Computational Cell Motility Model Educed from Single-Cell and High-Throughput Phenotype Analysis

细胞的机械特性和机械感觉对细胞的特性和动态平衡至关重要。广泛的研究表明，细胞的各种机械性能与其分化、调节基因表达和指导运动的能力之间存在相关性。这些过程在胚胎发育过程中是至关重要的，而且似乎是癌症的中心。虽然机械测量、生物化学和运动性与癌症行为之间的相关性正在建立，但对这些过程中的力学缺乏完整的理解。在这个项目中，研究人员开发了一个综合的计算模型，它将结合生物化学、细胞结构和细胞运动动力学行为，并能够计算通过实验技术测量的机械性能。这将使研究团队首先了解潜在的生物现象，其次了解这些工具如何将生物化学和基因组学与细胞力学联系起来。此外，为了充分表征力学在细胞过程中的作用，该项目将把高通量方法引入细胞生物物理学。这将使研究人员能够将基因组学和蛋白质组学与癌症光谱和细胞/组织模型中细胞的机械特性联系起来。该项目的结果将为在癌症诊断和制定治疗策略中使用实验工具提供指导。细胞运动模型已经建立了六个主要的生物物理成分/现象：A.片状脂体伸展，B.皮质肌动蛋白流动，C.膜张力，D.牵引力，E.由活性凝胶组成的中心区域，以及F.a粘弹性核。最后，细胞感觉到它们的机械环境和施加在它们身上的力，这些信号可以指导它们的运动。这些现象并不是独立的，对细胞突起前沿的作用的理解必须与细胞后部的细胞回缩、整体形状扭曲、细胞基质作用力以及细胞内肌动蛋白的全球命运和运输相结合。目前还没有结合这些特征的计算模型来发展对运动的主要机制的自我一致的理解。相应地，已经发展了广泛的细胞生物物理表征技术，从原子力显微镜(AFM)的基于探针的方法，到基于主动和被动珠子的方法，牵引力测量和全局细胞力学方法。了解这些方法正在探测的细胞结构和过程依赖于生化干预和建模，该项目使用一个集成的计算模型来解释对单个细胞或细胞群体的多种机械分析。开发的计算模型将与选定的癌细胞运动模型的一系列力学和结构研究进行对比测试，研究人员的实验室将对单个细胞同时进行多种力学测量，并对细胞群体进行高通量的力学研究。这种独特的计算/实验集成方法将使细胞机械研究与基因组/蛋白质组方法相结合。

The mechanical properties and mechanical sensing of cells is critical to the identity and homeostasis of the cell. A wide range of studies have shown correlations between a variety of mechanical properties of cells and their ability to differentiate, to modulate gene expression, and to direct motility. These processes are critical for during the development of the embryo and appear to be central in cancer. While correlations are being established between the mechanical measurements, biochemistry and motility, and cancer behavior, there is a striking lack of integrated understanding of mechanics in these processes. In this project, the investigators develop an integrated computational model that will incorporate biochemistry, cell structure, and cell motility dynamical behavior with the ability to compute mechanical properties as measured by experimental techniques. This will allow the research team to understand first, the underlying biological phenomena, and second, how these tools can connect biochemistry and genomics with cell mechanics. Further, to fully characterize the role of mechanics in cellular processes, this project will bring high-throughput methods to cell biophysics. This will allow the investigators to connect genomics and proteomics to the mechanical properties of cells across the spectrum of cancers and cell/tissue models. The results of this project will provide guidance for the use of experimental tools in diagnosis of cancer and in developing strategies for treatment. Models of cell motility have established six principle biophysical constituents/phenomena: a. lamellipodia extension, b. flow of cortical actin, c. membrane tension, d. traction, e. a central region consisting of an active gel, and f. a viscoelastic nucleus. Finally, cells sense their mechanical environment and forces that are applied to them, and these cues can direct their motility. These phenomena are not independent, and an understanding of the role of the leading edge of cell protrusions have to be combined with cell retraction at the rear, global shape distortions, cell-matrix forces, and the global fate and trafficking of actin in the cell. There is no current computational model that combines these features to develop a self-consistent understanding of the primary mechanisms of motility. Correspondingly, a wide range of cell biophysical characterization techniques have been developed ranging from probe-based methods of atomic force microscopy (AFM), and active and passive bead based methods, traction force measurements and global cell mechanical methods. Understanding the cell structures and processes that are being probed by these methods depends on biochemical interventions and modeling, and this project employs an integrated computational model to interpret multiple mechanical assays on single cells or on cell populations. The computational model that is developed will be tested against a battery of mechanical and structural studies on selected cancer cell motility models, with the investigators' laboratory performing simultaneous multi-mechanical measurements on individual cells, and performing high-throughput mechanical studies on cell populations. This unique integrated computational/experimental approach will allow cell mechanical studies to be integrated with genomic/proteomic methodologies.