References
1 Kleihues, P, Burger, PC, Scheithauer, BW. The new WHO classification of brain tumours. Brain Pathol 1993, 3:255–268.
2 Cunliffe, CH, Fischer, I, Parag, Y, Fowkes, ME. State‐of‐the‐art pathology: new WHO classification, implications, and new developments. Neuroimaging Clin N Am 2010, 20:259–271.
3 Diab, SG, Clark, GM, Osborne, CK, Libby, A, Allred, DC, Elledge, RM. Tumor characteristics and clinical outcome of tubular and mucinous breast carcinomas. J Clin Oncol 1999, 17:1442–1448.
4 O`Brien, KM, Cole, SR, Tse, C‐K, Perou, CM, Carey, LA, Foulkes, WD, Dressler, LG, Geradts, J, Millikan, RC. Intrinsic breast tumor subtypes, race, and long‐term survival in the Carolina breast cancer study. Clin Cancer Res 2010, 16:6100–6110.
5 Prat, A, Parker, J, Karginova, O, Fan, C, Livasy, C, Herschkowitz, J, He, X, Perou, C. Phenotypic and molecular characterization of the claudin‐low intrinsic subtype of breast cancer. Breast Cancer Res 2010, 12:R68.
6 Rickman, DS, Bobek, MP, Misek, DE, Kuick, R, Blaivas, M, Kurnit, DM, Taylor, J, Hanash, SM. Distinctive molecular profiles of high‐grade and low‐grade gliomas based on oligonucleotide microarray analysis. Cancer Res 2001, 61:6885–6891.
7 Vitucci, M, Hayes, DN, Miller, CR. Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy. Br J Cancer 2010, 104:545–553.
8 Chang, HY, Nuyten, DS, Sneddon, JB, Hastie, T, Tibshirani, R, Sorlie, T, Dai, H, He, YD, van`t Veer, LJ, Bartelink, H, et al. Robustness, scalability, and integration of a wound‐response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A 2005, 102:3738–3743.
9 Hedenfalk, I, Duggan, D, Chen, Y, Radmacher, M, Bittner, M, Simon, R, Meltzer, P, Gusterson, B, Esteller, M, Kallioniemi, OP, et al. Gene‐expression profiles in hereditary breast cancer. N Engl J Med 2001, 344: 539–548.
10 Jordan, BF, Runquist, M, Raghunand, N, Baker, A, Williams, R, Kirkpatrick, L, Powis, G, Gillies, RJ. Dynamic contrast‐enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF‐1
α using PX‐478. Neoplasia 2005, 7:475–485.
11 Liotta, LA, Kohn, EC. The microenvironment of the tumour‐host interface. Nature 2001, 411:375–379.
12 Bronchud, MH, Foote, M, Giaccone, G, Olopade, O, Workman, P, eds.
Principles of Molecular Oncology.
3rd ed. NJ:
Humana Press Inc.; 2008.
13 Croce, CM. Oncogenes and cancer. N Engl J Med 2008, 358:502–511.
14 Robertson, KD. DNA methylation and human disease. Nat Rev Genet 2005, 6:597–610.
15 Visvader, JE. Cells of origin in cancer. Nature 2011, 469:314–322.
16 Burrage, K, Burrage, P, Leier, A, Marquez‐Lago, TT, Nicolau, DV. %22Stochastic simulation for spatial modelling of dynamic processes in a living cell.%22 In: Koeppl, H, Setti, G, di Bernardo, M, Densmore, D, eds.
Design and Analysis of Bio‐molecular Circuits. New York:
Springer; 2011.
17 Leier, A, Marquez‐Lago, TT, Burrage, K. Generalized binomial tau‐leap method for biochemical kinetics incorporating both delay and intrinsic noise. J Chem Phys 2008, 128:205107.
18 Marquez‐Lago, TT, Leier, A, Burrage, K. Probability distributed time delays: integrating spatial effects into temporal models. BMC Syst Biol 2010, 4:19.
19 Marquez‐Lago, TT, Burrage, K. Binomial tau‐leap spatial stochastic simulation algorithm for applications in chemical kinetics. J Chem Phys 2007, 127:104101.
20 Andrews, SS, Addy, NJ, Brent, R, Arkin, AP. Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 2010, 6:e1000705.
21 Pogson, M, Smallwood, R, Qwarnstrom, E, Holcombe, M. Formal agent‐based modelling of intracellular chemical interactions. Biosystems 2006, 85:37–45.
22 Blinov, ML, Faeder, JR, Goldstein, B, Hlavacek, WS. BioNetGen: software for rule‐based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 2004, 20:3289–3291.
23 Bauer, AL, Jackson, TL, Jiang, Y, Rohlf, T. Receptor cross‐talk in angiogenesis: mapping environmental cues to cell phenotype using a stochastic, Boolean signaling network model. J Theor Biol 2010, 264:838–846.
24 Birtwistle, MR, Hatakeyama, M, Yumoto, N, Ogunnaike, BA, Hoek, JB, Kholodenko, BN. Ligand‐dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 2007, 3:144.
25 Borisov, N, Aksamitiene, E, Kiyatkin, A, Legewie, S, Berkhout, J, Maiwald, T, Kaimachnikov, NP, Timmer, J, Hoek, JB, Kholodenko, BN. Systems‐level interactions between insulin‐EGF networks amplify mitogenic signaling. Mol Syst Biol 2009, 5:256.
26 Hsieh, M‐Y, Yang, S, Raymond‐Stinz, MA, Steinberg, S, Vlachos, DG, Shu, W, Wilson, B, Edwards, JS. Stochastic simulations of ErbB homo and heterodimerisation: potential impacts of receptor conformational state and spatial segregation. IET Syst Biol 2008, 2:256–272.
27 Walker, DC, Georgopoulos, NT, Southgate, J. From pathway to population—a multiscale model of juxtacrine EGFR‐MAPK signalling. BMC Syst Biol 2008, 2:102.
28 Kholodenko, BN, Kolch, W. Giving space to cell signaling. Cell 2008, 133:566–567.
29 Tian, T, Harding, A, Inder, K, Plowman, S, Parton, RG, Hancock, JF. Plasma membrane nanoswitches generate high‐fidelity Ras signal transduction. Nat Cell Biol 2007, 9:905–914.
30 Morgan, DO.
The Cell Cycle: Principles of Control. London:
New Science Press, Ltd.; 2007.
31 Ballestar, E, Esteller, M. Epigenetic gene regulation in cancer. Adv Genet 2008, 61:247–267.
32 Bell, DW. Our changing view of the genomic landscape of cancer. J Pathol 2010, 220:231–243.
33 Berdasco, M, Esteller, M. Aberrant epigenetic landscape in cancer: how cellular identity goes awry. Dev Cell 2010, 19:698–711.
34 Kristensen, LS, Nielsen, HM, Hansen, LL. Epigenetics and cancer treatment. Eur J Pharmacol 2009, 625:131–142.
35 Rejniak, KA, Anderson, ARA. Hybrid models of tumor growth. WIREs Syst Biol Med 2011, 3:115–125.
36 Allred, DC, Wu, Y, Mao, S, Nagtegaal, ID, Lee, S, Perou, CM, Mohsin, SK, O`Connell, P, Tsimelzon, A, Medina, D. Ductal carcinoma
in situ and the emergence of diversity during breast cancer evolution. Clin Cancer Res 2008, 14:370–378.
37 Bankhead, A III, Magnuson, NS, Heckendorn, RB. Cellular automaton simulation examining progenitor hierarchy structure effects on mammary ductal carcinoma
in situ. J Theor Biol 2007, 246:491–498.
38 Macklin, P, Edgerton, ME, Thompson, AM, Cristini, V. Patient‐calibrated agent‐based modelling of ductal carcinoma
in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression. J Theor Biol. Submitted for publication.
39 Rejniak, KA. An immersed boundary framework for modelling the growth of individual cells: an application to the early tumour development. J Theor Biol 2007, 247:186–204.
40 Rejniak, KA, Anderson, ARA. A computational study of the development of epithelial acini: I. Sufficient conditions for the formation of a hollow structure. Bull Math Biol 2008, 70:677–712.
41 Rejniak, KA, Anderson, ARA. A computational study of the development of epithelial acini: II. Necessary conditions for structure and lumen stability. Bull Math Biol 2008, 70:1450–1479.
42 Rejniak, KA, Dillon, RH. A single cell‐based model of the ductal tumour microarchitecture. Comput Math Methods Med 2007, 8:51–69.
43 Norton, KA, Wininger, M, Bhanot, G, Ganesan, S, Barnard, N, Shinbrot, T. A 2D mechanistic model of breast ductal carcinoma
in situ (DCIS) morphology and progression. J Theor Biol 2010, 263:393–406.
44 Jiang, Y, Pjesivac‐Grbovic, J, Cantrell, C, Freyer, JP. A multiscale model for avascular tumor growth. Biophys J 2005, 89:3884–3894.
45 Rubenstein, BM, Kaufman, LJ. The Role of Extracellular Matrix in Glioma Invasion: A Cellular Potts Model Approach. Biophys J 2008, 95:5661–5680.
46 Shirinifard, A, Gens, JS, Zaitlen, BL, Poplawski, NJ, Swat, M, Glazier, JA. 3D Multi‐Cell Simulation of Tumor Growth and Angiogenesis. PLoS One 2009, 4:e7190.
47 Gatenby, RA, Smallbone, K, Maini, PK, Rose, F, Averill, J, Nagle, RB, Worrall, L, Gillies, RJ. Cellular adaptations to hypoxia and acidosis during somatic evolution of breast cancer. Br J Cancer 2007, 97:646–653. PMCID: 2360372.
48 Silva, AS, Gatenby, RA, Gillies, RJ, Yunes, JA. A quantitative theoretical model for the development of malignancy in ductal carcinoma
in situ. J Theor Biol 2010, 262:601–613.
49 Smallbone, K, Gatenby, RA, Gillies, RJ, Maini, PK, Gavaghan, DJ. Metabolic changes during carcinogenesis: potential impact on invasiveness. J Theor Biol 2007, 244:703–713.
50 Macklin, P, Edgerton, ME, Cristini, V. %22Agent‐based cell modeling: application to breast cancer.%22 In: Cristini, V, Lowengrub, JS.
Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. New York:
Cambridge University Press; 2010, 206–234.
51 Macklin, P, Edgerton, ME, Lowengrub, JS, Cristini, V. %22Discrete cell modeling.%22 In: Cristini, V, Lowengrub, JS.
Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. New York:
Cambridge University Press; 2010, 88–122.
52 Macklin, P, Kim, J, Tomaiuolo, G, Edgerton, ME, Cristini, V. %22Agent‐based modeling of ductal carcinoma
in situ: application to patient‐specific breast cancer modeling.%22 In: Pham, T, ed.
Computational Biology: Issues and Applications in Oncology. New York:
Springer; 2009, 77–112.
53 Deisboeck, TS, Wang, Z, Macklin, P, Cristini, V. Multiscale cancer modeling. Annu Rev Biomed Eng 2011, 13.
54 Lowengrub, J, Frieboes, HB, Jin, F, Chuang, Y‐L, Li, X, Macklin, P, Wise, SM, Cristini, V. Nonlinear modeling of cancer: bridging the gap between cells and tumors. Nonlinearity 2010, 23:R1–R91.
55 Gatenby, RA, Gillies, RJ. A microenvironmental model of carcinogenesis. Nat Rev Cancer 2007, 8:56–61.
56 Eftimie, R, Bramson, JL, Earn, DJD. Interactions between the immune system and cancer: a brief review of non‐spatial mathematical models. Bull Math Biol 2011, 73:2–32.
57 Clairambault, J. Modelling physiological and pharmacological control on cell proliferation to optimise cancer treatments. Math Model Nat Phenom 2009, 4:12–67.
58 Pathmanathan, P, Gavaghan, DJ, Whiteley, JP, Chapman, SJ, Brady, JM. Predicting tumor location by modeling the deformation of the breast. IEEE Trans Biomed Eng 2008, 55:2471–2480.
59 Hogea, C, Biros, G, Abraham, F, Davatzikos, C. A robust framework for soft tissue simulations with application to modeling brain tumor mass effect in 3D MR images. Phys Med Biol 2007, 52:6893–6908.
60 Ambrosi, D, Preziosi, L. Cell adhesion mechanisms and stress relaxation in the mechanics of tumours. Biomech Model Mechanobiol 2009, 8:397–413.
61 Unnikrishnan, GU, Unnikrishnan, VU, Reddy, JN, Lim, CT. Review on the constitutive models of tumor tissue for computational analysis. Appl Mech Rev 2010, 63:040801.
62 Wang, C, Rockhill, J, Mrugala, M, Peacock, D, Lai, A, Jusenius, K, Wardlaw, J, Cloughesy, T, Spence, A, Rockne, R, et al. Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. Cancer Res 2009, 69:9133.
63 Eikenberry, SE, Sankar, T, Preul, MC, Kostelich, EJ, Thalhauser, CJ, Kuang, Y. Virtual glioblastoma: growth, migration and treatment in a three‐dimensional mathematical model. Cell Prolif 2009, 42:511–528.
64 McDougall, SR, Anderson, ARA, Chaplain, MAJ. Mathematical modelling of dynamic adaptive tumour‐induced angiogenesis: clinical implications and therapeutic targeting strategies. J Theor Biol 2006, 241: 564–589.
65 Frieboes, HB, Edgerton, ME, Fruehauf, JP, Rose, FR, Worrall, LK, Gatenby, RA, Ferrari, M, Cristini, V. Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Res 2009, 69:4484–4492.
66 Byrne, H, Preziosi, L. Modelling solid tumour growth using the theory of mixtures. Math Med Biol 2003, 20:341–366.
67 Preziosi, L, Tosin, A. Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications. J Math Biol 2009, 58:625–656.
68 Frieboes, HB, Lowengrub, JS, Wise, S, Zheng, X, Macklin, P, Elaine, LBD, Cristini, V. Computer simulation of glioma growth and morphology. Neuroimage 2007, 37:S59–S70.
69 Kordon, EC, Smith, GH. An entire functional mammary gland may comprise the progeny from a single cell. Development 1998, 125:1921–1930.
70 Butcher, D, Alliston, T, Weaver, V. A tense situation: forcing tumour progression. Nat Rev Cancer 2009, 9:108–122.
71 Kim, Y, Stolarska, M, Othmer, H. A hybrid model for tumor spheroid growth in vitro. I: theoretical development and early results. Math Models Methods Appl Sci 2007, 17(suppl):1773
72 Stolarska, M, Kim, Y, Othmer, H. Multi‐scale models of cell and tissue dynamics. Philos Trans R Soc A 2009, 367:3525–3553.
73 Bearer, EL, Lowengrub, JS, Frieboes, HB, Chuang, YL, Jin, F, Wise, SM, Ferrari, M, Agus, DB, Cristini, V. Multiparameter computational modeling of tumor invasion. Cancer Res 2009, 69:4493–4501.
74 Frieboes, HB, Jin, F, Chuang, YL, Wise, SM, Lowengrub, JS, Cristini, V. Three‐dimensional multispecies nonlinear tumor growth‐II: tumor invasion and angiogenesis. J Theor Biol 2010, 264:1254–1278.
75 Jin, F, Chuang, Y‐L. %22Hybrid continuum‐discrete tumor models.%22 In: Cristini, V, Lowengrub, JS. Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. New York:
Cambridge University Press; 2010, 123–152.
76 Bauer, AL, Jackson, TL, Jiang, Y. A cell‐based model exhibiting branching and anastomosis during tumor‐induced angiogenesis. Biophys J 2007, 92: 3105–3121.
77 Bauer, AL, Jackson, TL, Jiang, Y. Topography of extracellular matrix mediates vascular morphogenesis and migration speeds in angiogenesis. PLoS Comput Biol 2009, 5:e1000445.
78 Bissell, M, Aggeler, J. Dynamic reciprocity: how do extracellular matrix and hormones direct gene expression? Prog Clin Biol Res 1987, 249:251–262.
79 Tanos, T, Brisken, C. What signals operate in the mammary niche? Breast Dis 2008, 29:69–82.
80 Hatzikirou, H, Basanta, D, Simon, M, Schaller, K, Deutsch, A. “Go or Grow”: the key to the emergence of invasion in tumour progression? Math Med Biol 2010.
81 Olive, KP, Jacobetz, MA, Davidson, CJ, Gopinathan, A, McIntyre, D, Honess, D, Madhu, B, Goldgraben, MA, Caldwell, ME, Allard, D, et al. Inhibition of hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 2009, 324:1457–1461.
82 Sinek, JP, Sanga, S, Zheng, XM, Frieboes, HB, Ferrari, M, Cristini, V. Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation. J Math Biol 2009, 58:485–510.