S. Rubinacci, A. Graudenzi, G. Caravagna, G. Mauri, J. Osborne et al., CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer, Cancer Inform, vol.114, pp.53-65, 2015.

Y. Kim, G. Powathil, H. Kang, D. Trucu, H. Kim et al., Strategies of Eradicating Glioma Cells: A Multi-Scale Mathematical Model with MiR-451-AMPK-mTOR Control, PLOS ONE, vol.261, issue.3, p.114370, 2015.
DOI : 10.1371/journal.pone.0114370.s001

M. Robertson-tessi, R. J. Gillies, R. A. Gatenby, and A. R. Anderson, Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes, Cancer Research, vol.75, issue.8, pp.1567-79, 2015.
DOI : 10.1158/0008-5472.CAN-14-1428

S. D. Finley, L. H. Chu, and A. S. Popel, Computational systems biology approaches to anti-angiogenic cancer therapeutics, Drug Discovery Today, vol.20, issue.2, pp.187-97, 2015.
DOI : 10.1016/j.drudis.2014.09.026

A. A. Qutub, F. Mac-gabhann, E. D. Karagiannis, P. Vempati, and A. S. Popel, Multiscale models of angiogenesis, IEEE Engineering in Medicine and Biology Magazine, vol.28, issue.2, pp.14-31, 2009.
DOI : 10.1109/MEMB.2009.931791

S. Sharan and S. Woo, Systems pharmacology approaches for optimization of antiangiogenic therapies: challenges and opportunities, Frontiers in Pharmacology, vol.3, p.33, 2015.
DOI : 10.1038/psp.2013.65

A. Cappuccio, P. Tieri, and F. Castiglione, Multi-scale modelling in immunology: a review, Brief Bioinform, 2015.

B. Eklund, G. Spencer-dene, L. Clark, G. Pickering, M. Stamp et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N Engl J Med, vol.366, pp.883-92, 2012.

D. Hanahan and R. A. Weinberg, Hallmarks of Cancer: The Next Generation, Cell, vol.144, issue.5, pp.646-74, 2011.
DOI : 10.1016/j.cell.2011.02.013

S. Gross, R. Rahal, N. Stransky, C. Lengauer, and K. P. Hoeflich, Targeting cancer with kinase inhibitors, Journal of Clinical Investigation, vol.125, issue.5, pp.1780-1789, 2015.
DOI : 10.1172/JCI76094

F. Cavallo, C. De-giovanni, P. Nanni, G. Forni, and P. L. Lollini, 2011: the immune hallmarks of cancer, Cancer Immunology, Immunotherapy, vol.18, issue.Suppl 10, pp.319-345, 2011.
DOI : 10.1007/s00262-010-0968-0

P. K. Sorger, S. R. Allerheiligen, D. R. Abernethy, R. B. Altman, K. L. Brouwer et al., Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. An NIH white paper by the QSP workshop group, Bethesda: NIHDocuments, 2011.

D. C. Kirouac, J. Y. Du, J. Lahdenranta, R. Overland, D. Yarar et al., Computational Modeling of ERBB2-Amplified Breast Cancer Identifies Combined ErbB2/3 Blockade as Superior to the Combination of MEK and AKT Inhibitors, Science Signaling, vol.6, issue.288, p.68, 2013.
DOI : 10.1126/scisignal.2004008

S. A. Visser, D. P. De-alwis, T. Kerbusch, J. A. Stone, and S. R. Allerheiligen, Implementation of quantitative and systems pharmacology in large pharma.. CPT Pharmacometrics Syst Pharmacol, p.142, 2014.

G. R. Jang, R. Z. Harris, and D. T. Lau, Pharmacokinetics and its role in small molecule drug discovery research, Medicinal Research Reviews, vol.13, issue.5, pp.382-96, 2001.
DOI : 10.1002/med.1015

H. M. Jones, Y. Chen, C. Gibson, T. Heimbach, N. Parrott et al., Physiologically based pharmacokinetic modeling in drug discovery and development: A pharmaceutical industry perspective, Clinical Pharmacology & Therapeutics, vol.102, issue.suppl. 1, pp.247-62, 2015.
DOI : 10.1002/cpt.37

D. E. Mager and W. J. Jusko, Development of Translational Pharmacokinetic???Pharmacodynamic Models, Clinical Pharmacology & Therapeutics, vol.35, issue.6, pp.909-921, 2008.
DOI : 10.1038/clpt.2008.52

J. S. Barrett, M. J. Fossler, K. D. Cadieu, and M. R. Gastonguay, Pharmacometrics: A Multidisciplinary Field to Facilitate Critical Thinking in Drug Development and Translational Research Settings, The Journal of Clinical Pharmacology, vol.81, issue.iii, pp.632-681, 2008.
DOI : 10.1177/0091270008315318

R. Bruno, Assessment of tumor growth inhibition metrics to predict overall survival, Clin Pharmacol Ther. 2014, vol.96, issue.2, pp.135-142

K. Venkatakrishnan, L. Friberg, D. Ouellet, J. Mettetal, A. Stein et al., Optimizing Oncology Therapeutics Through Quantitative Translational and Clinical Pharmacology: Challenges and Opportunities, Clinical Pharmacology & Therapeutics, vol.66, issue.1, pp.37-54
DOI : 10.1002/cpt.7

B. F. Begam and J. S. Kumar, A study on chemoinformatics and its applications on modern drug discovery, Procedia Engineering, pp.1264-1275, 2012.

A. M. Clark, A. J. Williams, and S. Ekins, Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data, Journal of Cheminformatics, vol.7, issue.1, pp.1-20, 2015.
DOI : 10.1002/minf.201200034

S. Reardon, Organs-on-chips, Nature, vol.423, p.266

M. Karthikeyan, R. Vyas, S. Tambe, D. Radhamohan, and B. Kulkarni, Role of Chemical Reactivity and Transition State Modeling for Virtual Screening, Combinatorial Chemistry & High Throughput Screening, vol.18, issue.7, pp.18-638, 2015.
DOI : 10.2174/1386207318666150703113135

M. N. Hirt, A. Hansen, and T. Eschenhagen, Cardiac Tissue Engineering: State of the Art, Circulation Research, vol.114, issue.2, pp.354-367, 2014.
DOI : 10.1161/CIRCRESAHA.114.300522

J. R. Pritchard, Defining principles of combination drug mechanisms of action, Proceedings of the National Academy of Sciences, vol.110, issue.2, pp.170-179, 2013.
DOI : 10.1073/pnas.1210419110

K. Curtius, A Multiscale Model Evaluates Screening for Neoplasia in Barrett???s Esophagus, PLOS Computational Biology, vol.224, issue.5, p.1004272, 2015.
DOI : 10.1371/journal.pcbi.1004272.s008

S. D. Finley, Pharmacokinetics of anti-VEGF agent aflibercept in cancer predicted by data driven, molecular-detailed model. CPT: Pharmacometrics & Systems Pharmacology, pp.641-650, 2015.

S. D. Finley and A. S. Popel, Effect of Tumor Microenvironment on Tumor VEGF During Anti-VEGF Treatment: Systems Biology Predictions, JNCI Journal of the National Cancer Institute, vol.105, issue.11, pp.105-802, 2013.
DOI : 10.1093/jnci/djt093

L. W. Clegg and F. M. Gabhann, Site-Specific Phosphorylation of VEGFR2 Is Mediated by Receptor Trafficking: Insights from a Computational Model, PLOS Computational Biology, vol.28, issue.6, pp.11-1004158, 2015.
DOI : 10.1371/journal.pcbi.1004158.s015

S. D. Finley, L. H. Chu, and A. S. Popel, Computational systems biology approaches to anti-angiogenic cancer therapeutics, Drug Discovery Today, vol.20, issue.2, pp.187-97, 2015.
DOI : 10.1016/j.drudis.2014.09.026

B. Schoeberl, Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor-PI3K Axis, Science Signaling, vol.2, issue.77, p.31, 2009.
DOI : 10.1126/scisignal.2000352

E. I. Ette and P. J. Williams, Pharmacometrics: The Science of Quantitative Pharmacology, 2007.
DOI : 10.1002/0470087978

K. Venkatakrishnan, L. Friberg, D. Ouellet, J. Mettetal, A. Stein et al., Optimizing Oncology Therapeutics Through Quantitative Translational and Clinical Pharmacology: Challenges and Opportunities, Clinical Pharmacology & Therapeutics, vol.66, issue.1, pp.37-54
DOI : 10.1002/cpt.7

T. Yankeelov, R. Abramson, and C. Quarles, Quantitative multimodality imaging in cancer research and therapy, Nature Reviews Clinical Oncology, vol.46, issue.11, pp.670-80
DOI : 10.2967/jnumed.111.092650

J. Fadia-bekkal-brikci, B. Clairambault, B. Ribba, and . Perthame, An age-and-cyclin-structured cell population model for healthy and tumoral tissues, Journal of Mathematical Biology, vol.16, issue.6, pp.91-110, 2008.
DOI : 10.1007/s00285-007-0147-x

R. Gatenby and E. Gawlinski, A reaction-diffusion model of cancer invasion, Cancer Research, vol.56, issue.24, pp.5745-53, 1996.

D. Ambrosi and L. Preziosi, ON THE CLOSURE OF MASS BALANCE MODELS FOR TUMOR GROWTH, Mathematical Models and Methods in Applied Sciences, vol.12, issue.05, pp.737-753, 2002.
DOI : 10.1142/S0218202502001878

T. Colin, F. Cornelis, J. Jouganous, J. Palussière, and O. Saut, Patient-specific simulation of tumor growth, response to the treatment, and relapse of a lung metastasis: a clinical case, Journal of Computational Surgery, vol.99, issue.2, pp.1-10, 2015.
DOI : 10.1186/s40244-014-0014-1

URL : https://hal.archives-ouvertes.fr/hal-01102586

K. Swanson and . Rostomily, A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle, British Journal of Cancer, vol.170, issue.1, pp.113-119, 2008.
DOI : 10.1002/(SICI)1096-9098(199912)72:4<199::AID-JSO4>3.0.CO;2-O

E. Konukoglu, O. Clatz, P. Bondiau, H. Delingette, and N. Ayache, Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins, Medical Image Analysis, vol.14, issue.2, pp.111-125, 2010.
DOI : 10.1016/j.media.2009.11.005

URL : https://hal.archives-ouvertes.fr/inria-00616107

J. Weis, M. Miga, L. Arlinghaus, X. Li, A. Chakravarthy et al., A mechanically coupled reaction???diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy, Physics in Medicine and Biology, vol.58, issue.17, pp.5851-66, 2013.
DOI : 10.1088/0031-9155/58/17/5851

S. Benzekry, A. Gandolfi, and P. Hahnfeldt, Global Dormancy of Metastases Due to Systemic Inhibition of Angiogenesis, PLoS ONE, vol.63, issue.1, pp.84249-84260, 2014.
DOI : 10.1371/journal.pone.0084249.t002

URL : https://hal.archives-ouvertes.fr/hal-00868592

A. Lorz, T. Lorenzi, M. E. Hochberg, J. Clairambault, and B. Perthame, Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies, ESAIM: Mathematical Modelling and Numerical Analysis, vol.47, issue.2, pp.377-399, 2013.
DOI : 10.1051/m2an/2012031

URL : https://hal.archives-ouvertes.fr/hal-00714274

T. Colin, A. Iollo, D. Lombardi, and O. Saut, SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS, Mathematical Models and Methods in Applied Sciences, vol.22, issue.06, pp.1250003-1250004, 2012.
DOI : 10.1142/S0218202512500030

E. Konukoglu, O. Clatz, H. Delingette, and N. Ayache, Personalization of Reaction-Diffusion Tumor Growth Models in MR Images, Multi-scale Cancer Modeling, 2010.
DOI : 10.1201/b10407-18

URL : https://hal.archives-ouvertes.fr/inria-00616111

G. An, Agent-based models in translational systems biology, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, vol.50, issue.12, 2009.
DOI : 10.1002/wsbm.45

J. Gallaher and A. Anderson, Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance. Interface Focus, Aug, vol.63, issue.4, p.20130016, 2013.

P. Macklin, M. E. Edgerton, A. M. Thompson, and V. Cristini, Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression, Journal of Theoretical Biology, vol.301, pp.122-162, 2012.
DOI : 10.1016/j.jtbi.2012.02.002

J. A. Engelberg, G. E. Ropella, and C. A. Hunt, Essential operating principles for tumor spheroid growth, BMC Systems Biology, vol.2, issue.1, p.110, 2008.
DOI : 10.1186/1752-0509-2-110

G. An and S. Kulkarni, An agent-based modeling framework linking inflammation and cancer using evolutionary principles: Description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data, Mathematical Biosciences, vol.260, pp.201516-201540
DOI : 10.1016/j.mbs.2014.07.009

R. Meza, Age-specific incidence of cancer: Phases, transitions, and biological implications, Proceedings of the National Academy of Sciences, p.16284, 2008.
DOI : 10.1073/pnas.0801151105

S. Moolgavkar and A. Knudson, Mutation and Cancer: A Model for Human Carcinogenesis2, JNCI: Journal of the National Cancer Institute, vol.66, issue.6, pp.1037-1052, 1981.
DOI : 10.1093/jnci/66.6.1037

E. Luebeck and S. Moolgavkar, Multistage carcinogenesis and the incidence of colorectal cancer, Proceedings of the National Academy of Sciences, pp.99-15095, 2002.
DOI : 10.1073/pnas.222118199

F. Xu, A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform, Biotechnology Journal, vol.31, issue.2, pp.204-212, 2011.
DOI : 10.1002/biot.201000340

A. J. Engler, P. O. Humbert, B. Wehrle-haller, and V. M. Weaver, Multi-scale modeling of form and function, Science, issue.5924, pp.324-208, 2009.

A. Chakrabarti, S. Verbridge, A. D. Stroock, C. Fischbach, and J. D. Varner, Multiscale Models of Breast Cancer Progression, Annals of Biomedical Engineering, vol.109, issue.Suppl, pp.2488-2500, 2012.
DOI : 10.1007/s10439-012-0655-8

T. Yankeelov, N. Atuegwu, D. Hormuth, J. Weis, S. Barnes et al., Clinically Relevant Modeling of Tumor Growth and Treatment Response, Science Translational Medicine, vol.5, issue.187, pp.187-196, 2013.
DOI : 10.1126/scitranslmed.3005686

H. Aerts, E. Velazquez, R. Leijenaar, C. Parmar, P. Grossmann et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, vol.5, p.4006, 2014.

J. Weis, M. Miga, L. Arlinghaus, X. Li, V. Abramson et al., Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model, Cancer Research, vol.75, issue.22, 2014.
DOI : 10.1158/0008-5472.CAN-14-2945

A. Divoli, Conflicting Biomedical Assumptions for Mathematical Modeling: The Case of Cancer Metastasis, PLoS Computational Biology, vol.21, issue.10, pp.7-1002132, 2011.
DOI : 10.1371/journal.pcbi.1002132.s009

A. Kevin, C. Janes, and . Wang, Bringing systems biology to cancer, immunology and infectious disease, Genome Biol, vol.2014, issue.7, pp.15-407

J. P. Marquez, E. L. Elson, and G. M. Genin, Whole cell mechanics of contractile fibroblasts: relations between effective cellular and extracellular matrix moduli, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.97, issue.12, pp.368-635, 1912.
DOI : 10.1103/PhysRevLett.97.128103

G. An, Closing the Scientific Loop: Bridging Correlation and Causality in the Petaflop Age, Science Translational Medicine, vol.2, issue.41, pp.41-75, 2010.
DOI : 10.1126/scitranslmed.3000390