DOI QR코드

DOI QR Code

From proteomics toward systems biology: integration of different types of proteomics data into network models

  • Rho, Sang-Chul (School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology) ;
  • You, Sung-Yong (School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology) ;
  • Kim, Yong-Soo (School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology) ;
  • Hwang, Dae-Hee (School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology)
  • Accepted : 2008.02.25
  • Published : 2008.03.31

Abstract

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.

Keywords

References

  1. Zhang, S., Jin, G., Zhang, X. S. and Chen, L. (2007) Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 7, 2856-2869. https://doi.org/10.1002/pmic.200700095
  2. Weston, A. D. and Hood, L. (2004) Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J. Proteome. Res. 3, 179-196. https://doi.org/10.1021/pr0499693
  3. Ideker, T., Galitski, T. and Hood, L. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics. Hum. Genet. 2, 343-372. https://doi.org/10.1146/annurev.genom.2.1.343
  4. Knezevic, V., Leethanakul, C., Bichsel, V. E., Worth, J. M., Prabhu, V. V., Gutkind, J. S., Liotta, L. A., Munson, P. J., Petricoin, E. F., 3rd. and Krizman, D. B. (2001) Proteomic profiling of the cancer microenvironment by antibody arrays. Proteomics 1, 1271-1278. https://doi.org/10.1002/1615-9861(200110)1:10<1271::AID-PROT1271>3.0.CO;2-6
  5. Aebersold, R. and Goodlett, D. R. (2001) Mass spectrometry in proteomics. Chem. Rev. 101, 269-295. https://doi.org/10.1021/cr990076h
  6. Souchelnytskyi, S. (2005) Bridging proteomics and systems biology: what are the roads to be traveled? Proteomics 5, 4123-4137. https://doi.org/10.1002/pmic.200500135
  7. Prakash, A., Mallick, P., Whiteaker, J., Zhang, H., Paulovich, A., Flory, M., Lee, H., Aebersold, R. and Schwikowski, B. (2006) Signal maps for mass spectrometry-based comparative proteomics. Mol. Cell. Proteomics. 5, 423-432. https://doi.org/10.1074/mcp.M500133-MCP200
  8. Cui, X. and Churchill, G. A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome. Biol. 4, 210. https://doi.org/10.1186/gb-2003-4-4-210
  9. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome. Res. 13, 2498-2504. https://doi.org/10.1101/gr.1239303
  10. Tanaka, T., Bai, Z., Srinoulprasert, Y., Yang, B. G., Hayasaka, H. and Miyasaka, M. (2005) Chemokines in tumor progression and metastasis. Cancer. Sci. 96, 317-322. https://doi.org/10.1111/j.1349-7006.2005.00059.x
  11. Yates, JR., 3rd. (2004) Mass spectral analysis in proteomics. Annu. Rev. Biophys. Biomol. Struct. 33, 297-316. https://doi.org/10.1146/annurev.biophys.33.111502.082538
  12. Nesvizhskii, A. I., Vitek, O. and Aebersold, R. (2007) Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods. 4, 787-797. https://doi.org/10.1038/nmeth1088
  13. Liu, J., Cai, Y., Wang, J., Zhou, Q., Yang, B., Lu, Z., Jiao, L., Zhang, D., Sui, S., Jiang, Y., Ying, W. and Qian, X. (2007) Phosphoproteome profile of human liver Chang's cell based on 2-DE with fluorescence staining and MALDI-TOF/TOF-MS. Electrophoresis 28, 4348-4358. https://doi.org/10.1002/elps.200600696
  14. Maor, R., Jones, A., Nuhse, T. S., Studholme, D. J., Peck, S. C. and Shirasu, K. (2007) Multidimensional protein identification technology (MudPIT) analysis of ubiquitinated proteins in plants. Mol. Cell. Proteomics. 6, 601-610. https://doi.org/10.1074/mcp.M600408-MCP200
  15. Chen, E. I., Hewel, J., Felding-Habermann, B. and Yates, J. R., 3rd. (2006) Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol. Cell. Proteomics. 5, 53-56. https://doi.org/10.1074/mcp.T500013-MCP200
  16. Domon, B. and Aebersold, R. (2006) Mass spectrometry and protein analysis. Science 312, 212-217. https://doi.org/10.1126/science.1124619
  17. Hansen, K. C., Schmitt-Ulms, G., Chalkley, R. J., Hirsch, J., Baldwin, M. A. and Burlingame, A. L. (2003) Mass spectrometric analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and multidimensional chromatography. Mol. Cell. Proteomics. 2, 299-314. https://doi.org/10.1074/mcp.M300021-MCP200
  18. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A. and Mann, M. (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics. 1, 376-386. https://doi.org/10.1074/mcp.M200025-MCP200
  19. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A. and Pappin, D. J. (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics. 3, 1154-1169. https://doi.org/10.1074/mcp.M400129-MCP200
  20. Han, D. K., Eng, J., Zhou, H. and Aebersold, R. (2001) Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946-951. https://doi.org/10.1038/nbt1001-946
  21. Li, X. J., Zhang, H., Ranish, J. A. and Aebersold, R. (2003) Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal. Chem. 75, 6648-6657. https://doi.org/10.1021/ac034633i
  22. Higgs, R. E., Knierman, M. D., Gelfanova, V., Butler, J. P. and Hale, J. E. (2005) Comprehensive label-free method for the relative quantification of proteins from biological samples. J. Proteome. Res. 4, 1442-1450 https://doi.org/10.1021/pr050109b
  23. Gilchrist, A., Au, C. E., Hiding, J., Bell, A. W., Fernandez-Rodriguez, J., Lesimple, S., Nagaya, H., Roy, L., Gosline, S. J., Hallett, M., Paiement, J., Kearney, R. E., Nilsson, T. and Bergeron, J. J. (2006) Quantitative proteomics analysis of the secretory pathway. Cell 127, 1265-1281. https://doi.org/10.1016/j.cell.2006.10.036
  24. Ono, M., Shitashige, M., Honda, K., Isobe, T., Kuwabara, H., Matsuzuki, H., Hirohashi, S. and Yamada, T. (2006) Label-free quantitative proteomics using large peptide data sets generated by nanoflow liquid chromatography and mass spectrometry. Mol. Cell. Proteomics. 5, 1338-1347/ https://doi.org/10.1074/mcp.T500039-MCP200
  25. Bridges, S. M., Magee, G. B., Wang, N., Williams, W. P., Burgess, S. C. and Nanduri, B. (2007) ProtQuant: a tool for the label-free quantification of MudPIT proteomics data. BMC. Bioinformatics. 8 Suppl 7, S24. https://doi.org/10.1186/1471-2105-8-24
  26. Mann, M. and Jensen, O. N. (2003) Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255-261. https://doi.org/10.1038/nbt0303-255
  27. Witze, E. S., Old, W. M., Resing, K. A. and Ahn, N. G. (2007) Mapping protein post-translational modifications with mass spectrometry. Nat. Methods. 4, 798-806. https://doi.org/10.1038/nmeth1100
  28. Jensen, O. N. (2004) Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol. 8, 33-41. https://doi.org/10.1016/j.cbpa.2003.12.009
  29. Selbach, M. and Mann, M. (2006) Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nat. Methods. 3, 981-983. https://doi.org/10.1038/nmeth972
  30. Burckstummer, T., Bennett, K. L., Preradovic, A., Schutze, G., Hantschel, O., Superti-Furga, G. and Bauch, A. (2006) An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat. Methods. 3, 1013-1019. https://doi.org/10.1038/nmeth968
  31. Kitatsuji, C., Kurogochi, M., Nishimura, S., Ishimori, K. and Wakasugi, K. (2007) Molecular basis of guanine nucleotide dissociation inhibitor activity of human neuroglobin by chemical cross-linking and mass spectrometry. J. Mol. Biol. 368, 150-160. https://doi.org/10.1016/j.jmb.2007.02.002
  32. Rauch, A., Bellew, M., Eng, J., Fitzgibbon, M., Holzman, T., Hussey, P., Igra, M., Maclean, B., Lin, C. W., Detter, A., Fang, R., Faca, V., Gafken, P., Zhang, H., Whiteaker, J., States, D., Hanash, S., Paulovich, A. and McIntosh, M. W. (2006) Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J. Proteome. Res. 5, 112-121. https://doi.org/10.1021/pr0503533
  33. http://sbeams.org
  34. Keller, A., Eng, J., Zhang, N., Li, X. J. and Aebersold, R. (2005) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 1-8
  35. Huang da, W., Sherman, B. T., Tan, Q., Collins, J. R., Alvord, W. G., Roayaei, J., Stephens, R., Baseler, M. W., Lane, H. C. and Lempicki, R. A. (2007) The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome. Biol. 8, R183 https://doi.org/10.1186/gb-2007-8-9-r183
  36. Craig, R. and Beavis, R. C. (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466-1467. https://doi.org/10.1093/bioinformatics/bth092
  37. Jimmy, K. E., Ashley, L. M. and John, R. Y. (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976-989. https://doi.org/10.1016/1044-0305(94)80016-2
  38. Keller, A., Nesvizhskii, A. I., Kolker, E. and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392. https://doi.org/10.1021/ac025747h
  39. Nesvizhskii, A. I., Keller, A., Kolker, E. and Aebersold, R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646-4658. https://doi.org/10.1021/ac0341261
  40. Kim, S., Na, S., Sim, J. W., Park, H., Jeong, J., Kim, H., Seo, Y., Seo, J., Lee, K. J. and Paek, E. (2006) MODi: a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra. Nucleic. Acids. Res. 34, W258-263. https://doi.org/10.1093/nar/gkl245
  41. Bader, G. D., Betel, D. and Hogue, C. W. (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids. Res. 31, 248-250. https://doi.org/10.1093/nar/gkg056
  42. Mishra, G. R., Suresh, M., Kumaran, K., Kannabiran, N., Suresh, S., Bala, P., Shivakumar, K., Anuradha, N., Reddy, R., Raghavan, T. M., Menon, S., Hanumanthu, G., Gupta, M., Upendran, S., Gupta, S., Mahesh, M., Jacob, B., Mathew, P., Chatterjee, P., Arun, K. S., Sharma, S., Chandrika, K. N., Deshpande, N., Palvankar, K., Raghavnath, R., Krishnakanth, R., Karathia, H., Rekha, B., Nayak, R., Vishnupriya, G., Kumar, H. G., Nagini, M., Kumar, G. S., Jose, R., Deepthi, P., Mohan, S. S., Gandhi, T. K., Harsha, H. C., Deshpande, K. S., Sarker, M., Prasad, T. S. and Pandey, A. (2006) Human protein reference database-2006 update. Nucleic. Acids. Res. 34, D411-414. https://doi.org/10.1093/nar/gkj141
  43. Saeed, A. I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N., Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., Sturn, A., Snuffin, M., Rezantsev, A., Popov, D., Ryltsov, A., Kostukovich, E., Borisovsky, I., Liu, Z., Vinsavich, A., Trush, V. and Quackenbush, J. (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34, 374-378.
  44. von Mering, C., Jensen, L. J., Kuhn, M., Chaffron, S., Doerks, T., Kruger, B., Snel, B. and Bork, P. (2007) STRING 7-recent developments in the integration and prediction of protein interactions. Nucleic. Acids. Res. 35, D358-362. https://doi.org/10.1093/nar/gkl825
  45. Kanehisa, M., Goto, S., Kawashima, S. and Nakaya, A. (2002) The KEGG databases at GenomeNet. Nucleic. Acids. Res. 30, 42-46. https://doi.org/10.1093/nar/30.1.42
  46. http://www.ingenuity.com.
  47. Shannon, P. T., Reiss, D. J., Bonneau, R. and Baliga, N. S. (2006) The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC. Bioinformatics. 7, 176. https://doi.org/10.1186/1471-2105-7-176
  48. Maere, S., Heymans, K. and Kuiper, M. (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448-3449. https://doi.org/10.1093/bioinformatics/bti551
  49. Bader, G. D. and Hogue, C. W. (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC. Bioinformatics. 4, 2. https://doi.org/10.1186/1471-2105-4-2
  50. Ideker, T., Ozier, O., Schwikowski, B. and Siegel, A. F. (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1, S233-240. https://doi.org/10.1093/bioinformatics/18.suppl_1.S233
  51. Jayapandian, M., Chapman, A., Tarcea, V. G., Yu, C., Elkiss, A., Ianni, A., Liu, B., Nandi, A., Santos, C., Andrews, P., Athey, B., States, D. and Jagadish, H. V. (2007) Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together. Nucleic. Acids. Res. 35, D566-571. https://doi.org/10.1093/nar/gkl859
  52. Tao, W. A., Wollscheid, B., O'Brien, R., Eng, J. K., Li, X. J., Bodenmiller, B., Watts, J. D., Hood, L. and Aebersold, R. (2005) Quantitative phosphoproteome analysis using a dendrimer conjugation chemistry and tandem mass spectrometry. Nat. Methods. 2, 591-598. https://doi.org/10.1038/nmeth776
  53. Bodenmiller, B., Mueller, L. N., Mueller, M., Domon, B. and Aebersold, R. (2007) Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat. Methods. 4, 231-237. https://doi.org/10.1038/nmeth1005
  54. Kaji, H., Yamauchi, Y., Takahashi, N. and Isobe, T. (2006) Mass spectrometric identification of N-linked glycopeptides using lectin-mediated affinity capture and glycosylation sitespecific stable isotope tagging. Nat. Protoc. 1, 3019-3027. https://doi.org/10.1038/nprot.2006.444
  55. Zhang, H., Li, X. J., Martin, D. B. and Aebersold, R. (2003) Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 21, 660-666. https://doi.org/10.1038/nbt827
  56. Kirkpatrick, D. S., Weldon, S. F., Tsaprailis, G., Liebler, D. C. and Gandolfi, A. J. (2005) Proteomic identification of ubiquitinated proteins from human cells expressing His-tagged ubiquitin. Proteomics 5, 2104-2111. https://doi.org/10.1002/pmic.200401089
  57. Petrotchenko, E. V., Olkhovik, V. K. and Borchers, C. H. (2005) Isotopically coded cleavable cross-linker for studying protein-protein interaction and protein complexes. Mol. Cell. Proteomics. 4, 1167-1179. https://doi.org/10.1074/mcp.T400016-MCP200
  58. Oda, Y., Owa, T., Sato, T., Boucher, B., Daniels, S., Yamanaka, H., Shinohara, Y., Yokoi, A., Kuromitsu, J. and Nagasu, T. (2003) Quantitative chemical proteomics for identifying candidate drug targets. Anal. Chem. 75, 2159-2165. https://doi.org/10.1021/ac026196y
  59. Zubarev, R. A. (2004) Electron-capture dissociation tandem mass spectrometry. Curr. Opin. Biotechnol. 15, 12-16. https://doi.org/10.1016/j.copbio.2003.12.002
  60. Chi, A., Huttenhower, C., Geer, L. Y., Coon, J. J., Syka, J. E., Bai, D. L., Shabanowitz, J., Burke, D. J., Troyanskaya, O. G. and Hunt, D. F. (2007) Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 104, 2193-2198. https://doi.org/10.1073/pnas.0607084104
  61. Shadforth, I. P., Dunkley, T. P., Lilley, K. S. and Bessant, C. (2005) i-Tracker: for quantitative proteomics using iTRAQ. BMC. Genomics. 6, 145. https://doi.org/10.1186/1471-2164-6-145
  62. MacCoss, M. J., Wu, C. C., Liu, H., Sadygov, R. and Yates, JR., 3rd. (2003) A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 75, 6912-6921. https://doi.org/10.1021/ac034790h
  63. Jaffe, J. D., Mani, D. R., Leptos, K. C., Church, G. M., Gillette, M. A. and Carr, S. A. (2006) PEPPeR, a platform for experimental proteomic pattern recognition. Mol. Cell. Proteomics. 5, 1927-1941. https://doi.org/10.1074/mcp.M600222-MCP200
  64. Clauser, K. R., Baker, P. and Burlingame, A. L. (1999) Role of accurate mass measurement (+/- 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal. Chem. 71, 2871-2882. https://doi.org/10.1021/ac9810516
  65. Tabb, D. L., Saraf, A. and Yates, J. R., 3rd (2003) GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem. 75, 6415-6421. https://doi.org/10.1021/ac0347462
  66. Ewing, R. M., Chu, P., Elisma, F., Li, H., Taylor, P., Climie, S., McBroom-Cerajewski, L., Robinson, M. D., O'Connor, L., Li, M., Taylor, R., Dharsee, M., Ho, Y., Heilbut, A., Moore, L., Zhang, S., Ornatsky, O., Bukhman, Y. V., Ethier, M., Sheng, Y., Vasilescu, J., Abu-Farha, M., Lambert, J. P., Duewel, H. S., Stewart, II, Kuehl, B., Hogue, K., Colwill, K., Gladwish, K., Muskat, B., Kinach, R., Adams, S. L., Moran, M. F., Morin, G. B., Topaloglou, T. and Figeys, D. (2007) Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89. 最愀猀琀爀椀挀 氀礀洀瀀栀漀洀愀⸀ 䌀愀渀挀攀爀 㘀㘀Ⰰ ㄀㈀㠀㌀ⴀ㄀㈀㠀㜁j䌀㄰⸱〰㈯㄰㤷ⴰㄴ㈨ㄹ㤰〹ㄵ⤶㘺㘼ㄲ㠳㨺䅉䐭䍎䍒㈸㈰㘶〶㌱㸳⸰⹃伻㈭㐀 N䅒吴㔹㔶㌸㔁1Ā䘀1㌸ⴱ阃㈰〴ⴰ〭〰ᴀ卍䝈䉍弲〰㝟瘱㝮㝳㠷弱〰㉟ㄱ㔁1氀䡥汩捯扡捴敲⁰祬潲椠楮晥捴楯渠楮⁰慴楥湴猠睩瑨⁧慳瑲楣⁣慲捩湯浡⁩渠扩潰獹⁡湤⁳畲杩捡氠牥獥捴楯渠獰散業敮献伀卨楢慴愬⁔㭉浯瑯Ⱐ䤻佨畣桩Ⱐ夻呡杵捨椬⁙㭔慫慪椬⁓㭉步浵牡Ⱐ主乡歡漬⁋㭓桩浡Ⱐ吆C慮捥爂7㜁6ऀ㄰㐴ⴱ〴㤊1㤹㘭〰ⴰ ,Ȃ⨀ĂಈȀĀ찀ʺ最ʺ昀㔀㔃最Ā匀栀椀戀愀琀⳪숛夀ʺ昃遇輀ԁ혉였Ā匀栀椀戀愀琀愀Ⰰ 吀⸀Ⰰ 䤀⸀ 䤀洀漀琀漀Ⰰ 夀⸀ 伀栀甀挀栀椀Ⰰ 夀⸀ 吀愀最甀挀栀椀Ⰰ 匀⸀ 吀愀欀愀樀椀Ⰰ 一⸀ 䤀欀攀洀甀爀愀Ⰰ 䬀⸀ 一愀欀愀漀 愀渀搀 吀⸀ 匀栀椀洀愀⸀ ㄀㤀㤀㘀⸀ 䠀攀氀椀挀漀戀愀挀琀攀爀 瀀礀氀漀爀椀 椀渀昀攀挀琀椀漀渀 椀渀 瀀愀琀椀攀渀琀猀 眀椀琀栀 最愀猀琀爀椀挀 挀愀爀挀椀渀漀洀愀 椀渀 戀椀漀瀀猀礀 愀渀搀 猀甀爀最椀挀愀氀 爀攀猀攀挀琀椀漀渀 猀瀀攀挀椀洀攀渀猀⸀ 䌀愀渀挀攀爀 㜀㜀Ⰰ ㄀ 㐀㐀ⴀ㄀ 㐀㤁j䀀㄰⸱〰㈯⡓䥃䤩㄰㤷ⴰㄴ㈨ㄹ㤶〳ㄵ⤷㜺㘼㄰㐴㨺䅉䐭䍎䍒㘾㌮〮䍏㬲ⵈఀ乁剔㐵㤷ㄳ㘱Ā㄁F湮㴃 䨻䡡湳敮Ⱐ医ᴀ卍䝈䉍弲〰㝟瘱㝮㝳㠷弱〰㉟ㄱ㘁1娀䍡杁⁳敲
  67. Nikitin, A., Egorov, S., Daraselia, N. and Mazo, I. (2003) Pathway studio--the analysis and navigation of molecular networks. Bioinformatics 19, 2155-2157. https://doi.org/10.1093/bioinformatics/btg290

Cited by

  1. Towards understanding epithelial–mesenchymal transition: A proteomics perspective vol.1794, pp.9, 2009, https://doi.org/10.1016/j.bbapap.2009.05.001
  2. Protein Structure-Sensitive Electrocatalysis at Dithiothreitol-Modified Electrodes vol.132, pp.27, 2010, https://doi.org/10.1021/ja102427y
  3. Translational Systems Approaches to the Biology of Inflammation and Healing vol.32, pp.2, 2010, https://doi.org/10.3109/08923970903369867
  4. Simple protein structure-sensitive chronopotentiometric analysis with dithiothreitol-modified Hg electrodes vol.87, 2012, https://doi.org/10.1016/j.bioelechem.2012.01.004
  5. Extracting biomarkers of commitment to cancer development: potential role of vibrational spectroscopy in systems biology vol.15, pp.5, 2015, https://doi.org/10.1586/14737159.2015.1028372
  6. In silico augmentation of the drug development pipeline: examples from the study of acute inflammation vol.72, pp.2, 2011, https://doi.org/10.1002/ddr.20415
  7. The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function vol.7, pp.1-2, 2013, https://doi.org/10.1002/prca.201200095
  8. Genes in asthma: new genes and new ways vol.8, pp.5, 2008, https://doi.org/10.1097/ACI.0b013e32830f1dc1
  9. Fish proteome analysis: Model organisms and non-sequenced species vol.10, pp.4, 2010, https://doi.org/10.1002/pmic.200900609
  10. Where systems biology meets postharvest vol.62, pp.3, 2011, https://doi.org/10.1016/j.postharvbio.2011.05.007
  11. Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers vol.19, pp.2, 2015, https://doi.org/10.1111/jcmm.12447
  12. Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC–MS for biomarker discovery vol.83, pp.4, 2011, https://doi.org/10.1016/j.talanta.2010.10.029
  13. Exploring the Mysteries of Traditional Chinese Medicine Systematically by Expression Microarrays vol.73, pp.8, 2012, https://doi.org/10.1002/ddr.21042
  14. A Proteomics and other Omics approach in the context of farmed fish welfare and biomarker discovery pp.17535123, 2020, https://doi.org/10.1111/raq.12308