; Methods; Modeling; Modification; Molecular; Morphology; Organ; Pathway interactions; Persons; Phosphorylation; Proteomics; Reaction; Receptor Signaling; Research; Resolution; Risk; Shapes; Signal Transduction; Signaling Molecule; Stimulus; System; Systems Biology; T-Cell Receptor; Technology; Testing; Time; Tissues; Toll-like receptors; Translating; Translations; Whole Organism; Work; abstracting; base; cell behavior; graphical user interface; improved; infectious disease model; information display; model building; particle; pathogen; receptor; response; signal processing; simulation; simulation software; tool

Modern technology now allows the analysis of immune responses and host-pathogen interactions at a global level, across scales ranging from intracellular signaling networks, to individual cell behavior, to the functioning of a tissue, organ, and even the whole organism. The challenge is not only to collect the large amounts of data such methods permit, but also to organize the information in a manner that enhances our understanding of how the immune system operates or pathogens affect their hosts. Quantitative computer simulations are gaining importance as valuable tools for probing the limits of our understanding of cellular behavior. A major roadblock on the way to successful computational modeling in cell biology has been that the translation of qualitative biological models into computational models required the intervention of engineers/mathematicians as interfaces between biological hypotheses and their theoretical and computational representations. The software being developed by the computational biology group of the Laboratory of Systems Biology eliminates the necessity of having this translation done by a person and thereby reduces the risk of oversimplification of biological mechanisms or the loss of important details in the course of translation by a non-biologist. The software ("Simmune") offers an intuitive graphical interface combined with state-of-the-art simulation technology. We recently added a module that automatically translates pathway models based on bi-molecular interactions into network visualizations that interactively display information about the details of the underlying reactions, such as required phosphorylations or induced molecular state transformations. Additionally, our software makes it possible to create computer simulations that combine detailed biochemical representation of cellular signaling processes with the spatial resolution necessary to reproduce the effects of localized recruitment and organization of signaling components. We have created a database and database interface system that can couple those computational models to experimental data and externally generated proteomic information. Our simulation software has the capability to combine biochemically detailed models with simulations that include morphological cellular plasticity. This makes it possible explore the interplay between cellular signaling processes and morphological dynamics that are controlled by those signaling processes while at the same time having a potentially strong influence on them. The ability of our modeling approach to simulate this combination of biochemical and morphological dynamics is based on algorithms we developed that are capable of automatically generating computational representations of intracellular reaction-diffusion networks. The input data provided by the user of our software consist of specifications of interactions between molecular binding sites and the modifications the interacting molecules undergo as a result of the interaction. These inputs - for which our software offers an intuitive graphical interface - are automatically translated into reaction-diffusion networks that reflect the specific geometry of the simulated cells. When the cells change their morphologies in the course of a simulation, the networks can, again automatically, be adjusted to reflect the new cellular shapes. We also develop components for this software that permit exploring the behavior of computational models over a wide range of parameter values to test whether a given model can reproduce experimental data, such as dose-response measurements, when its parameters are constrained only by what are considered physiologically reasonable ranges. In contrast to the commonly held assumption that a computational model can reproduce any data when it contains more than a handful of parameters, we found that even quite comprehensive models built with only mechanistic molecular interactions frequently fail to reproduce experimental data sets when these are sufficiently rich with regard to their dynamical or dose-dependent features. To improve the possibilities for model exchange between different modeling efforts we contributed to the development of a new standard for encoding multi-component / multi-state molecular complexes in SBML (Systems Biology Markup Language). Finally, we developed a highly efficient stochastic particle-based simulation algorithm that combines components from Brownian Dynamics approaches and Greens Function Reaction Dynamics to permit large time steps (for maximal efficiency) while maintaining a high degree of precision.

现代技术现在可以在全球水平上分析免疫反应和宿主与病原体的相互作用，范围从细胞内信号网络到单个细胞行为，再到组织、器官甚至整个有机体的功能。挑战不仅是收集这种方法允许的大量数据，而且还在于组织信息的方式，以增强我们对免疫系统如何运行或病原体如何影响宿主的理解。 定量计算机模拟作为探索我们对细胞行为理解的极限的有价值的工具，正变得越来越重要。细胞生物学中成功的计算建模的一个主要障碍是，将定性的生物学模型转换为计算模型需要工程师/数学家的干预，因为生物学假说及其理论和计算表示之间的接口。由系统生物学实验室的计算生物学小组开发的软件消除了由人进行翻译的必要性，从而降低了生物机制过于简化或非生物学家在翻译过程中丢失重要细节的风险。该软件(“免疫”)提供了一个直观的图形界面与最先进的模拟技术相结合。我们最近增加了一个模块，可以自动将基于双分子相互作用的途径模型转换为网络可视化，以交互方式显示有关潜在反应的细节信息，如所需的磷酸化或诱导的分子状态转换。 此外，我们的软件使创建计算机模拟成为可能，将细胞信号过程的详细生化表示与必要的空间分辨率相结合，以再现信号组件的局部招募和组织的效果。我们已经创建了一个数据库和数据库接口系统，可以将这些计算模型与实验数据和外部生成的蛋白质组信息相结合。 我们的模拟软件能够将生化细节模型与包括形态细胞可塑性的模拟结合起来。这使得探索细胞信号过程和由这些信号过程控制的形态动力学之间的相互作用成为可能，同时对它们产生潜在的强大影响。我们的建模方法模拟生化和形态动力学的这种组合的能力是基于我们开发的算法，该算法能够自动生成细胞内反应-扩散网络的计算表示。我们软件的用户提供的输入数据包括分子结合位点之间的相互作用的规范以及相互作用的分子作为相互作用的结果所经历的修饰。这些输入-我们的软件为其提供了直观的图形界面-自动转换为反应扩散网络，反映了模拟细胞的特定几何形状。当细胞在模拟过程中改变其形态时，网络可以再次自动调整以反映新的细胞形状。 我们还为该软件开发了组件，允许在广泛的参数值范围内探索计算模型的行为，以测试当给定模型的参数仅受被认为是生理合理范围限制时，该模型是否可以重现实验数据，例如剂量响应测量。与通常认为计算模型可以重现任何数据的假设相反，我们发现，即使是只用机械分子相互作用建立的相当全面的模型，也经常无法重现实验数据集，当这些数据集的动力学或剂量依赖特征足够丰富时。 为了提高不同建模工作之间模型交换的可能性，我们促进了用SBML(系统生物学标记语言)编码多组分/多态分子复合体的新标准的开发。 最后，我们开发了一种高效的基于随机粒子的模拟算法，它结合了布朗动力学方法和格林函数反应动力学方法的组件，在保持高精度的同时允许大的时间步长(以获得最大的效率)。