Collaborative Research: Utilization of Smart Materials and Predictive Modeling to Integrate Intracellular Dynamics with Cell Biomechanics and Collective Tissue Behavior

合作研究：利用智能材料和预测模型将细胞内动力学与细胞生物力学和集体组织行为相结合

• 批准号: 1334611
• 负责人: James Henderson
• 金额: $ 29.1万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334611&HistoricalAwards=false
• 关键词: Collaborative; Research; Utilization; Smart; Materials

The goal of this research project is to employ programmable materials from the field of biomaterials science, particle-tracking algorithms from the fields of biomechanics and biophysics, and molecular probes from the fields of cell and molecular biology to track and model the interaction of multiple intracellular components and the resultant large-scale cellular behaviors. A grand challenge in biomechanics and mechanobiology is that of understanding the complex interactions that occur between intracellular structures and how those interactions produce function at the cell and tissue levels. Through three objectives, this project will yield a method for quantitatively characterizing interactions between intracellular components and linking those patterns with large-scale behaviors such as cell polarization. First, the biomechanical sequence of cell polarization at the intracellular level will be elucidated via automated, synchronized tracking of multiple components in single cells in highly constrained environments. Second, cell polarization will be induced in individual cells using smart substrates and patterns in intracellular components will be linked to this large-scale cell behavior. Third, cell polarization will be studied and modeled, from the intracellular to multicellular levels, in a model of contact inhibition release/localized epithelial-mesenchymal transition with high cell densities. Coordinated cell movements are critical to biological processes such as embryonic development, cancer progression, and wound healing. Although cell movement is generated by structures inside cells, it is not known how interactions of those structures produce cell movement and resulting organization within groups of cells. This project will answer that question. A new computational approach will quantify the simultaneously interactions of several different important structures inside cells. New smart material will be used to trigger changes to cell movement, and the computational approach will determine how interactions of structures inside cells also change. Both low cell densities, where cells interact rarely, and high cell densities, where cells are always touching as happens in living organisms, will be studied. A model for collective movement of large groups of cells that can make predictions about tissues formation and disease will be developed.Society will benefit from the technical and professional development of the individuals involved, important advances in the fields of biomechanics, biophysics, and biology, and breakthroughs that can be anticipated in healthcare fields. Research, education, and diversity at both collaborating institutions will be further integrated through a yearly summer research program that will recruit exceptional Hampton University students as undergraduate researchers.

该研究项目的目标是采用生物材料科学领域的可编程材料，生物力学和生物物理学领域的粒子跟踪算法，以及细胞和分子生物学领域的分子探针，以跟踪和模拟多个细胞内组分的相互作用以及由此产生的大规模细胞行为。生物力学和机械生物学的一个重大挑战是理解细胞内结构之间发生的复杂相互作用以及这些相互作用如何在细胞和组织水平上产生功能。通过三个目标，该项目将产生一种方法，用于定量表征细胞内组分之间的相互作用，并将这些模式与细胞极化等大规模行为联系起来。首先，在细胞内水平的细胞极化的生物力学序列将通过在高度受限的环境中的单细胞中的多个组件的自动化，同步跟踪来阐明。第二，使用智能底物在单个细胞中诱导细胞极化，并且细胞内组分的模式将与这种大规模细胞行为相关联。第三，将在接触抑制释放/局部上皮-间充质转化的高细胞密度模型中，从细胞内到多细胞水平研究细胞极化并建模。协调的细胞运动对于胚胎发育、癌症进展和伤口愈合等生物过程至关重要。虽然细胞运动是由细胞内的结构产生的，但尚不清楚这些结构的相互作用如何产生细胞运动和细胞群内的组织。这个项目将回答这个问题。一种新的计算方法将量化细胞内几种不同重要结构的同时相互作用。新的智能材料将用于触发细胞运动的变化，计算方法将确定细胞内结构的相互作用如何变化。将研究低细胞密度（细胞很少相互作用）和高细胞密度（细胞总是接触，就像活生物体中发生的那样）。通过开发能够预测组织形成和疾病的大群细胞集体运动模型，使相关人员的技术和专业发展、生物力学、生物物理学和生物学领域的重大进展以及医疗保健领域的突破性进展都将使社会受益。两个合作机构的研究，教育和多样性将通过每年的夏季研究计划进一步整合，该计划将招募优秀的汉普顿大学学生作为本科研究人员。