Publications

• J. C. Schaff, A. Lakshminarayana, R. F. Murphy, F.T. Bergmann, A. Funahashi, D. P. Sullivan, L. P. Smith (2023) SBML Level 3 Package: Spatial Processes, Version 1, Release 1. Journal of Integrative Bioinformatics, pp. 20220054. [DOI]
• R. Ambler, G. L. Edmunds, S. L. Tan, S. Cirillo, J. I. Pernes, X. Ruan, J. Huete Carrasco, C.C.W. Wong, J. Lu, J. Ward, G. Toti, A. J. Hedges, S.J. Dovedi, R. F. Murphy, D. J. Morgan and C. Wülfing (2020) PD-1 suppresses the maintenance of cell couples between cytotoxic T cells and tumor target cells within the tumor. Science Signaling 13:eaau4518. [DOI}
• D. J. Clark, L.E. McMillan, S.L. Tan, G. Bellomo, C. Massou, H. Thompson, L. Mykhaylechko, D. Alibhai, X. Ruan, K.L. Singleton, M. Du, A.J. Hedges, P.L. Schwartzberg, P. Verkade, R.F. Murphy, and C. Wülfing (2019) Transient protein accumulation at the center of the T cell antigen presenting cell interface drives efficient IL-2 secretion. eLife 8:e45789. [DOI]
• X. Ruan and R. F. Murphy (2019) Evaluation of methods for generative modeling of cell and nuclear shape. Bioinformatics 35:2475-2489. [DOI]
• X. Ruan, C. Wülfing, and R. F. Murphy (2017) Image-based Spatiotemporal Causality Inference for Protein Signaling Networks. Bioinformatics 33:i217-i224 (Proceedings of 25th Annual International Conference on Intelligent Systems in Molecular Biology; only 16% of submitted papers accepted). [DOI]
• R. Ambler, X. Ruan, R.F. Murphy, and C. Wülfing (2017) Systems Imaging of the immune synapse. The Immune Synapse: Methods and Protocols, Methods in Molecular Biology, vol. 1584 (C. T. Baldari and M. L. Dustin, eds.), pp. 409-421. [DOI]
• Y. Li, T. D. Majarian, A. W. Naik, G. R. Johnson, and R. F. Murphy (2016) Point process models for localization and interdependence of punctate cellular structures. Cytometry Part A 89:633-643.
• K. T. Roybal, T. E. Buck, X. Ruan, B. H. Cho, D. J. Clark, R. Ambler, H. M. Tunbridge, J. Zhang, P. Verkade, C. Wuelfing, and R. F. Murphy (2016) Computational spatiotemporal analysis identifies WAVE2 and Cofilin as joint regulators of costimulation-mediated T cell actin dynamics. Science Signaling 9: rs3.
• R. F. Murphy (2016) Building cell models and simulations from microscope images. Methods 96: 33-39 (Special Issue on High-throughput Imaging).
• R. M. Donovan, J.-J. Tapia, D. P. Sullivan, J. R. Faeder , R. F. Murphy , M. Dittrich, D. M. Zuckerman (2016) Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using A Weighted Ensemble Of Trajectories. PLoS Computational Biology 12: e1004611.
• G. R. Johnson, J. Li, A. Shariff, G.K.Rohde, and R.F. Murphy (2015) Automated Learning of Subcellular Pattern Variation among Punctate Proteins and of a Generative Model of their Distributions in Relation to Microtubules. PLoS Computational Biology 11: e1004614.
• G. R. Johnson, T.E. Buck, D. P. Sullivan, G. R. Rohde, and R. F. Murphy (2015) Joint Modeling of Cell and Nuclear Shape Variation. Molecular Biology of the Cell 26: 4046-4056 (Special Issue on Quantitative Biology).
• J. Li, A. Shariff, M. Wiking, E. Lundberg, G.K. Rohde and R.F. Murphy (2012) Estimating microtubule distributions from 2D immunofluorescence microscopy images reveals differences among human cultured cell lines. PLoS ONE 7:e0050292.
• 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.