Automated learning of generative models for subcellular location: building blocks for systems biology.
The goal of location proteomics is the systematic and comprehensive study of protein subcellular location. We have previously developed automated, quantitative methods to identify protein subcellular location families, but there have been no effective means of communicating their patterns to integrate them with other information for building cell models. We built generative models of subcellular location that are learned from a collection of images so that they not only represent the pattern, but also capture its variation from cell to cell. Our models contain three components: a nuclear model, a cell shape model and a protein-containing object model. We built models for six patterns that consist primarily of discrete structures. To validate the generated images, we showed that they are recognized with reasonable accuracy by a classifier trained on real images. We also showed that the model parameters themselves can be used as features to discriminate the classes. The models allow the synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. They can potentially be combined for many proteins to yield a high resolution location map in support of systems biology.
Zhao
Ting
T
Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Murphy
Robert F
RF
D001499:Bayes Theorem; D053144:Cell Nucleus Shape / Q000502:physiology; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008954:Models, Biological; D016011:Normal Distribution; D011506:Proteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D013347:Subcellular Fractions / Q000378:metabolism*; D049490:Systems Biology / Q000379:methods*
D011506:Proteins