Modeling Multiscale Control of Liver Regeneration

项目总结 这个合作的U01项目的目标是开发一种新的多尺度建模框架，该框架采用 利用由单小区数据集提供的关于蜂窝功能状态的深入信息。新兴 针对数百到数千种高通量分子检测的技术和分析方法 单细胞使人们对细胞的异质性、层次性和复杂性有了前所未有的了解 功能状态。存在对系统方法的未得到满足的需求，以便在 多尺度建模框架。我们这个影响广泛的项目的中心创新理念是充分实现 通过开发细胞功能状态和状态的模型来开发这些新的单细胞数据集的潜力 过渡到分子和组织尺度与生理尺度功能之间的桥梁。我们将重点关注 跟随前沿挑战：针对大数据的新计算建模方法 多个尺度上的同步数据来源。我们将开发建议的多尺度建模框架 在理解控制组织对损伤的协调反应的控制原则的背景下。我们的 方法包括明确说明免疫、间质、内皮和上皮细胞的功能状态 细胞，以及驱动状态转变的假定分子过程，对多种组织具有广泛的适用性 维修场景。组织修复过程的复杂性使得在纯粹的定性分析中， 以确定多尺度分子、细胞和生理因素如何、程度和时间 有助于对整个过程的协调控制。我们将重点关注肝脏再生的过程，因为 一个测试平台，用于全面开发、微调和演示我们的多尺度建模方法 应用范围更广，实用性更强。我们已经招募了一支合作的调查团队，他们的专长是 计算建模、高通量单细胞尺度分子分析、体内操作、活体内 用于生理标度分析的成像和非侵入性方法。我们的跨学科项目努力是 按照三个目标组织：目标1开发一个数学框架来模拟分子网络和 用于预测单个确定的分子机制对细胞规模影响的细胞功能状态 单元格数据集。目的2将分子和细胞网络模型与空间组织模型相结合 微结构和代谢能力预测肝脏损伤反应的生理后果。目标 3对网络中关键机制和动态变化的多尺度模型进行评估和实验测试 平衡，提供对肝脏损伤的再生反应的控制原理的洞察。 AIMS的成功完成将产生一种优化的方法，以利用#年的单单元数据集 多尺度建模。我们将积极参加多尺度建模联盟工作组， 包括医疗保健建模与仿真可信实践委员会、多尺度系统生物学委员会、 模型和数据共享，以及临床和翻译问题。

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.