Automated High-Throuput Estimation and Modeling of Protein Network Distributions

描述（由申请人提供）：本研究旨在开发和测试方法，这些方法将使形成网络的蛋白质的亚细胞分布（如微管）的蛋白质组级高通量研究成为可能，并用于识别与这些分布相关的蛋白质。更具体地说，我们将测试和进一步完善我们开发的方法，以估计大量细胞的丝分布的平均特性（表征统计变化）。我们的目标是利用来自许多（可能是数千）细胞的可用数据所提供的力量来估计这类蛋白质的高通量研究的真正核心问题：一些自变量（药物或其他实验条件）对感兴趣的网络丝分布的总体平均影响是什么。初步的工作已经确定了这种方法的可行性，我们建议在各种细胞类型的各种条件下，以已知的方式扰动分布，用微管的真实数据进行广泛的测试。结果将确定单个微管生长模型（参数变化）是否适用于许多细胞类型（即，消除仅由细胞大小和形状引起的变化）。我们还将扩展这种方法，以确定许多未知蛋白质与纤维网络的相关性（亲和力），用于目前产生此类数据的几个蛋白质组研究。这些方法将用于分析现有和正在进行的蛋白质组级研究中数千种蛋白质的图像。将通过与现有蛋白质数据库和文献中的信息进行比较，并通过额外的实验，对可能与微管相关的特定蛋白质进行基于图像建模的鉴定。该研究的成功完成不仅将提供关于许多蛋白质位置的重要新信息，而且将填补目前蛋白质组研究建模方法的空白，并有助于在高通量筛选实验中对不同药物、sirna或突变的作用进行机制量化。

DESCRIPTION (provided by applicant): This research is aimed at developing and testing methods that will enable proteome-wide high-throughput studies of the subcellular distributions of proteins that form networks (such as microtubules), and for identifying proteins whose distributions are related to these. More specifically we will test and further refine methods that we have developed to estimate the average properties (characterize the statistical variation) of the filament distributions for a large number of cells. Our aim is to use the power afforded by the availability of data arising from many (possibly thousands) of cells to estimate what is really at the heart of the question for high-throughput studies of proteins of this type: what is the overall average effect of some independent variable (drug or other experimental condition) on the network filament distribution of interest. Preliminary work has established the feasibility of this approach, and we propose to test it extensively with real data for microtubules in a variety of cell types under a variety of conditions that perturb distributions in known ways. The results will establish whether a single microtubule growth model (with changes in parameters) is valid for many cell types (i.e., to remove variation due solely to cell size and shape). We will also extend this method to determine the correlation (affinity) of many unknown proteins to filament networks for several proteome wide studies currently generating such data. The methods will be used to analyze images for thousands of proteins from existing and ongoing proteome-scale studies. The identification solely on the basis of image-based modeling of specific proteins as likely to be microtubule-associated will be tested for selected examples by comparison with information in existing protein databases and literature and by additional experimentation. The successful completion of this study would not only provide important new information about the location of many proteins, but will fill a current void in modeling approaches for proteome-wide studies and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.