About this Journal Submit a Manuscript Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 742835, 8 pages
http://dx.doi.org/10.1155/2013/742835
Review Article

Advanced Systems Biology Methods in Drug Discovery and Translational Biomedicine

Molecular Medicine Research Center, State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China

Received 17 June 2013; Accepted 26 August 2013

Academic Editor: Bing Niu

Copyright © 2013 Jun Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H.-Y. Chuang, M. Hofree, and T. Ideker, “A decade of systems biology,” Annual Review of Cell and Developmental Biology, vol. 26, pp. 721–744, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. A.-L. Barabási, N. Gulbahce, and J. Loscalzo, “Network medicine: a network-based approach to human disease,” Nature Reviews Genetics, vol. 12, no. 1, pp. 56–68, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. R. P. Araujo, L. A. Liotta, and E. F. Petricoin, “Proteins, drug targets and the mechanisms they control: the simple truth about complex networks,” Nature Reviews Drug Discovery, vol. 6, no. 11, pp. 871–880, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Pujol, R. Mosca, J. Farrés, and P. Aloy, “Unveiling the role of network and systems biology in drug discovery,” Trends in Pharmacological Sciences, vol. 31, no. 3, pp. 115–123, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. E. A. Sobie, Y.-S. Lee, S. L. Jenkins, and R. Iyengar, “Systems biology—biomedical modeling,” Science Signaling, vol. 4, no. 190, article tr2, 2011. View at Scopus
  6. D. Faratian, R. G. Clyde, J. W. Crawford, and D. J. Harrison, “Systems pathology—taking molecular pathology into a new dimension,” Nature Reviews Clinical Oncology, vol. 6, no. 8, pp. 455–464, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Ma'ayan, “Introduction to network analysis in systems biology,” Science Signaling, vol. 4, no. 190, article tr5, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Vidal, M. E. Cusick, and A.-L. Barabási, “Interactome networks and human disease,” Cell, vol. 144, no. 6, pp. 986–998, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Chang, T. Xu, Y. Li, and K. Wang, “Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of “date” and “party” hubs,” Scientific Reports, vol. 3, article 1691, 2013.
  10. B. B. Aldridge, J. M. Burke, D. A. Lauffenburger, and P. K. Sorger, “Physicochemical modelling of cell signalling pathways,” Nature Cell Biology, vol. 8, no. 11, pp. 1195–1203, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. E. A. Sobie, “An introduction to dynamical systems,” Science Signaling, vol. 4, no. 191, article tr6, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. S. R. Neves, “Developing models in virtual cell,” Science Signaling, vol. 4, no. 192, article tr12, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. S. R. Neves, “Obtaining and estimating kinetic parameters from the literature,” Science Signaling, vol. 4, no. 191, article tr8, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. K. D. Costa, S. H. Kleinstein, and U. Hershberg, “Biomedical model fitting and error analysis,” Science Signaling, vol. 4, no. 192, article tr9, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Fisher and T. A. Henzinger, “Executable cell biology,” Nature Biotechnology, vol. 25, no. 11, pp. 1239–1249, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Zhang, M. V. Shah, J. Yang et al., “Network model of survival signaling in large granular lymphocyte leukemia,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 42, pp. 16308–16313, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein et al., “Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction,” Molecular Systems Biology, vol. 5, article 331, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Materi and D. S. Wishart, “Computational systems biology in drug discovery and development: methods and applications,” Drug Discovery Today, vol. 12, no. 7-8, pp. 295–303, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. K.-I. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal, and A.-L. Barabási, “The human disease network,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 21, pp. 8685–8690, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Linghu, E. S. Snitkin, Z. Hu, Y. Xia, and C. DeLisi, “Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network,” Genome Biology, vol. 10, no. 9, article R91, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Suthram, J. T. Dudley, A. P. Chiang, R. Chen, T. J. Hastie, and A. J. Butte, “Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets,” PLoS Computational Biology, vol. 6, no. 2, Article ID e1000662, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. G. Wu, X. Feng, and L. Stein, “A human functional protein interaction network and its application to cancer data analysis,” Genome Biology, vol. 11, no. 5, article R53, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Hwang, I. Y. Lee, H. Yoo et al., “A systems approach to prion disease,” Molecular Systems Biology, vol. 5, article 252, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. N. Yosef, A. K. Shalek, J. T. Gaublomme, et al., “Dynamic regulatory network controlling TH17 cell differentiation,” Nature, vol. 496, no. 7446, pp. 461–468, 2013.
  25. I. Arnit, M. Garber, N. Chevrier et al., “Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses,” Science, vol. 326, no. 5950, pp. 257–263, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. H.-Y. Chuang, E. Lee, Y.-T. Liu, D. Lee, and T. Ideker, “Network-based classification of breast cancer metastasis,” Molecular Systems Biology, vol. 3, article 140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. I. W. Taylor, R. Linding, D. Warde-Farley et al., “Dynamic modularity in protein interaction networks predicts breast cancer outcome,” Nature Biotechnology, vol. 27, no. 2, pp. 199–204, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Breitkreutz, L. Hlatky, E. Rietman, and J. A. Tuszynski, “Molecular signaling network complexity is correlated with cancer patient survivability,” Proceedings of the National Academy of Sciences, vol. 109, no. 23, pp. 9209–9212, 2012.
  29. H. Y. Chuang, L. Rassenti, M. Salcedo, et al., “Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression,” Blood, vol. 120, no. 13, pp. 2639–2649, 2012.
  30. T. Huang, J. Wang, Y.-D. Cai, H. Yu, and K.-C. Chou, “Hepatitis c virus network based classification of hepatocellular cirrhosis and carcinoma,” PLoS ONE, vol. 7, no. 4, Article ID e34460, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. J. N. Andersen, S. Sathyanarayanan, A. Di Bacco et al., “Pathway-based identification of biomarkers for targeted therapeutics: personalized oncology with PI3K pathway inhibitors,” Science Translational Medicine, vol. 2, no. 43, p. 43ra55, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. M. S. Carro, W. K. Lim, M. J. Alvarez et al., “The transcriptional network for mesenchymal transformation of brain tumours,” Nature, vol. 463, no. 7279, pp. 318–325, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. K. D. Bromberg, A. Ma'ayan, S. R. Neves, and R. Iyengar, “Design logic of a cannabinoid receptor signaling network that triggers neurite outgrowth,” Science, vol. 320, no. 5878, pp. 903–909, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Saez-Rodriguez, L. G. Alexopoulos, M. S. Zhang, M. K. Morris, D. A. Lauffenburger, and P. K. Sorger, “Comparing signaling networks between normal and transformed hepatocytes using discrete logical models,” Cancer Research, vol. 71, no. 16, pp. 5400–5411, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. O. Rozenblatt-Rosen, R. C. Deo, M. Padi, et al., “Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins,” Nature, vol. 487, pp. 491–495, 2012.
  36. B. Zhang, C. Gaiteri, L. G. Bodea, et al., “Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease,” Cell, vol. 153, no. 3, pp. 707–720, 2013.
  37. E. C. Stites, P. C. Trampont, Z. Ma, and K. S. Ravichandran, “Network analysis of oncogenic Ras activation in cancer,” Science, vol. 318, no. 5849, pp. 463–467, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. D. J. Klinke II, “Signal transduction networks in cancer: quantitative parameters influence network topology,” Cancer Research, vol. 70, no. 5, pp. 1773–1782, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. B. Liu, J. Zhang, P. Y. Tan et al., “A computational and experimental study of the regulatory mechanisms of the complement system,” PLoS Computational Biology, vol. 7, no. 1, Article ID e1001059, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. M. A. Pujana, J.-D. J. Han, L. M. Starita et al., “Network modeling links breast cancer susceptibility and centrosome dysfunction,” Nature Genetics, vol. 39, no. 11, pp. 1338–1349, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. K. M. Mani, C. Lefebvre, K. Wang et al., “A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas,” Molecular Systems Biology, vol. 4, article 169, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. X. Wu, R. Jiang, M. Q. Zhang, and S. Li, “Network-based global inference of human disease genes,” Molecular Systems Biology, vol. 4, article 189, 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, and R. Sharan, “Associating genes and protein complexes with disease via network propagation,” PLoS Computational Biology, vol. 6, no. 1, Article ID 1000641, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Zhao and R. Iyengar, “Systems pharmacology: network analysis to identify multiscale mechanisms of drug action,” Annual Review of Pharmacology and Toxicology, vol. 52, pp. 505–521, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. M. A. Yıldırım, K.-I. Goh, M. E. Cusick, A.-L. Barabási, and M. Vidal, “Drug-target network,” Nature Biotechnology, vol. 25, no. 10, pp. 1119–1126, 2007.
  46. G. V. Paolini, R. H. B. Shapland, W. P. Van Hoorn, J. S. Mason, and A. L. Hopkins, “Global mapping of pharmacological space,” Nature Biotechnology, vol. 24, no. 7, pp. 805–815, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. A. L. Hopkins, “Network pharmacology: the next paradigm in drug discovery,” Nature Chemical Biology, vol. 4, no. 11, pp. 682–690, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin, and B. K. Shoichet, “Relating protein pharmacology by ligand chemistry,” Nature Biotechnology, vol. 25, no. 2, pp. 197–206, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. J. Li, X. Zhu, and J. Y. Chen, “Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts,” PLoS Computational Biology, vol. 5, no. 7, Article ID e1000450, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. M. J. Keiser, V. Setola, J. J. Irwin et al., “Predicting new molecular targets for known drugs,” Nature, vol. 462, no. 7270, pp. 175–181, 2009. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Campillos, M. Kuhn, A.-C. Gavin, L. J. Jensen, and P. Bork, “Drug target identification using side-effect similarity,” Science, vol. 321, no. 5886, pp. 263–266, 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. M. Takarabe, M. Kotera, Y. Nishimura, S. Goto, and Y. Yamanishi, “Drug target prediction using adverse event report systems: a pharmacogenomic approach,” Bioinformatics, vol. 28, no. 18, pp. i611–i618, 2012.
  53. F. Cheng, C. Liu, J. Jiang et al., “Prediction of drug-target interactions and drug repositioning via network-based inference,” PLoS Computational Biology, vol. 8, no. 5, Article ID e1002503, 2012.
  54. E. Lounkine, M. J. Keiser, S. Whitebread et al., “Large-scale prediction and testing of drug activity on side-effect targets,” Nature, vol. 486, no. 7403, pp. 361–367, 2012.
  55. M. Kuhn, M. Al Banchaabouchi, M. Campillos, et al., “Systematic identification of proteins that elicit drug side effects,” Molecular Systems Biology, vol. 9, p. 663, 2013.
  56. L. Yang, J. Chen, and L. He, “Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome,” PLoS Computational Biology, vol. 5, no. 7, Article ID e1000441, 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. L. Yang, K. Wang, J. Chen et al., “Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome—clozapine-induced agranulocytosis as a case study,” PLoS Computational Biology, vol. 7, no. 3, Article ID e1002016, 2011. View at Publisher · View at Google Scholar · View at Scopus
  58. R. L. Chang, L. Xie, L. Xie, P. E. Bourne, and B. Ø. Palsson, “Drug off-target effects predicted using structural analysis in the context of a metabolic network model,” PLoS Computational Biology, vol. 6, no. 9, Article ID e1000938, 2010. View at Publisher · View at Google Scholar · View at Scopus
  59. L. Chen, J. Lu, J. Zhang, K.-R. Feng, M.-Y. Zheng, and Y.-D. Cai, “Predicting chemical toxicity effects based on chemical-chemical interactions,” PLoS ONE, vol. 8, no. 2, Article ID e56517, 2013.
  60. F. Iorio, R. Bosotti, E. Scacheri et al., “Discovery of drug mode of action and drug repositioning from transcriptional responses,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 33, pp. 14621–14626, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Gottlieb, G. Y. Stein, E. Ruppin, and R. Sharan, “PREDICT: a method for inferring novel drug indications with application to personalized medicine,” Molecular Systems Biology, vol. 7, article 496, 2011. View at Publisher · View at Google Scholar · View at Scopus
  62. M. Sirota, J. T. Dudley, J. Kim et al., “Discovery and preclinical validation of drug indications using compendia of public gene expression data,” Science Translational Medicine, vol. 3, no. 96, p. 96ra77, 2011.
  63. G. Jin, C. Fu, H. Zhao, K. Cui, J. Chang, and S. T. C. Wong, “A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy,” Cancer Research, vol. 72, no. 1, pp. 33–44, 2012. View at Publisher · View at Google Scholar · View at Scopus
  64. M. Iskar, G. Zeller, P. Blattmann, et al., “Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding,” Molecular Systems Biology, vol. 9, p. 662, 2013.
  65. J. Jia, F. Zhu, X. Ma, Z. W. Cao, Y. X. Li, and Y. Z. Chen, “Mechanisms of drug combinations: interaction and network perspectives,” Nature Reviews Drug Discovery, vol. 8, no. 2, pp. 111–128, 2009. View at Publisher · View at Google Scholar · View at Scopus
  66. J. B. Fitzgerald, B. Schoeberl, U. B. Nielsen, and P. K. Sorger, “Systems biology and combination therapy in the quest for clinical efficacy,” Nature Chemical Biology, vol. 2, no. 9, pp. 458–466, 2006. View at Publisher · View at Google Scholar · View at Scopus
  67. S. Iadevaia, Y. Lu, F. C. Morales, G. B. Mills, and P. T. Ram, “Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis,” Cancer Research, vol. 70, no. 17, pp. 6704–6714, 2010. View at Publisher · View at Google Scholar · View at Scopus
  68. J. Zou, S.-D. Luo, Y.-Q. Wei, and S.-Y. Yang, “Integrated computational model of cell cycle and checkpoint reveals different essential roles of Aurora-A and Plk1 in mitotic entry,” Molecular BioSystems, vol. 7, no. 1, pp. 169–179, 2011. View at Publisher · View at Google Scholar · View at Scopus
  69. J. Zou, P. Ji, Y.-L. Zhao, et al., “Neighbor communities in drug combination networks characterize synergistic effect,” Molecular BioSystems, vol. 8, no. 12, pp. 3185–3196, 2012.
  70. J. Lehár, A. S. Krueger, W. Avery et al., “Synergistic drug combinations tend to improve therapeutically relevant selectivity,” Nature Biotechnology, vol. 7, pp. 659–666, 2009.
  71. L. N. Kwong, J. C. Costello, H. Liu, et al., “Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma,” Nature Medicine, vol. 18, no. 10, pp. 1503–1510, 2012.
  72. M. J. Lee, A. S. Ye, A. K. Gardino, et al., “Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks,” Cell, vol. 149, no. 4, pp. 780–794, 2012.