Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.

Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.
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DOI:
10.1002/bies.201200032
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发表时间:
2012-09
期刊:
影响因子:
4
通讯作者:
Murphy, Robert F.
Murphy, Robert F.
中科院分区:
生物学3区
文献类型:
--
作者:
Buck, Taraz E.;Li, Jieyue;Rohde, Gustavo K.;Murphy, Robert F.

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我们回顾了最先进的计算方法,用于从图像数据构建细胞和核形状的生成统计模型以及其中的亚细胞结构和蛋白质的排列。这些自动化方法允许对细胞图像进行一致的分析,以了解可能的表型范围,区分它们,并为进一步的研究提供信息。这样的模型也可以提供真实的几何和初始蛋白质的位置模拟,以便更好地了解细胞和亚细胞过程。为了确定细胞成分的结构以及蛋白质和其他分子如何在其中分布,这里描述的生成建模方法可以与高通量成像技术相结合,从很少先验假设的数据中推断和表示亚细胞组织。我们还讨论了这些方法的潜在改进和未来的研究方向。
We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.
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