Publications on Generative Models of Subcellular Organization

T.E. Buck, J. Li, G.K. Rohde, and R.F. Murphy (2012) Towards the virtual cell: Automated approaches to building models of subcellular organization 'learned' from microscopy images. Bioessays 34:791-799.

R. F. Murphy (2012) CellOrganizer: Image-derived Models of Subcellular Organization and Protein Distribution. Methods in Cell Biology 110:179-193.

T. Peng and R.F. Murphy (2011) Image-derived, Three-dimensional Generative Models of Cellular Organization. Cytometry Part A 79A:383-391.

This paper describes extension of the initial 2D models of Zhao and Murphy (2007) to 3D.

A. Shariff, R.F. Murphy, and G. Rohde (2011) Automated Estimation of Microtubule Model Parameters from 3-D Live Cell Microscopy Images. Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging (ISBI 2011), pp. 1330-1333.

This paper describes modification of the microtubule model described below in order to allow for estimation of free tubulin, and applies the model to images of cells treated with and without nocodazole to depolymerize microtubules. The results are consistent with expectation.

R. F. Murphy (2010) Communicating Subcellular Distributions. Cytometry Part A 77A:686-692.

This review provides a perspective on methods for estimating pattern fractions and learning generative models. It addresses the critical problem of representing information learned about subcellular organization for comparison between cell and tissue types and for use in systems simulations.

A. Shariff, G. K. Rohde and R. F. Murphy (2010) A Generative Model of Microtubule Distributions, and Indirect Estimation of its Parameters from Fluorescence Microscopy Images. Cytometry 77A:457-466.

Methods have been described previously for learning models of cell organization from microscope images in order to be able to synthesize and combine subcellular distributions. These methods involve direct estimation of the model parameters but for some subcellular patterns (such as those of microtubules or microfilaments), direct estimation is difficult due to large numbers of tangled fibers. We describe the first method for indirectly learning a microtubule model and show that it produces results consistent with current knowledge.

T. Peng, Wei Wang, G. K. Rohde, R. F. Murphy (2009) Instance-Based Generative Biological Shape Modeling. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging (ISBI 2009), pp. 690-693.

G.K. Rohde, W. Wang, T. Peng, and R.F. Murphy (2008). Deformation-Based Nonlinear Dimension Reduction: Applications To Nuclear Morphometry. Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging (ISBI 2008), pp. 500-503.

G. K. Rohde, A. Ribeiro, K. N. Dahl, and R. F. Murphy (2008). Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa Cells. Cytometry, 73A:341-350.

T. Zhao and R.F. Murphy (2007). Automated Learning of Generative Models for Subcellular Location: Building Blocks for Systems Biology. Cytometry 71A:978-990.

Support for CellOrganizer has been provided by grants GM075205, GM090033 and GM103712 from the National Institute of General Medical Sciences, grants MCB1121919 and MCB1121793 from the U.S. National Science Foundation, by a Forschungspreis from the Alexander von Humboldt Foundation, and by the School of Life Sciences of the Freiburg Institute for Advanced Studies.