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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hanahan, D. and R.A. Weinberg. The hallmarks of cancer. Cell 100: 57–70, 2000.
Ge, H., A.J. Walhout, and M. Vidal. Integrating ‘omic' information: a bridge between genomics and systems biology. Trends Genet 19: 551–60, 2003.
Bruggeman, F.J., J.J. Hornberg, F.C. Boogerd, and H.V. Westerhoff. Introduction to systems biology. Exs 97: 1–19, 2007.
Bruggeman, F.J. and H.V. Westerhoff. The nature of systems biology. Trends Microbiol 15: 45–50, 2007.
Butcher, E.C., E.L. Berg, and E.J. Kunkel. Systems biology in drug discovery. Nat Biotechnol 22: 1253–9, 2004.
Kitano, H. Computational systems biology. Nature 420: 206–10, 2002.
Khalil, I.G. and C. Hill. Systems biology for cancer. Curr Opin Oncol 17: 44–8, 2005.
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.
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.
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.
Dayananda, P.W., J.T. Kemper, and M.M. Shvartsman. A stochastic model for prostate-specific antigen levels. Math Biosci 190: 113–26, 2004.
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.
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.
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.
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.
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.
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.
Driscoll, T. and R. Mitchell. Fatal work injuries in New South Wales. N S W Public Health Bull 13: 95–9, 2002.
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.
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.
Conti, D.V., V. Cortessis, J. Molitor, and D.C. Thomas. Bayesian modeling of complex metabolic pathways. Hum Hered 56: 83–93, 2003.
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.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0772-1_17
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-0771-4
Online ISBN: 978-1-4419-0772-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)