• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>other >Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach
【2h】

Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach

机译：使用连续模型从延时图像序列中解译胚胎组织扩散的力学：一种近似贝叶斯计算方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Advanced imaging techniques generate large datasets capable of describing the structure and kinematics of tissue spreading in embryonic development, wound healing, and the progression of many diseases. These datasets can be integrated with mathematical models to infer biomechanical properties of the system, typically identifying an optimal set of parameters for an individual experiment. However, these methods offer little information on the robustness of the fit and are generally ill-suited for statistical tests of multiple experiments. To overcome this limitation and enable efficient use of large datasets in a rigorous experimental design, we use the approximate Bayesian computation rejection algorithm to construct probability density distributions that estimate model parameters for a defined theoretical model and set of experimental data. Here, we demonstrate this method with a 2D Eulerian continuum mechanical model of spreading embryonic tissue. The model is tightly integrated with quantitative image analysis of different sized embryonic tissue explants spreading on extracellular matrix (ECM) and is regulated by a small set of parameters including forces on the free edge, tissue stiffness, strength of cell-ECM adhesions, and active cell shape changes. We find statistically significant trends in key parameters that vary with initial size of the explant, e.g., for larger explants cell-ECM adhesion forces are weaker and free edge forces are stronger. Furthermore, we demonstrate that estimated parameters for one explant can be used to predict the behavior of other similarly sized explants. These predictive methods can be used to guide further experiments to better understand how collective cell migration is regulated during development.
机译：先进的成像技术可生成大型数据集，该数据集能够描述在胚胎发育，伤口愈合以及许多疾病的进展中扩散的组织的结构和运动学。这些数据集可以与数学模型集成，以推断系统的生物力学特性，通常可以为单个实验确定最佳的参数集。但是，这些方法提供的拟合鲁棒性信息很少，通常不适用于多个实验的统计测试。为克服此限制并允许在严格的实验设计中有效使用大型数据集，我们使用近似贝叶斯计算拒绝算法来构造概率密度分布，以估计定义的理论模型和实验数据集的模型参数。在这里，我们用传播胚胎组织的二维欧拉连续体力学模型演示了该方法。该模型与散布在细胞外基质（ECM）上的不同大小的胚胎组织外植体的定量图像分析紧密集成，并受到一小组参数的调节，包括自由边缘上的力，组织刚度，细胞ECM粘附强度和活性细胞形状变化。我们发现关键参数的统计显着趋势随外植体的初始大小而变化，例如，对于较大的外植体，细胞ECM粘附力更弱，自由边缘力更强。此外，我们证明一种外植体的估计参数可用于预测其他大小相似的外植体的行为。这些预测方法可用于指导进一步的实验，以更好地了解发育过程中如何调节集体细胞的迁移。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号