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细胞表型特征的计算与分析
其他题名Phenotypic Characterization of Cell Assay
畅航
学位类型工学博士
导师杨青 ; Bahram Parvin
2008-05-26
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词细胞核分割 多元特征表示 蛋白质检测 Nuclear Segmentation Protein Detection Multivariate Representation
摘要在后基因时代,功能基因学(functional genomics)成为生物研究的一大挑战。其研 究内容之一是:基因的表达同细胞表型特征的关系。想要从基因角度理解复杂器官的生物 学原理,我们首先要理解表型特征产生和维持的动态过程。而表型特征,实质上是特定的 基因得以选择性的表达所导致的结果,它反映出细胞的变化历程以及细胞对周围环境的响 应。为了定义细胞的表型特征,我们可以在大量的数据中,以细胞核或细胞质为依据,对 多种关键的蛋白质进行动力学分析和定量评估。同时,我们还需要加入刺激因素,通过细 胞对刺激的响应,建立并测试相关的功能模型。在以上所提出的研究方法中,至关重要的 环节就是将与不同目标器官相关联的蛋白质的定位分析加以分类,建立定量分析蛋白质响 应的计算方法,并为细胞的响应建立多元的表达方法,从而使得我们能够在更高的层面上 对生物特性加以分析。本文的工作及主要贡献包括以下四个部分: 1 ° 提出了针对细胞膜蛋白质分割方法。通过对细胞膜蛋白质图像的观察和分析,我们 提出迭代切线voting算法检测和增强细胞膜蛋白质信号。以此为基础,我们利用适 当的几何约束和细胞核信息,提出了细胞膜蛋白质的分割方法,它为基于细胞的蛋 白质分析提供了依据。实验数据证明了算法的鲁棒性,实验结果具有合理的生物物 理解释。 2 ° 提出了基于单个焦平面的三维细胞簇分割算法。通过对单个焦平面三维细胞簇图像 的观察和分析,我们利用合理的几何约束和动态轮廓模型,建立了基于“检测-约 束-分割”三个步骤的分割方法。该方法为基于单个焦平面的三维细胞簇模型的分 析和建模提供了重要依据。实验结果证明了算法的有效性。 3 ° 提出了三维细胞簇分割方法。通过对三维细胞簇数据的观察和分析,我们利用合 理的几何约束和Radon变换,建立了基于“检测-约束-分割”三个步骤的分割方 法。由于三维细胞簇模型在功能上更接近于人体细胞模型,因此该方法所得出的分 析结果为研究人体细胞模型提供了更加可靠的信息。实验结果验证了算法的鲁棒 性。 4 ° 建立了信息的多元表示方法。该多元表示法为信息的组织,以及基于更高层次的信 息分析提供了重要依据。我们以细胞膜蛋白质为例,给出了基于细胞的多元特征分 析的例子。示例中所得出的分析结果,具有很好的生物物理解释。目前我们所有计 算得出的生物信息都以此方式进行表达。
其他摘要The challenge of the post-genomic era is functional genomics, i.e., understanding how the genome is expressed to produce myriad cell phenotypes within the context of systems biology. To use genomic information to understand the biology of complex organisms, one must understand the dynamics of phenotype generation and maintenance. A phenotype is the result of selective expression of the genome. It is an expression of the history of the cell and its response to the extracellular environment. In order to define cell "phenomes", one would track the kinetics and quantities of multiple constituent proteins, their cellular context and morphological features in large populations. Such studies should also include responses to stimuli so that functional models can be generated and tested. The key requirement is to define a taxonomy of protein localization in reference to organelle of interest, develop computational methods to quantify their response, and design a multivariate representation of cellular response in context that enables higher level analysis and profiling. The main contributions of this dissertation thesis include: 1) Proposed algorithms for the segmentation of nuclear membrane protein. Based on the observation on the images, we extended the existing iterative radial voting algorithm to iterative tangential voting algorithm for the detection of nuclear membrane signal. After that, some geometric constraints combined with the nuclear information were leveraged for the segmentation of nuclear membrane. This approach enables the analysis of membrane protein on a cell-by-cell basis and was demonstrated to be robust by our experiments. Furthermore, the analysis results have reasonable biophysical interpretation. 2) Proposed a segmentation algorithm for the cell culture on a single focal plane. This algorithm is a combination of geometric constrains and active contour model. It has the following three steps: detection-constraint-evolution, where the detection is performed by iterative radial voting. It enables the analysis of cell culture model on a single focal plane and was evaluated by our experiments. 3) Proposed a segmentation algorithm for 3D cell culture model. This algorithm is the combination of geometric constraints and Radon transform. It has three sub-steps: detection-constraint-evolution, where the detection is performed by iterative radial voting. Since 3D cell culture model has similar functional properties as those observed in human, it provides more reliable information than the analysis based on 2D cell culture model. The robustness was demonstrated by our experiments. 4) Proposed a multivariate feature representation, and gave an example of analysis of membrane protein based on this representation. This representation enables the higher-level interpretation of the bioinformation and the experiments had valuable biophysical meanings. All of our features for different type of data are organized by this representation.
馆藏号XWLW1286
其他标识符200518014628091
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6076
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
畅航. 细胞表型特征的计算与分析[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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