Rapid Publication

You have full text access to this OnlineOpen article

Automated learning of generative models for subcellular location: Building blocks for systems biology†

  1. Ting Zhao1,2,
  2. Robert F. Murphy3,4,5,*

Article first published online: 30 OCT 2007

DOI: 10.1002/cyto.a.20487

Issue

Cytometry Part A

Cytometry Part A

Volume 71A, Issue 12, pages 978–990, December 2007

Additional Information

How to Cite

Zhao, T. and Murphy, R. F. (2007), Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry, 71A: 978–990. doi: 10.1002/cyto.a.20487

Author Information

  1. 1

    Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  2. 2

    Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  3. 3

    Molecular Biosensor and Imaging Center, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  4. 4

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  5. 5

    Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

*Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA

  1. †

    This work was presented at the XXIII Congress of the International Society for Analytical Cytology, Quebec City, Canada, 20–24 May 2006

Publication History

  1. Issue published online: 18 NOV 2007
  2. Article first published online: 30 OCT 2007
  3. Manuscript Accepted: 8 OCT 2007
  4. Manuscript Received: 11 SEP 2007

Funded by

  • NSF. Grant Number: EF-0331657
  • NIH. Grant Numbers: U54 RR022241, U54 DA021519

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

View Full Article (HTML) Get PDF (568K)

LITERATURE CITED

  • 1
    Ideker T, Galitski T, Hood L. A new approach to decoding life: Systems biology. Annu Rev Genomics Hum Genet 2001; 2: 343–372.
  • 2
    Kitano H. Computational systems biology. Nature 2002; 420: 206–210.
  • 3
    Sauro HM, Hucka M, Finney A, Wellock C, Bolouri H, Doyle J, Kitano H. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 2003; 7: 355–372.
  • 4
    Faust M, Montenarh M. Subcellular localization of protein kinase CK2. A key to its function? Cell Tissue Res 2000; 301: 329–340.
  • 5
    Ortoleva P, Berry E, Brun Y, Fan J, Fontus M, Hubbard K, Jaqaman K, Jarymowycz L, Navid A, Sayyed-Ahmad A,Shreif Z,Stanley F,Tuncay K,Weitzke E,Wu LC. The karyote physico-chemical genomic, proteomic, metabolic cell modeling system. OMICS 2003; 7: 269–283.
  • 6
    Chou K-C,Elrod DW. Protein subcellular location prediction. Prot Eng 1999; 12: 107–118.
  • 7
    Chou KC,Cai YD. Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition. J Cell Biochem 2003; 90: 1250–1260.
    Direct Link:
  • 8
    Park KJ, Kanehisa M. Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 2003; 19: 1656–1663.
  • 9
    Pan YX, Zhang ZZ, Guo ZM, Feng GY, Huang ZD, He L. Application of pseudo amino acid composition for predicting protein subcellular location: Stochastic signal processing approach. J Prot Chem 2003; 22: 395–402.
  • 10
    Chen X,Velliste M, Murphy RF. Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics. Cytometry Part A 2006; 69A: 631–640.
    Direct Link:
  • 11
    Glory E,Murphy RF. Automated subcellular location determination and high throughput microscopy. Developmental Cell 2007; 12: 7–16.
  • 12
    Boland MV, Murphy RF. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 2001; 17: 1213–1223.
  • 13
    Murphy RF,Velliste M,Porreca G. Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. J VLSI Signal Process 2003; 35: 311–321.
  • 14
    Velliste M,Murphy RF. Automated determination of protein subcellular locations from 3D fluorescence microscope images. In: Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging, Washington, DC, 7–10 June 2002. pp 867–870.
  • 15
    Chen X, Velliste M, Weinstein S, Jarvik JW, Murphy RF. Location proteomics—Building subcellular location trees from high resolution 3D fluorescence microscope images of randomly-tagged proteins. Proc SPIE 2003; 4962: 298–306.
  • 16
    Chen X, Murphy RF. Objective clustering of proteins based on subcellular location patterns. J Biomed Biotechnol 2005; 2005: 87–95.
  • 17
    Hu Y,Carmona J,Murphy RF. Application of temporal texture features to automated analysis of protein subcellular locations in time series fluorescence microscope images. In: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging, Arlington, VA, 6–9 April 2006. pp 1028–1031.
  • 18
    Krause A,Stoye J,Vingron M. Large scale hierarchical clustering of protein sequences. BMC Bioinformatics 2005; 6: 15.
  • 19
    Balaji S, Srinivasan N. Use of a database of structural alignments and phylogenetic trees in investigating the relationship between sequence and structural variability among homologous proteins. Prot Eng 2001; 14: 219–226.
  • 20
    Loew LM, Schaff JC. The virtual cell: A software environment for computational cell biology. Trends Biotechnol 2001; 19: 401–406.
  • 21
    Coggan JS, Bartol TM, Esquenazi E, Stiles JR, Lamont S, Martone ME, Berg DK, Ellisman MH, Sejnowski TJ. Evidence for ectopic neurotransmission at a neuronal synapse. Science 2005; 309: 446–451.
  • 22
    Huang K, Murphy RF. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 2004; 5: 78.
  • 23
    Thomas CH, Collier JH, Sfeir CS, Healy KE. Engineering gene expression and protein synthesis by modulation of nuclear shape. Proc Natl Acad Sci USA 2002; 4: 1972–1977.
  • 24
    Blum H. Biological shape and visual science. J Theor Biol 1973; 38: 205–287.
  • 25
    Tam R,Heidrich W. Shape simplification based on the medial axis transform. In: Proceedings of the 14th IEEE Conference on Visualization, 2003; Seattle, Washington, USA. pp 481–488.
  • 26
    Hiransakolwong N, Vu K, Hua KA, Lang S-D. Shape recognition based on the medial axis approach. Proceedings of the 2004 IEEE International Conference on Multimedia Exposition, 2004; Taipei, Taiwan. pp 257–260.
  • 27
    Murata S-i, Herman P, Lakowicz JR. Texture analysis of fluorescence lifetime images of AT- and GC-rich regions in nuclei. J Histochem Cytochem 2001; 49: 1443–1451.
  • 28
    Palcic B. Nuclear texture: Can it be used as a surrogate endpoint biomarker? J Cellular Biochem 1994; 19 ( Suppl): 40–46.
  • 29
    Jørgensen T, Yogesan K, Tveter KJ, Skjørten F, Danielsen HE. Nuclear texture analysis: A new prognostic tool in metastatic prostate cancer. Cytometry 1998; 24: 277–283.
    Direct Link:
  • 30
    Zhu SC,Wu Y,Mumford D. Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. Int J Comput Vision 1998; 27: 107–126.
  • 31
    Nealen A,Alexa M. Hybrid texture synthesis. In: Proceedings of the 14th Eurographics Workshop Rendering, 2003; Leuven, Belgium. pp 97–105.
  • 32
    Portilla J,Simoncelli EP. A parametric texture model based on joint statistics of complex wavelet coefficients. Int J Computer Vision 2000; 40: 49–71.
  • 33
    Lehmussola A,Selinummi J,Ruusuvuori P,Niemistö,Yli-Harja O. Simulating fluorescent microscope images of cell populations. In: Proceedings of the 27 Annual Conference of the IEEE Engineering in Medicine and Biology Society, 2005; Shanghai, China. pp 3153–3156.
  • 34
    Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models—Their training and application. Comput Vision Image Understanding 1995; 61: 38–59.
  • 35
    Zhao T, Velliste M, Boland MV, Murphy RF. Object type recognition for automated analysis of protein subcellular location. IEEE Trans Image Process 2005; 14: 1351–1359.
  • 36
    Bilmes J. A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussion mixture and hidden Markov models. Berkeley, CA: International Computer Science Institute; 1997. Report nr TR-97–021.
  • 37
    Huang K, Velliste M, Murphy RF. Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. Proc SPIE 2003; 4962: 307–318.
  • 38
    Garcia Osuna E, Hua J, Bateman N, Zhao T, Berget P, Murphy R. Large-scale automated analysis of protein subcellular location patterns in randomly-tagged 3T3 cells. Ann Biomed Eng 2007; 35: 1081–1087.
  • 39
    Schubert W, Bonnekoh B, Pmmer AJ, Philipsen L, Bockelmann R, Malykh Y, Gollnick H, Friedenberger M, Bode M, Dress AWM. Analyzing proteome topology and function by automated multi-dimensional fluorescence microscopy. Nat Biotechnol 2006; 24: 1270–1278.
  • 40
    Hucka M,Finney A,Sauro H,Bolouri H,Doyle J,Kitano H,Arkin A,Bornstein B, Bray D,Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U,Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J,Wang J. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 2003; 19: 524–531.
  • 41
    Lloyd CM, Halstead MDB, Nielsen PF. CellML: Its future, present and past. Prog Biophys Mol Biol 2004; 85: 433–450.
View Full Article (HTML) Get PDF (568K)