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Investigating the Role of Cross-Talk Between Chemical and Stromal Factors in Endothelial Cell Phenotype Determination

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Modeling Tumor Vasculature
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Abstract

Signal transduction is part of a complex communication system responsible for regulating fundamental cellular activities, including metabolism, protein synthesis, division, migration, and survival. The capacity of cells to perceive and precisely respond to their environment is critical to development, tissue repair, and normal tissue homeostasis. Vast amounts of disparate, postgenomic data facilitated by the development of high-throughput technologies are now available and computational modeling is needed to synthesize and understand these data within their systems biology context. In this chapter, we discuss computational approaches available to model signal transduction and present several types of Boolean network models of cell signaling during tumor angiogenesis. Using these models, we investigate the role of cross-talk between chemical and stromal factors in endothelial cell phenotype determination. Understanding the principal mechanisms underpinning the functions of signalling networks will enable identification of targets for pharmacological interventions in the treatment of cancer and other angiogenesis-dependent diseases.

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References

  • R. Albert and H. G. Othmer. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in drosophila melanogaster. J. Theor. Biol., 223(1):1–18, 2003.

    CAS  PubMed  PubMed Central  Google Scholar 

  • M. Aldana, E. Balleza, S. Kauffman, and O. Resendiz. Robustness and evolvability in genetic regulatory networks. J. Theor. Biol., 245:433–448, 2007.

    PubMed  Google Scholar 

  • B. B. Aldridge, J. M. Burke, D. A. Lauffenburger, and P. K. Sorger. Physicochemical modeling of cell signaling pathways. Nat. Cell Biol., 8:1195–1203, 2006.

    CAS  PubMed  Google Scholar 

  • D. Barua, J. R. Faeder, and J. M. Haugh. Structure-based kinetic models of modular signaling protein function: Focus on Shp2. Biophys. J., 92:2290–2300, 2007.

    CAS  PubMed  PubMed Central  Google Scholar 

  • D. Barua, J. R. Faeder, and J. M. Haugh. Computational models of tandem src homology 2 domain interactions and application to phosphoinositide 3-kinase. J. Biol. Chem., 283:7738–7745, 2008.

    Google Scholar 

  • A. L. Bauer, C. A. A. Beauchemin, and A. S. Perelson. Agent-based modeling of host-pathogen systems: The successes and challenges. Information Sciences, 179(10):1379–1389, 2009.

    PubMed  Google Scholar 

  • A. L. Bauer, T. L. Jackson, Y. Jiang, and T. Rohlf. Receptor cross-talk in angiogenesis: Mapping environmental cues to cell phenotype using a stochastic, boolean signaling network model. J. Theor. Biol., 286:838–846, 2010.

    Google Scholar 

  • U. S. Bhalla and R. Iyengar. Emergent properties of networks of biological signaling pathways. Science, 284:92–96, 1999.

    PubMed  PubMed Central  Google Scholar 

  • M. L. Blinov, J. R. Faeder, B. Goldstein, and W. S. Hlavacek. Bionetgen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics, 83:3289–3291, 2006.

    Google Scholar 

  • M. L. Blinov, J. R. Faeder, B. Goldstein, and W. S. Hlavacek. A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. Biosystems, 83:136–151, 2006.

    CAS  PubMed  Google Scholar 

  • S. Bornholdt and T. Rohlf. Topological evolution of dynamical networks: Global criticality from local dynamics. Phys. Rev. Lett., 84:6114–6117, 2000.

    CAS  PubMed  Google Scholar 

  • S. Bornholdt and K. Sneppen. Neutral mutations and punctuated equilibrium in evolving genetic networks. Phys. Rev. Lett., 81:236–239, 1998.

    CAS  Google Scholar 

  • S. Braunewell and S. Bornholdt. Superstability of the yeast cell-cycle dynamics: Ensuring causality in the presence of biochemical stochasticity. J. Theor. Biol., 245(4):638–643, 2007.

    CAS  PubMed  Google Scholar 

  • M. Chaves, R. Albert, and E. D. Sontag. Robustness and fragility of Boolean models for genetic regulatory networks. J. Theor. Biol., 235:431–449, 2005.

    PubMed  Google Scholar 

  • C. S. Chen, M. Mrksich, S. Huang, G. M. Whitesides, and D. E. Ingber. Micropatterned surfaces for control of cell shape, position, and function. Biotechnol. Prog., 14:356–363, 1998.

    CAS  PubMed  Google Scholar 

  • J. Colvin, M. I. Monine, J. R. Faeder, W. S. Hlavacek, D. D. Von Hoff, and R. G. Posner. Simulation of large-scale rule-based models. Bioinformatics, 25:910–917, 2009.

    CAS  PubMed  PubMed Central  Google Scholar 

  • M. I. Davidich and S. Bornholdt. Boolean network model predicts cell cycle sequence of fission yeast. arXiv: q-bio, page 1313440, 2007.

    Google Scholar 

  • G. E. Davis and D. R. Senger. Endothelial extracellular matrix: Biosynthesis, remodeling, and functions during vascular morphogenesis and neovessel stabilization. Circ. Res., 97:1093 – 1107, 2005.

    CAS  PubMed  Google Scholar 

  • B. Derrida and Y. Pomeau. Random networks of automata: A simple annealed approximation. Europhys. Lett., 1(2):45–49, 1986.

    Google Scholar 

  • X. Dong, P. T. Foteinou, S. E. Calvano, S. F. Lowry, and I. P. Androulakis. Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS ONE, 5(2):e9249, 2010.

    PubMed  PubMed Central  Google Scholar 

  • N. J. Eungdamrong and R. Iyengar. Modeling cell signaling networks. Biol. Cell, 96:355–362, 2004.

    CAS  PubMed  PubMed Central  Google Scholar 

  • J. Folkman. Angiogenesis. Annu. Rev. Med., 57:1–18, 2006.

    CAS  PubMed  Google Scholar 

  • F. Mac Gabhann and A. S. Popel. Dimerization of VEGF receptors and implications for signal transduction: a computational study. Biophys. Chem., 128:125–139, 2007.

    CAS  PubMed  PubMed Central  Google Scholar 

  • H. Gerhardt, M. Golding, M. Fruttiger, C. Ruhrberg, A. Lundkvist, A. Abramsson, M. Jeltsch, C. Mitchell, K. Alitalo, D. Shima, and C. Betsholtz. VEGF guides angiogenic sprouting utilizing endothelial tip cell filopodia. J. Cell Biol., 161:1163–1177, 2003.

    CAS  PubMed  PubMed Central  Google Scholar 

  • C. Gershenson. Classification of random boolean networks. In R. K. Standish, M. A. Bedau, and H. A. Abbass, editors, Artificial Life VIII:Proceedings of the Eight International Conference on Artificial Life, pages 1–8, 2002.

    Google Scholar 

  • C. Gershenson. Updating schemes in random Boolean networks: Do they really matter? In ARTIFICIAL LIFE IX, pages 238–243. M I T PRESS, 2004.

    Google Scholar 

  • D. T. Gillespie. A general method for numerically simulating the stochastic time evolution of coupled reaction equations. J. Comp. Phys., 22:403–434, 1976.

    CAS  Google Scholar 

  • L. Glass. The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol., 39:103–129, 1973.

    CAS  PubMed  Google Scholar 

  • L. Glass. Classification of biological networks by their qualitative dynamics. J. Theor. Biol., 54:85–107, 1975.

    CAS  PubMed  Google Scholar 

  • C. J. Gottardi, E. Wong, and B. M. Gumbiner. E-cadherin suppresses cellular transformation by inhibiting beta-catenin signaling in an adhesion-independent manner. J. Cell Biol., 153(5):1049–1060, 2001.

    CAS  PubMed  PubMed Central  Google Scholar 

  • D. J. Hicklin and L. M. Ellis. Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J. Clin. Onc., 23(5):1011–1027, 2005.

    CAS  Google Scholar 

  • W. S. Hlavacek, J. R. Faeder, M. L. Blinov, A. S. Perelson, and B. Goldstein. The complexity of complexes in signal transduction. Biotechnol. Bioeng., 84:861–876, 2003.

    Google Scholar 

  • M. Hsing, J. L. Bellenson, C. Shankey, and A. Cherkasov. Modeling of cell signaling pathways in macrophages by semantic networks. BMC Bioinformatics, 5:156–169, 2004.

    PubMed  PubMed Central  Google Scholar 

  • M. Hsing and A. Cherkasov. Integration of biological data with semantic networks. Curr. Bioinform., 1(3):273–290, 2006.

    CAS  Google Scholar 

  • B. Hu, G. M. Fricke, J. R. Faeder, R. G. Posner, and W. S. Hlavacek. Getbonnie for building, analyzing and sharing rule-based models. Bioinformatics, 25:1457–1460, 2009.

    CAS  PubMed  PubMed Central  Google Scholar 

  • S. Huang and D. E. Ingber. The structural and mechanical complexity of cell-growth control. Nat. Cell Biol., 1(5):E131–E138, 1999.

    CAS  PubMed  Google Scholar 

  • H. Hutchings, N. Ortega, and J. Plouet. Extracellular matrix-bound vascular endothelial growth factor promotes endothelial cell adhesion, migration, and survival through integrin ligation. FASEB J., 17(11):1520–1522, 2003.

    CAS  PubMed  Google Scholar 

  • E. R. Jackson, D. Johnson, and W. G. Nash. Gene networks in development. J. Theor. Biol., 119(4):379–396, 1986.

    CAS  PubMed  Google Scholar 

  • H. V Jain, J. E. Nor, and T. L. Jackson. Modeling the VEGF-Bcl2-CXCL8 pathway in intratumoral angiogenesis. Bull. Math. Biol., 70(1):89–117, 2008.

    PubMed  Google Scholar 

  • S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol., 22:437–467, 1969.

    CAS  PubMed  Google Scholar 

  • S. A. Kauffman. The origins of order: Self-organization and selection in evolution. Oxford University Press, first edition, 1993.

    Google Scholar 

  • B. N. Kholodenko. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem., 267:1583–1588, 2000.

    CAS  PubMed  Google Scholar 

  • B. N. Kholodenko. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol., 7:165–176, 2006.

    CAS  PubMed  PubMed Central  Google Scholar 

  • K. Klemm and S. Bornholdt. Robust gene regulation: Deterministic dynamics from asynchronous networks with delay. arXiv: q-bio, page 0309013, 2003.

    Google Scholar 

  • K. Klemm and S. Bornholdt. Stable and unstable attractors in Boolean networks. Phys. Rev. E, 72:055101(R), 2005.

    Google Scholar 

  • H. A. Levine, A. L. Tucker, and M. Nilsen-Hamilton. A mathematical model for the role of cell signal transduction in the initiation and inhibition of angiogenesis. Growth Factors, 20(4):155–175, 2002.

    CAS  PubMed  Google Scholar 

  • S. Li, S. M. Assmann, and R. Albert. Predicting essential components of signal transduction networks: A dynamic model of guard cell abscisic acid signaling. PLoS Biology, 4(10):1732– 1748, 2006.

    CAS  Google Scholar 

  • J. Lilien and J. Balsamo. The regulation of cadherin-mediated adhesion by tyrosine phosphorylation/deposphorylation of β-catenin. Curr. Opin. Cell Biol., 17:459–465, 2005.

    CAS  PubMed  Google Scholar 

  • N. V. Mantzaris, S. Webb, and H. G. Othmer. Mathematical modeling of tumor-induced angiogenesis. J. Math. Biol., 49:111–187, 2004.

    PubMed  Google Scholar 

  • J. Miller, M. Parker, R B. Bourret, and M. C. Giddings. An agent-based model of signal transduction in bacterial chemotaxis. PLoS ONE, 5(5):e9454, 2010.

    PubMed  PubMed Central  Google Scholar 

  • E. N. Miranda and N. Parga. Noise effects in the kauffman model. Europhys. Lett., 10:293, 1989.

    Google Scholar 

  • F. Mu, R. F. Williams, C. J. Unkefer, P. J. Unkefer, J. R. Faeder, and W. S. Hlavacek. Carbon fate maps for metabolic reactions. Bioinformatics, 23:3193–3199, 2007.

    CAS  PubMed  Google Scholar 

  • J. E. Nör, J. Christensen, D. J. Mooney, and P. J. Polverini. Vascular endothelial growth factor (VEGF)-mediated angiogenesis is associated with enhanced endothelial cell survival and induction of bcl-2 expression. Am. J. Path., 154(2):375–384, 1999.

    PubMed  PubMed Central  Google Scholar 

  • KEGG: Kyoto Encyclopedia of Genes and Genomes, 1995–2007.

    Google Scholar 

  • N. Paweletz and M. Knierim. Tumor related angiogenesis. Crit. Rev. Oncol. Hematol., 9:197–242, 1989.

    CAS  PubMed  Google Scholar 

  • T. P. Peixoto and B. Drossel. Noise in random boolean networks. Phys. Rev. E, 79(3):036108, Mar 2009.

    Google Scholar 

  • C. A. Petri. Nets, time and space. Theoret. Comp. Sci., 153:3–48, 2009.

    Google Scholar 

  • M. Pogson, M. Holcombe, R. Smallwood, and E. Qwarnstrom. Introducing spatial information into predictive nf-κb modeling – an agent-based approach. PLoS ONE, 3(6):e2367, 2008.

    PubMed  PubMed Central  Google Scholar 

  • T. Rohlf and S. Bornholdt. Self-organized pattern formation and noise-induced control based on particle computations. JSTAT, L12001:379–396, 2005.

    Google Scholar 

  • R. Rubenstein, P. C. Gray, T. J. Cleland, M. S. Piltch, W. S. Hlavacek, R. M. Roberts, J. Ambrosiano, and J.-I. Kim. Dynamics of the nucleated polymerization model of prion replication. Biophys. Chem., 125:360–367, 2007.

    CAS  PubMed  Google Scholar 

  • E. Ruoslahti and J. C. Reed. Anchorage dependence, integrins, and apoptosis. Cell, 77:477–478, 1994.

    CAS  PubMed  Google Scholar 

  • B. Samuelsson and C. Troein. Superpolynomial growth in the number of attractors in kauffman networks. Phys. Rev. Lett, 90:098701, 2003.

    PubMed  Google Scholar 

  • A. C. Vaiana and K. Y. Sanbonmatsu. Stochastic gating and drugribosome interactions. J. Mol. Biol., 386(3):648–661, 2009.

    CAS  PubMed  Google Scholar 

  • F. H. Silver, J. W. Freeman, and G. P. Seehra. Collagen self-assembly and the development of tendon mechanical properties. J. Biomech., 36:1529–1553, 2003.

    PubMed  Google Scholar 

  • A. E. Smith, B. M. Slepchenko, J. C. Schaff, L. M. Loew, and I. G. Macara. Systems analysis of Ran transport. Science, 295:488–491, 2002.

    CAS  PubMed  Google Scholar 

  • P. R. Somanath, A. Ciocea, and T. V. Byzova. Integrin and growth factor receptor alliance in angiogenesis. Cell Biochem. Biophys., 53(2):53–64, 2009.

    CAS  PubMed  Google Scholar 

  • G. von Dassow, E. Meir, E. M. Munro, and G. M. Odell. The segment polarity network is a robust developmental module. Nature, 406:188–192, 2000.

    Google Scholar 

  • G. von Dassow and G. M. Odell. Design and constraints of the drosophila segment polarity module: robust spatial patterning emerges from intertwined cell state switches. J. Exp. Zool., 294:179–215, 2002.

    Google Scholar 

  • S. Wolfram. Cellular automata as models of complexity. Nature, 311(5985):419–424, 1984.

    Google Scholar 

  • G. D. Yancopoulos, S. Davis, N. W. Gale, J. S. Rudge, S. J. Wiegand, and J. Holash. Vascular-specific growth factors and blood vessel formation. Nature, 407:242–248, 2000.

    CAS  PubMed  Google Scholar 

  • A. Zanetti, M. G. Lampugnani, G. Balconi, F. Breviario, M. Corada, L. Lanfrancone, and E. Dejana. Vascular endothelial growth factor induces shc association with vascular endothelial cadherin: A potential feedback mechanism to control vascular endothelial growth factor receptor-2 signaling. Arterioscler. Thromb. Vasc. Biol., 22:617–622, 2002.

    CAS  PubMed  Google Scholar 

  • Q. Zhang, H. H. Petersen, H. Ostergaard, W. Ruf, and A. J. Olson. Molecular dynamics simulations and functional characterization of the interactions of the PAR2 ectodomain with factor viia. Proteins, 77(3):559–569, 2009.

    PubMed  PubMed Central  Google Scholar 

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Bauer, A.L., Rohlf, T. (2012). Investigating the Role of Cross-Talk Between Chemical and Stromal Factors in Endothelial Cell Phenotype Determination. In: Jackson, T.L. (eds) Modeling Tumor Vasculature. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0052-3_4

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