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Systems Approach for Understanding Metastasis

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Lung Cancer Metastasis

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Abstract

A systems approach to analysis is based on the belief that the component parts of a system will act differently when isolated from its environment or other parts of the system. In other words, the whole is greater than the sum of its parts due to the relationship and the interaction between the parts. In biology, the goal of a systems approach is to understand the operation of complex biological systems by providing the missing link between molecules and physiology. Currently systems biology encompasses many different approaches with an ultimate aim of developing predictive models for complex human diseases including cancer. This chapter will highlight some of the tools and efforts of systems biology that are applied to cancer and will discuss how these efforts can be further extended to the much needed understanding and targeting of lung tumor metastasis.

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References

  1. Hanahan, D. and R.A. Weinberg. The hallmarks of cancer. Cell 100: 57–70, 2000.

    Article  PubMed  CAS  Google Scholar 

  2. Ge, H., A.J. Walhout, and M. Vidal. Integrating ‘omic' information: a bridge between genomics and systems biology. Trends Genet 19: 551–60, 2003.

    Article  PubMed  CAS  Google Scholar 

  3. Bruggeman, F.J., J.J. Hornberg, F.C. Boogerd, and H.V. Westerhoff. Introduction to systems biology. Exs 97: 1–19, 2007.

    PubMed  CAS  Google Scholar 

  4. Bruggeman, F.J. and H.V. Westerhoff. The nature of systems biology. Trends Microbiol 15: 45–50, 2007.

    Article  PubMed  CAS  Google Scholar 

  5. Butcher, E.C., E.L. Berg, and E.J. Kunkel. Systems biology in drug discovery. Nat Biotechnol 22: 1253–9, 2004.

    Article  PubMed  CAS  Google Scholar 

  6. Kitano, H. Computational systems biology. Nature 420: 206–10, 2002.

    Article  PubMed  CAS  Google Scholar 

  7. Khalil, I.G. and C. Hill. Systems biology for cancer. Curr Opin Oncol 17: 44–8, 2005.

    Article  PubMed  CAS  Google Scholar 

  8. Zhu, Y., H. Li, D.J. Miller, Z. Wang, J. Xuan, R. Clarke, E.P. Hoffman, and Y. Wang. caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data. BMC Bioinformatics 9: 383, 2008.

    Article  PubMed  Google Scholar 

  9. Zhang, R., M.V. Shah, J. Yang, S.B. Nyland, X. Liu, J.K. Yun, R. Albert, and T.P. Loughran, Jr. Network model of survival signaling in large granular lymphocyte leukemia. Proc Natl Acad Sci USA 105: 16308–13, 2008.

    Article  PubMed  CAS  Google Scholar 

  10. Hornberg, J.J., B. Binder, F.J. Bruggeman, B. Schoeberl, R. Heinrich, and H.V. Westerhoff. Control of MAPK signalling: from complexity to what really matters. Oncogene 24: 5533–42, 2005.

    Article  PubMed  CAS  Google Scholar 

  11. Dayananda, P.W., J.T. Kemper, and M.M. Shvartsman. A stochastic model for prostate-specific antigen levels. Math Biosci 190: 113–26, 2004.

    Article  PubMed  CAS  Google Scholar 

  12. Shannon, P., A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498–504, 2003.

    Article  PubMed  CAS  Google Scholar 

  13. Gansner, E.R. and S.C. North. An open graph visualization system and its applications to software engineering. Software Practice and Experience 30: 1203–33, 2000.

    Article  Google Scholar 

  14. Burnside, E.S., D.L. Rubin, J.P. Fine, R.D. Shachter, G.A. Sisney, and W.K. Leung. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology 240: 666–73, 2006.

    Article  PubMed  Google Scholar 

  15. Cruz-Ramirez, N., H.G. Acosta-Mesa, H. Carrillo-Calvet, L.A. Nava-Fernandez, and R.E. Barrientos-Martinez. Diagnosis of breast cancer using Bayesian networks: a case study. Comput Biol Med 37: 1553–64, 2007.

    Article  PubMed  Google Scholar 

  16. Antal, P., G. Fannes, D. Timmerman, Y. Moreau, and B. De Moor. Using literature and data to learn Bayesian networks as clinical models of ovarian tumors. Artif Intell Med 30: 257–81, 2004.

    Article  PubMed  Google Scholar 

  17. Werhli, A.V., M. Grzegorczyk, and D. Husmeier. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22: 2523–31, 2006.

    Article  PubMed  CAS  Google Scholar 

  18. Driscoll, T. and R. Mitchell. Fatal work injuries in New South Wales. N S W Public Health Bull 13: 95–9, 2002.

    Article  PubMed  Google Scholar 

  19. Keshamouni, V.G., P. Jagtap, G. Michailidis, J.R. Strahler, R. Kuick, A.K. Reka, P. Papoulias, R. Krishnapuram, A. Srirangam, T.J. Standiford, P.C. Andrews, and G.S. Omenn. Temporal quantitative proteomics by iTRAQ 2D-LC-MS/MS and corresponding mRNA expression analysis identify post-transcriptional modulation of actin-cytoskeleton regulators during TGF-beta-Induced epithelial-mesenchymal transition. J Proteome Res 8: 35–47, 2009.

    Article  PubMed  CAS  Google Scholar 

  20. Chang, J.H., K.B. Hwang, S.J. Oh, and B.T. Zhang. Bayesian network learning with feature abstraction for gene-drug dependency analysis. J Bioinform Comput Biol 3: 61–77, 2005.

    Article  PubMed  CAS  Google Scholar 

  21. Conti, D.V., V. Cortessis, J. Molitor, and D.C. Thomas. Bayesian modeling of complex metabolic pathways. Hum Hered 56: 83–93, 2003.

    Article  PubMed  Google Scholar 

  22. Smith, V.A., J. Yu, T.V. Smulders, A.J. Hartemink, and E.D. Jarvis. Computational inference of neural information flow networks. PLoS Comput Biol 2: e161, 2006.

    Google Scholar 

  23. Tucker, A., V. Vinciotti, X. Liu, and D. Garway-Heath. A spatio-temporal Bayesian network classifier for understanding visual field deterioration. Artif Intell Med 34: 163–77, 2005.

    Article  PubMed  Google Scholar 

  24. Xiang, Z., R.M. Minter, X. Bi, P.J. Woolf, and Y. He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis. Bioinformatics 23: 2423–32, 2007.

    Article  PubMed  CAS  Google Scholar 

  25. Dojer, N., A. Gambin, A. Mizera, B. Wilczynski, and J. Tiuryn. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics 7: 249, 2006.

    Google Scholar 

  26. Li P., C. Zhang, E.J. Perkins, P. Gong, and Y. Deng. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics, 8 Suppl 7: S13, 2007.

    Google Scholar 

  27. Kim, S., S. Imoto, and S. Miyano. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems, 75(1-3): 57–65, 2004.

    Google Scholar 

  28. Kim, S.Y., Imoto, and S. Miyano. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform, 4(3): 228–235, 2003.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering and Biomedical Engineering, University of Michigan, 48109, Ann Arbor, MI, USA

    Peter J. Woolf

  2. Department of Chemical Engineering, University of Michigan, 48109, Ann Arbor, MI, USA

    Peter J. Woolf & Angel Alvarez

  3. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, 48109, Ann Arbor, MI, USA

    Venkateshwar G. Keshamouni Ph.D.

Authors
  1. Peter J. Woolf
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  2. Angel Alvarez
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  3. Venkateshwar G. Keshamouni Ph.D.
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Corresponding author

Correspondence to Peter J. Woolf .

Editor information

Editors and Affiliations

  1. Dept. Internal Medicine, University of Michigan Health System, Zina Pitcher Place 109, Ann Arbor, 48109-2200, U.S.A.

    Venkateshwar Keshamouni

  2. Dept. Internal Medicine, University of Michigan, E. Medical Center Drive 1500, Ann Arbor, 48109, U.S.A.

    Douglas Arenberg

  3. Dept. Internal Medicine, University of Michigan, E. Medical Center Drive 1500, Ann Arbor, 48109, U.S.A.

    Gregory Kalemkerian

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© 2009 Springer Science+Business Media, LLC

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Woolf, P.J., Alvarez, A., Keshamouni, V.G. (2009). Systems Approach for Understanding Metastasis. In: Keshamouni, V., Arenberg, D., Kalemkerian, G. (eds) Lung Cancer Metastasis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0772-1_17

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