Competetive Renewal of MSM: Multiscale Studies of Segmentation in Vertebrates

描述(申请人提供)：脊柱和骨骼肌肉起源于胚胎结构，称为体节，从头到尾依次形成。正常的肌肉骨骼发育需要每个体节中正确的细胞数量，以及每个体节获得正确的轴向地址。干扰可导致脊柱畸形，范围从脊椎元素的完全紊乱(例如脊柱肋骨和脊柱胸椎发育不良和发育不良)到脊柱的偏斜(前凸、脊柱后凸和脊柱侧弯)以及骨骼元素的区域身份的错误指定(例如Klippel-Feil综合征)。过去二十年的研究已经发现了分子振荡器和生长因子的梯度，假设可以控制体节的大小和身份。然而，关于这些分子参与者如何(或是否)与导致体节图案的细胞黏附、增殖和迁移等细胞行为有关的信息知之甚少。这种不确定性在一定程度上是由于体细胞发生的时空复杂性、涉及的机制的数量以及过去模型和实验之间相对缺乏相互作用。该项目采用多尺度方法来解决所有三个问题。由美国国立卫生研究院领导的跨机构建模和分析小组(IMAG)已将开发开源、多尺度生物模拟环境和部署整合不同尺度运行模型的示范项目确定为关键目标。该项目将建立全面的脊椎动物体细胞发生的3D多尺度预测模型，能够生成和测试关于物种间差异(作为个体对个体的变异性和稳健性的模型)和扰动机制的特定假说。它将改进组织模拟环境(CC3D)以提高其对社区的可用性，进行新的生物实验以收集数据作为3D体细胞发生模型的输入并测试模型预测，并使用多细胞信息共享的新兴标准(CBO、CBMSL)部署模型和实验数据。具体地说，它将：1)开发新的3D模型，整合分子、细胞和组织尺度上的行为，以再现正常的分割动力学，并通过使用先进的延时荧光显微镜和基于微流体的梯度细胞技术进行定量测量来测试它们；2)使用新的实验范式，允许独立于分子“分割时钟”来研究分割，以挑战和验证分割模型；3)扩展模型和实验，以了解体节如何获得位置一致性；4)以可共享的格式发布开源数据和模型。除了生成脊柱发育及其异常的预测模型外，该项目还应使未来能够研究其他器官的发育，并确立多尺度建模在生物医学科学中的作用。它对模型和数据共享能力的重视将促进生物建模人员和实验人员之间有效地共享资源、工具和模型，大大减少重复劳动。

DESCRIPTION (provided by applicant): The vertebral column and skeletal musculature derive from embryonic structures called somites, which form sequentially from head to tail. Normal musculoskeletal development requires the correct number of cells in each somite and that each somite acquires its correct axial address. Perturbations can lead to malformations of the spine ranging from complete disarray of the vertebral elements (e.g. spondylocostal and spondylothoracic dysostoses and dysplasias) to deviations of the spine (lordosis, kyphosis, and scoliosis) and misspecification of the regional identity of skeletal elements (e.g. Klippel-Feil syndrome). Research in the last two decades has uncovered molecular oscillators and gradients of growth factors hypothesized to control somite size and identity. However, little is known about how (or if) these molecular players relate to cell behaviors like cell adhesion, proliferation and migration that result in somite patterning. In part this uncertainty is due to the spatiotemporal complexity of somitogenesis, the number of mechanisms involved and the relative lack of cross-talk between model and experiment in the past. This project undertakes a multiscale approach to address all three issues. The NIH-led Interagency Modeling and Analysis Group (IMAG) has identified as key goals the development of open-source, multi-scale biological simulation environments and the deployment of demonstration projects that integrate models operating at different scales. This project will build comprehensive 3D multiscale predictive models of vertebrate somitogenesis able to generate and test specific hypotheses concerning the mechanisms of interspecies differences (as a model of individual to individual variability and robustness) and perturbations. It will refine a tissue simulation environment (CC3D) to improve its usability to the community, perform new biological experiments to collect data as inputs for 3D somitogenesis models and to test model predictions, and deploy models and experimental data using emerging standards for sharing of multicellular information (CBO, CBMSL). Specifically, it will: 1) develop new 3D models to integrate behaviors at molecular, cellular and tissue scales to reproduce the normal dynamics of segmentation and test them by quantitative measurements using advanced time-lapse fluorescence microscopy and microfluidics-based gradient-cell technology; 2) use a novel experimental paradigm that allows segmentation to be studied independently of the molecular "segmentation clock", for challenging and validating the segmentation models; 3) extend the models and experiments to understand how somites acquire positional identities and 4) open-source release data and models in sharable formats. In addition to generating a predictive model for vertebral column development and its anomalies, this project should enable future studies of the development of other organs and establish the role of multi-scale modeling in biomedical science. Its emphasis on model and data share ability will promote efficient sharing of resources, tools and models among biomodelers and experimentalists, significantly reducing duplication of effort.