Modeling Multiscale Control of Liver Regeneration

肝脏再生的多尺度控制建模

• 批准号: 9768461
• 负责人: Joannes B Hoek
• 金额: $ 62.69万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9768461
• 关键词: Accounting; Activities of Daily Living; Address; Automobile Driving; Big Data; Biological Assay; Biological Process; Cell model; Cells; Clinical; Complex; Computer Simulation; Data; Data Collection; Data Set; Emerging Technologies; Endothelial Cells; Engineering; Environment; Epithelial Cells; Equilibrium; Fracture; Fracture Healing; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Healthcare; Heterogeneity; Immune; Individual; Liver; Liver Regeneration; Mathematics; Measures; Metabolic; Methods; Modeling; Molecular; Molecular Profiling; Neurons; Organ; Pathway interactions; Phenotype; Physiological; Physiology; Predisposition; Process; Regenerative response; Research Personnel; Retinal; Signal Transduction; Skin wound; Source; Stromal Cells; System; Systems Biology; Technology; Testing; Time; Tissues; Uncertainty; Wound Healing; base; body system; cartilage regeneration; cell type; data sharing; genome-wide; hematopoietic differentiation; high throughput screening; in vivo; injury and repair; innovation; insight; intravital imaging; laser capture microdissection; liver injury; liver repair; metaplastic cell transformation; models and simulation; molecular dynamics; molecular modeling; molecular scale; multi-scale modeling; network models; novel; protein expression; recruit; repaired; response; response to injury; single cell analysis; stem cell differentiation; tissue repair; working group

PROJECT SUMMARY The aim of this collaborative U01 project is to develop a novel multiscale modeling framework that takes advantage of the in depth information on cellular functional states provided by single cell data sets. Emerging technologies and analysis methods aimed at high-throughput molecular assays of hundreds to thousands of single cells have enabled an unprecedented view of the heterogeneity, hierarchy and complexity of cellular functional states. There is an unmet need for systematic methods to utilize such information-rich data sets in a multiscale modeling framework. The central innovative idea of our project with broad impact is to realize the full potential of these novel single cell data sets by developing models of cellular functional states and state transitions to bridge the molecular and tissue scales with physiological scale functions. We will focus on the following Cutting Edge Challenge: Novel computational modeling approaches for big data that account for simultaneous sources of data on multiple scales. We will develop the proposed multiscale modeling framework in the context of understanding the control principles governing the coordinated tissue response to injury. Our approach involves explicit accounting of cellular functional states of immune, stromal, endothelial and epithelial cells, and putative molecular processes driving the state transitions, with broad applicability to multiple tissue repair scenarios. The complexity of the tissue repair process makes it difficult, in a purely qualitative analysis, to identify how, to what extent, and at what time the multiscale molecular, cellular and physiological factors contribute to the coordinated control of the entire process. We will focus on the process of liver regeneration as an enabling testbed in order to fully develop, fine tune and illustrate our multiscale modeling approach for broader application and utility. We have recruited a collaborative team of investigators with expertise in computational modeling, high-throughput single cell scale molecular assays, in vivo manipulation, intravital imaging, and non-invasive methods for physiological scale analysis. Our cross-disciplinary project efforts are organized along three Aims: Aim 1 Develop a mathematical framework to model molecular networks and cellular functional states for predicting the cellular scale impact of molecular mechanisms identified by single cell data sets. Aim 2 Integrate the molecular and cellular network model with a model of spatial tissue microarchitecture and metabolic capacity to predict physiological consequences of response to liver injury. Aim 3 Evaluate and experimentally test the multiscale model for key mechanisms and dynamic shifts in network balances that provide insights into the control principles of the regenerative response to injury in the liver. Successful completion of the aims will yield an optimized approach for utilizing single cell data sets in multiscale modeling. We will actively participate in the Multiscale Modeling Consortium working groups, including Committee on Credible Practice of Modeling & Simulation in Healthcare, Multiscale Systems Biology, Model and Data Sharing, and Clinical and Translational Issues.

项目摘要 该协作U01项目的目的是开发一个新颖的多尺度建模框架 单个细胞数据集提供的细胞功能状态的深入信息的优势。新兴 技术和分析方法旨在用于数百至数千的高通量分子测定 单细胞已经使蜂窝的异质性，层次结构和复杂性具有前所未有的视图 功能状态。系统的方法未满足系统的需求，可以在一个 多尺度建模框架。我们项目具有广泛影响的中心创新思想是实现全部 这些新型单细胞数据集的潜力通过开发细胞功能状态和状态的模型 过渡到桥接分子和组织尺度的生理尺度功能。我们将专注于 以下尖端挑战：大数据的新型计算建模方法 在多个量表上同时数据源。我们将开发提出的多尺度建模框架 在理解控制组织对损伤的协调组织反应的控制原理的情况下。我们的 方法涉及免疫，基质，内皮和上皮的细胞功能状态的明确核算 细胞和推定的分子过程驱动状态转变，对多个组织具有广泛的适用性 维修方案。在纯粹的定性分析中，组织修复过程的复杂性使其变得困难 确定多尺度分子，细胞和生理因素的程度，何时何时，什么时候 有助于对整个过程的协调控制。我们将专注于肝脏再生过程 启用测试床，以充分开发，微调和说明我们的多尺度建模方法 更广泛的应用和实用程序。我们已经招募了一个具有专业知识的调查员团队 计算建模，高通量单细胞尺度分子测定，体内操作，插入 成像和非侵入性方法用于生理规模分析。我们的跨学科项目的工作是 沿三个目的组织：目标1开发一个数学框架来建模分子网络和 细胞功能状态，以预测单个分子机制的细胞尺度影响 单元数据集。 AIM 2将分子和细胞网络模型与空间组织模型相结合 微体系结构和代谢能力预测对肝损伤反应的生理后果。目的 3评估和实验测试多尺度模型的关键机制和动态变化 为肝脏损伤的再生反应的控制原理提供了洞察力的平衡。 成功完成目标将产生一种优化的方法，用于利用单个单元数据集 多尺度建模。我们将积极参加多尺度建模联盟工作组， 包括医疗保健建模和模拟实践委员会，多尺度系统生物学， 模型和数据共享以及临床和翻译问题。