Collaborative Research: Multi-Scale Modeling and Experimental Study of Hair Follicle Growth Control Mechanisms and Stem Cell Lineage Dynamics

合作研究：毛囊生长控制机制和干细胞谱系动力学的多尺度建模和实验研究

• 批准号: 1951184
• 负责人: Qixuan Wang
• 金额: $ 12万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951184&HistoricalAwards=false
• 关键词: Collaborative; Research; Multi; Scale; Modeling

Hair follicles are stem cell-rich skin mini-organs that can undergo oscillation-like cycles of regeneration throughout their lifetimes. In mammals, hairs have evolved for thermoregulation, camouflage, display, and mechanical protection. Importantly, hair follicle has emerged as a leading model system for studying general mechanisms of stem cell control, tissue patterning during morphogenesis, regeneration and aging. Though powerful, traditional reductionist research approaches are limited by technological barriers and high experiment costs. This project aims to develop an interdisciplinary approach between computational modeling and experimentations to understand crucial questions that arise from the growth control of the follicle and stem cell lineage dynamics. It is anticipated that the new mathematical model can be generalized to study multiple aspects of skin biology, beyond hair growth. The new theory on the role of cellular response to dynamic growth regulation will have wide applications to other biological growth systems. This project will also provide students with the opportunities to get exposed to state-of-the-art interdisciplinary research and will greatly promote diversity in mathematics and biological sciences by recruiting students from underrepresented groups.This project will study the control mechanisms of hair follicle growth dynamics both at spatial and temporal levels by adopting an interdisciplinary research approach between computational multiscale modeling and single-cell RNA-sequencing experiments. On the modeling side, a coupled stochastic partial differential equation sub-model for signaling dynamics and a discrete off-lattice sub-model using Voronoi tessellation technique for inter-cellular interactions will be developed. Several numerical coupling algorithms between the continuum and discrete sub-models will be evaluated, which will greatly benefit broader multiscale modeling research in biological developmental systems. On the experiment side, new single-cell RNA-sequencing data will be collected to calibrate the model and validate model predictions. Using this interdisciplinary approach, a novel growth control mechanism will be verified, which states that cells’ heterogeneous responses to signals play a critical regulatory role in the follicle growth dynamics. This project will provide significant insight into the biological problems, including spatially defining the maximum length of a follicle, temporally regulating the periodic follicle growth dynamics and mechano-chemical coupling in biological growth. Finally, by merging multiscale modeling with single-cell RNA-sequencing results, this project will also demonstrate a new path for synthesizing modeling and biological data in broader interdisciplinary research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

毛囊是富含干细胞的皮肤微型器官，在其一生中可以经历类似振荡的再生周期。在哺乳动物中，毛发已经进化为体温调节，伪装，展示和机械保护。重要的是，毛囊已经成为研究干细胞控制、形态发生、再生和衰老过程中组织图案形成的一般机制的主要模型系统。传统的还原论研究方法虽然强大，但受到技术壁垒和高昂实验成本的限制。该项目旨在开发计算建模和实验之间的跨学科方法，以了解卵泡生长控制和干细胞谱系动力学中出现的关键问题。预计新的数学模型可以推广到研究皮肤生物学的多个方面，而不仅仅是毛发生长。这一关于细胞反应在动态生长调节中的作用的新理论将对其他生物生长系统具有广泛的应用。本项目还将为学生提供接触最先进的跨学科研究的机会，并通过招收代表性不足的学生来极大地促进数学和生物科学的多样性。本项目将采用计算多尺度建模和单细胞RNA之间的跨学科研究方法，在空间和时间水平上研究毛囊生长动力学的控制机制。测序实验在建模方面，将开发用于信号动力学的耦合随机偏微分方程子模型和用于细胞间相互作用的使用Voronoi镶嵌技术的离散非格子子模型。对连续体和离散子模型之间的几种数值耦合算法进行了评价，这将极大地有益于生物发育系统中更广泛的多尺度建模研究。在实验方面，将收集新的单细胞RNA测序数据，以校准模型并验证模型预测。使用这种跨学科的方法，将验证一种新的生长控制机制，该机制表明细胞对信号的异质性反应在卵泡生长动力学中起着关键的调节作用。该项目将为生物学问题提供重要的见解，包括空间上定义卵泡的最大长度，时间上调节周期性卵泡生长动力学以及生物生长中的机械化学耦合。最后，通过将多尺度建模与单细胞RNA测序结果相结合，该项目还将展示在更广泛的跨学科研究中综合建模和生物数据的新途径。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mathematical modeling of chemotaxis guided amoeboid cell swimming

趋化引导变形细胞游泳的数学模型

• DOI: 10.1088/1478-3975/abf7d8
• 发表时间: 2021
• 期刊: Physical Biology
• 影响因子: 2
• 作者: Wang, Qixuan;Wu, Hao
• 通讯作者: Wu, Hao