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: 10272150
• 负责人: Martin Meier-Schellersheim
• 金额: $ 166.31万
• 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/10272150
• 关键词: Affect; Algorithms; Behavior; Binding; Binding Sites; Biochemical; Biological; Biological Models; Biological Process; Cell model; Cells; Cellular Membrane; Cellular Morphology; Cellular biology; Complex; Computational Biology; Computational Technique; Computer Models; Computer Simulation; Computer software; Cytokine Receptors; Data; Data Set; Databases; Development; Diffusion; Dose; Engineering; Formulation; Geometry; Goals; Immune; Immune response; Immune system; Immunology; Individual; Intervention; Intuition; Laboratories; Language; Mathematics; Measurement; Measures; Mediating; Methods; Modeling; Modernization; Modification; Molecular; Morphology; Motion; Operating System; Organ; Pathway interactions; Persons; Phosphorylation; Phosphotransferases; Physiological; Process; Proteomics; Reaction; Receptor Signaling; Research; Resolution; Risk; Shapes; Signal Pathway; Signal Transduction; Signaling Molecule; Stimulus; Surface; System; Systems Biology; T-Cell Receptor; T-Lymphocyte; Technology; Testing; Time; Tissues; Toll-like receptors; Translating; Translations; Visualization; Whole Organism; Work; base; cell behavior; cytokine; graphical user interface; improved; infectious disease model; information display; intercellular communication; interest; macrophage; model building; particle; pathogen; receptor; recruit; response; simulation; simulation software; tool; transcription factor

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 Immune System 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. Recently, we were able to extend the capabilities of this simulation approach to include stochastic particle motion on curved surfaces as models of cellular membranes. In order to generate the necessary quantitative data to support the type of mechanistic models of signaling processes that can be built with our modeling approach (Simmune) we have recently begun performing systematic quantitative experimental measurements of intracellular signaling processes. For example, we measure the time courses of the activation of kinases and transcription factors downstream of cytokine and Toll-like receptors. We perform such measurements for varying doses of receptor stimuli to test whether our models behave correctly over a broad range of stimulation strengths and also analyze the effects to crosstalk that can occur when several pathways are activated simultaneously.

现代技术现在允许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个体细胞行为，再到组织、器官甚至整个生物体的功能。挑战不仅在于收集这些方法所允许的大量数据，而且还在于以一种增强我们对免疫系统如何运作或病原体如何影响其宿主的理解的方式组织信息。 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越来越重要。在细胞生物学中成功进行计算建模的一个主要障碍是，将定性生物模型转化为计算模型需要工程师/数学家的干预，作为生物学假设及其理论和计算表示之间的接口。由免疫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完成这种翻译的必要性，从而降低了生物机制过度简化或在非生物学家翻译过程中丢失重要细节的风险。该软件（“Simmune”）提供了一个直观的图形界面，结合了最先进的模拟技术。我们最近添加了一个模块，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细节的信息，例如所需的磷酸化或诱导的分子状态转换。 此外，我们的软件可以创建计算机模拟，将细胞信号传导过程的详细生物化学表示与再现信号传导组分的局部募集和组织的影响所需的空间分辨率相结合。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。 我们的模拟软件有能力结合联合收割机生化详细模型与模拟，包括形态细胞可塑性。这使得有可能探索细胞信号传导过程和由这些信号传导过程控制的形态动力学之间的相互作用，同时对它们具有潜在的强烈影响。我们的建模方法来模拟这种组合的生化和形态动力学的能力是基于我们开发的算法，能够自动生成细胞内反应扩散网络的计算表示。由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和相互作用分子由于相互作用而经历的修饰。这些输入-我们的软件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。 我们还开发了该软件的组件，允许探索计算模型在广泛的参数值范围内的行为，以测试给定的模型是否可以重现实验数据，如剂量-反应测量，当其参数仅受生理合理范围的限制时。与普遍持有的假设，即计算模型可以重现任何数据时，它包含了一个以上的参数，我们发现，即使是相当全面的模型只建立机械分子相互作用经常无法重现实验数据集时，这些是足够丰富的动力学或剂量依赖性的功能。 为了提高不同的建模工作之间的模型交换的可能性，我们促成了一个新的标准的开发，用于编码多组分/多态分子复合物在SBML（系统生物学标记语言）。 最后，我们开发了一种高效的随机粒子模拟算法，该算法结合了布朗动力学方法和格林函数反应动力学的组件，以允许大的时间步长（最大效率），同时保持高精度。最近，我们能够扩展这种模拟方法的能力，包括随机粒子运动的曲面作为模型的细胞膜。 为了生成必要的定量数据，以支持可以用我们的建模方法（Smimune）建立的信号传导过程的机械模型的类型，我们最近开始对细胞内信号传导过程进行系统的定量实验测量。例如，我们测量了细胞因子和Toll样受体下游的激酶和转录因子激活的时间过程。我们对不同剂量的受体刺激进行这样的测量，以测试我们的模型在广泛的刺激强度范围内是否正确，并分析当几个通路同时激活时可能发生的串扰的影响。