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. ,
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
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
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
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
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
Multi-scale modelling in immunology: a review, Brief Bioinform, 2015. ,
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N Engl J Med, vol.366, pp.883-92, 2012. ,
Hallmarks of Cancer: The Next Generation, Cell, vol.144, issue.5, pp.646-74, 2011. ,
DOI : 10.1016/j.cell.2011.02.013
Targeting cancer with kinase inhibitors, Journal of Clinical Investigation, vol.125, issue.5, pp.1780-1789, 2015. ,
DOI : 10.1172/JCI76094
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
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. ,
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
Implementation of quantitative and systems pharmacology in large pharma.. CPT Pharmacometrics Syst Pharmacol, p.142, 2014. ,
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
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
Development of Translational Pharmacokinetic???Pharmacodynamic Models, Clinical Pharmacology & Therapeutics, vol.35, issue.6, pp.909-921, 2008. ,
DOI : 10.1038/clpt.2008.52
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
Assessment of tumor growth inhibition metrics to predict overall survival, Clin Pharmacol Ther. 2014, vol.96, issue.2, pp.135-142 ,
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
A study on chemoinformatics and its applications on modern drug discovery, Procedia Engineering, pp.1264-1275, 2012. ,
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
Organs-on-chips, Nature, vol.423, p.266 ,
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
Cardiac Tissue Engineering: State of the Art, Circulation Research, vol.114, issue.2, pp.354-367, 2014. ,
DOI : 10.1161/CIRCRESAHA.114.300522
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
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
Pharmacokinetics of anti-VEGF agent aflibercept in cancer predicted by data driven, molecular-detailed model. CPT: Pharmacometrics & Systems Pharmacology, pp.641-650, 2015. ,
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
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
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
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
Pharmacometrics: The Science of Quantitative Pharmacology, 2007. ,
DOI : 10.1002/0470087978
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
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
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
A reaction-diffusion model of cancer invasion, Cancer Research, vol.56, issue.24, pp.5745-53, 1996. ,
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
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
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
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
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
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
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
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
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
Agent-based models in translational systems biology, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, vol.50, issue.12, 2009. ,
DOI : 10.1002/wsbm.45
Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance. Interface Focus, Aug, vol.63, issue.4, p.20130016, 2013. ,
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
Essential operating principles for tumor spheroid growth, BMC Systems Biology, vol.2, issue.1, p.110, 2008. ,
DOI : 10.1186/1752-0509-2-110
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
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
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
Multistage carcinogenesis and the incidence of colorectal cancer, Proceedings of the National Academy of Sciences, pp.99-15095, 2002. ,
DOI : 10.1073/pnas.222118199
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
Multi-scale modeling of form and function, Science, issue.5924, pp.324-208, 2009. ,
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
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
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, vol.5, p.4006, 2014. ,
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
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
Bringing systems biology to cancer, immunology and infectious disease, Genome Biol, vol.2014, issue.7, pp.15-407 ,
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
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