Abstract
Location proteomics is the systematic study of subcellular locations of proteins. It seeks to provide a thorough understanding of location patterns and integrate such knowledge into systems biology studies. Progress in this field depends on the quantitative and automated analysis of images of location patterns. This chapter introduces various approaches to such analysis and summarizes successes in using them to investigate different image types and different cell types.
These approaches can be divided into two categories, feature-based analysis, and pattern modeling. In feature-based analysis, each image is converted into a feature vector and all further analysis is carried out on the features. This has enabled the automated comparison, classification, and clustering of location patterns on a large scale. An important conclusion from this work has been that, at least for certain problems, computerized analysis can perform better than visual examination. To take a further step, object-based models have been built to describe location patterns in a compact and portable form. This facilitates more complicated analysis, such as the decomposition of patterns that are themselves mixtures of more basic patterns. Moreover, generative models can be learned from collections of images so that new examples of location patterns can be synthesized from them. This provides a way to integrate location information into systems biology, by combining generative models of many proteins and using the synthesized images as initial conditions for cell behavior simulations.
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Zhao, T., Chen, SC., Murphy, R.F. (2007). Location Proteomics. In: Choi, S. (eds) Introduction to Systems Biology. Humana Press. https://doi.org/10.1007/978-1-59745-531-2_11
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DOI: https://doi.org/10.1007/978-1-59745-531-2_11
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