Image-derived, three-dimensional generative models of cellular organization.
Image-derived, three-dimensional generative models of cellular organization.
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DOI：
10.1002/cyto.a.21066
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发表时间：
2011-05
期刊：
影响因子：
3.7
通讯作者：
Murphy, Robert F.
中科院分区：
文献类型：
--
作者：
Peng, Tao;Murphy, Robert F.
关键词：
考虑到亚细胞位置对蛋白质功能的重要性，细胞行为的计算机模拟最终需要能够模拟蛋白质在细胞器和其他结构中的分布。为此，统计学习方法先前已被用于建立二维显微镜图像集的模型，其中每组包含单个亚细胞位置模式的多个图像。从每组图像中学习到的模型不仅代表了模式，而且还捕捉了该模式在细胞之间的变化。该模型由核形状、细胞形状、细胞器大小和形状以及相对于核和细胞边界的细胞器分布的子模型组成，并且允许合成图像，期望它们从与用于训练它们的图像相同的基础统计分布中绘制。在这里，我们使用类似的框架将这种生成模型方法扩展到三维，从而更准确地描述蛋白质亚细胞位置。不同模式的模型可以结合起来，以产生合成的多通道图像，包含尽可能多的蛋白质，这是很难获得的直接显微镜成像超过几个蛋白质。此外，模型参数表示比描述性图像特征更紧凑和可解释的传达亚细胞模式的方式，并且对于由扰动引起的亚细胞组织变化的自动识别可能特别有效。
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Towards this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images, where each set contains multiple images for a single subcellular location pattern. The model learned from each set of images not only represents the pattern but also captures the variation in that pattern from cell to cell. The models consist of sub-models for nuclear shape, cell shape, organelle size and shape and organelle distribution relative to nuclear and cell boundaries, and allow synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. Here we extend this generative models approach to three dimensions using a similar framework, permitting protein subcellular locations to be described more accurately. Models of different patterns can be combined to yield synthetic multi-channel image containing as many proteins as desired, something that is difficult to obtain by direct microscope imaging for more than a few proteins. In addition, the model parameters represent a more compact and interpretable way of communicating subcellular patterns than descriptive image features, and may be particularly effective for automated identification of changes in subcellular organization caused by perturbagens.
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影响因子：
3.7
作者：
Shariff, Aabid;Murphy, Robert F.;Rohde, Gustavo K.
通讯作者：
Rohde, Gustavo K.
影响因子：
10.6
作者：
Zhao, T;Velliste, M;Murphy, RF
通讯作者：
Murphy, RF
影响因子：
3.2
作者：
TURK, M;PENTLAND, A
通讯作者：
PENTLAND, A
DOI：
10.1073/pnas.0912090107
发表时间：
2010-02-16
影响因子：
11.1
作者：
Peng, Tao;Bonamy, Ghislain M. C.;Murphy, Robert F.
通讯作者：
Murphy, Robert F.
影响因子：
5.8
作者：
Boland, MV;Murphy, RF
通讯作者：
Murphy, RF