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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 1A. Armiento.

    Inverse problems and data assimilation methods applied to protein polymerisation, Université Paris 7 - Diderot, January 2017.

    https://hal.inria.fr/tel-01447286

Articles in International Peer-Reviewed Journals

  • 2A. Armiento, P. Moireau, D. Martin, N. Lepejova, M. Doumic, H. Rezaei.

    The mechanism of monomer transfer between two structurally distinct PrP oligomers, in: PLoS ONE, July 2017, vol. 12, no 7. [ DOI : 10.1371/journal.pone.0180538 ]

    https://hal.archives-ouvertes.fr/hal-01574346
  • 3H. T. Banks, M. Doumic-Jauffret, C. Kruse.

    A numerical scheme for the early steps of nucleation-aggregation Models, in: Journal of Mathematical Biology, January 2017, vol. 74, no 1-2, pp. 259-287. [ DOI : 10.1007/s00285-016-1026-0 ]

    https://hal.inria.fr/hal-00954437
  • 4A. A. Bhaya, P.-A. A. Bliman, G. Niedu, F. A. Pazos.

    A cooperative conjugate gradient method for linear systems permitting efficient multi-thread implementation, in: Computational and Applied Mathematics, 2017, pp. 1–28. [ DOI : 10.1007/s40314-016-0416-7 ]

    https://hal.inria.fr/hal-01558765
  • 5P.-A. Bliman, M. S. Aronna, F. C. Coelho, M. A. H. B. da Silva.

    Ensuring successful introduction of Wolbachia in natural populations of Aedes aegypti by means of feedback control, in: Journal of Mathematical Biology, August 2017. [ DOI : 10.1007/s00285-017-1174-x ]

    https://hal.inria.fr/hal-01579477
  • 6P.-A. Bliman, N. Vauchelet.

    Establishing Traveling Wave in Bistable Reaction-Diffusion System by Feedback, in: IEEE Control Systems Letters, 2017, vol. 1, no 1, pp. 62 - 67. [ DOI : 10.1109/LCSYS.2017.2703303 ]

    https://hal.inria.fr/hal-01558631
  • 7P. O. Bucur, M. Bekheit, C. Audebert, A. Othman, S. Hammad, M. Sebagh, M. A. Allard, B. Decante, A. Friebel, D. Drasdo, E. Miquelestorena-Standley, J. G. Hengstler, I. Vignon-Clementel, E. Vibert.

    Modulating Portal Hemodynamics With Vascular Ring Allows Efficient Regeneration After Partial Hepatectomy in a Porcine Model., in: Annals of Surgery, February 2017. [ DOI : 10.1097/SLA.0000000000002146 ]

    https://hal.archives-ouvertes.fr/hal-01494844
  • 8M. Burger, A. Lorz, M.-T. Wolfram.

    Balanced Growth Path Solutions of a Boltzmann Mean Field Game Model for Knowledge Growth, in: Kinetic and Related Models , March 2017, https://arxiv.org/abs/1602.01423. [ DOI : 10.3934/krm.2017005 ]

    https://hal.archives-ouvertes.fr/hal-01267078
  • 9J. Clairambault, B. Perthame, A. Quillas Maran.

    Analysis of a system describing proliferative-quiescent cell dynamics, in: Chinese Annals of Mathematics - Series B, 2018, pp. 1-13.

    http://hal.upmc.fr/hal-01674142
  • 10M. Doumic, B. Perthame, E. Ribes, D. Salort, N. Toubiana.

    Toward an integrated workforce planning framework using structured equations, in: European Journal of Operational Research, April 2017, vol. 262, https://arxiv.org/abs/1607.02349. [ DOI : 10.1016/j.ejor.2017.03.076 ]

    https://hal.inria.fr/hal-01343368
  • 11J. Elias.

    Positive effect of Mdm2 on p53 expression explains excitability of p53 in response to DNA damage, in: Journal of Theoretical Biology, April 2017, vol. 418, pp. 94-104, 1 year long embargo for free article distribution. [ DOI : 10.1016/j.jtbi.2017.01.038 ]

    https://hal.inria.fr/hal-01443268
  • 12C. Emako, J. Liao, N. Vauchelet.

    Synchronising and non-synchronising dynamics for a two-species aggregation model, in: Discrete and Continuous Dynamical Systems - Series B (DCDS-B), August 2017, vol. 22, no 6, pp. 2121 - 2146, https://arxiv.org/abs/1505.07659. [ DOI : 10.3934/dcdsb.2017088 ]

    https://hal.archives-ouvertes.fr/hal-01157578
  • 13S. Eugene, T. Bourgeron, Z. Xu.

    Effects of initial telomere length distribution on senescence onset and heterogeneity, in: Journal of Theoretical Biology, January 2017, vol. 413, 8 p, https://arxiv.org/abs/1606.06842.

    https://hal.inria.fr/hal-01378596
  • 14A. Goldman, M. Kohandel, J. Clairambault.

    Integrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer, Part 1: Biological Facts and Studies in Drug Resistance, in: Current Stem Cell Reports, August 2017. [ DOI : 10.1007/s40778-017-0097-1 ]

    https://hal.inria.fr/hal-01558477
  • 15A. Goldman, M. Kohandel, J. Clairambault.

    Integrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer, Part 2: From Theoretical Biology to Mathematical Models, in: Current Stem Cell Reports, August 2017. [ DOI : 10.1007/s40778-017-0098-0 ]

    https://hal.inria.fr/hal-01558479
  • 16C. Jourdana, P. Pietra, N. Vauchelet.

    Hybrid coupling of a one-dimensional Energy-Transport Schrödinger system, in: Monatshefte für Mathematik, December 2017, vol. 184, no 4, pp. 563–596. [ DOI : 10.1007/s00605-016-1008-8 ]

    https://hal.archives-ouvertes.fr/hal-01052415
  • 17T. Lorenzi, A. Lorz, B. Perthame.

    On interfaces between cell populations with different mobilities, in: Kinetic and Related Models , March 2017, vol. 10, no 1, pp. 299-311.

    https://hal.inria.fr/hal-01257180
  • 18A. Lorz, B. Perthame, C. Taing.

    Dirac concentrations in a chemostat model of adaptive evolution, in: Chinese Annals of Mathematics - Series B, March 2017.

    http://hal.upmc.fr/hal-01255449
  • 19A. Mellet, B. Perthame, F. Quiros.

    A Hele-Shaw Problem for Tumor Growth, in: Journal of Functional Analysis, 2017, vol. 273, pp. 3061-3093, https://arxiv.org/abs/1512.06995.

    http://hal.upmc.fr/hal-01241309
  • 20H. Moundoyi, A. Moussa, B. Perthame, B. Sarels.

    Analytical examples of diffusive waves generated by a traveling wave, in: Applicable Analysis, April 2017. [ DOI : 10.1080/00036811.2017.1314463 ]

    http://hal.upmc.fr/hal-01404972
  • 21A. Olivier.

    How does variability in cells aging and growth rates influence the malthus parameter?, in: Kinetic and Related Models , June 2017, vol. 10, no 2, pp. 481-512, https://arxiv.org/abs/1602.06970. [ DOI : 10.3934/krm.2017019 ]

    https://hal.archives-ouvertes.fr/hal-01274529
  • 22N. Outada, N. Vauchelet, T. Akrid, M. Khaladi.

    From Kinetic Theory of Multicellular Systems to Hyperbolic Tissue Equations: Asymptotic Limits and Computing, in: Mathematical Models and Methods in Applied Sciences, 2017, https://arxiv.org/abs/1610.03290.

    https://hal.archives-ouvertes.fr/hal-01378301
  • 23B. Perthame, D. Salort, G. Wainrib.

    Distributed synaptic weights in a LIF neural network and learning rules, in: Physica D: Nonlinear Phenomena, 2017, vol. 353-354, pp. 20-30, https://arxiv.org/abs/1706.05796. [ DOI : 10.1016/j.physd.2017.05.005 ]

    http://hal.upmc.fr/hal-01541093
  • 24C. Pouchol, J. Clairambault, A. Lorz, E. Trélat.

    Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy, in: Journal de Mathématiques Pures et Appliquées, October 2017, https://arxiv.org/abs/1612.04698. [ DOI : 10.1016/j.matpur.2017.10.007 ]

    https://hal.archives-ouvertes.fr/hal-01673589
  • 25Y. Yin, O. Sedlaczek, B. Müller, A. Warth, M. González-Vallinas, B. Lahrmann, N. Grabe, H.-U. Kauczor, K. Breuhahn, I. Vignon-Clementel, D. Drasdo.

    Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer, in: IEEE Transactions on Medical Imaging, 2017. [ DOI : 10.1109/TMI.2017.2698525 ]

    https://hal.inria.fr/hal-01421398

International Conferences with Proceedings

  • 26W. Djema, C. Bonnet, J. Clairambault, F. Mazenc, P. Hirsch, F. Delhommeau.

    Analysis of a Model of Dormancy in Cancer as a State of Coexistence Between Tumor and Healthy Stem Cells, in: ACC 2017 - American Control Conference, Seattle, United States, IEEE, May 2017, pp. 5135-5140. [ DOI : 10.23919/ACC.2017.7963751 ]

    https://hal.inria.fr/hal-01677927
  • 27W. Djema, H. Özbay, C. Bonnet, E. Fridman, F. Mazenc, J. Clairambault.

    Analysis of Blood Cell Production under Growth Factors Switching, in: IFAC 2017 - 20th World Congress of the International Federation of Automatic Control, Toulouse, France, Elsevier, July 2017, vol. 50, no 1, pp. 13312-13317. [ DOI : 10.1016/j.ifacol.2017.08.1331 ]

    https://hal.inria.fr/hal-01677914

Scientific Books (or Scientific Book chapters)

  • 28F. Bertaux, D. Drasdo, G. Batt.

    System modeling of receptor-induced apoptosis, in: TRAIL, Fas Ligand, TNF and TLR3 in Cancer, O. Micheau (editor), Resistance to Targeted Anti-Cancer Therapeutics, 2017, no 12, https://arxiv.org/abs/1712.06822.

    https://hal.inria.fr/hal-01667015
  • 29P.-A. Bliman, B. D 'Avila Barros.

    Interval Observers for SIR Epidemic Models Subject to Uncertain Seasonality, in: Lecture Notes in Control and Information Sciences, 2017, vol. 471, 9 p. [ DOI : 10.1007/978-3-319-54211-9_3 ]

    https://hal.inria.fr/hal-01567474

Other Publications

References in notes
  • 51L. Almeida, C. Emako, N. Vauchelet.

    Existence and diffusive limit of a two-species kinetic model of chemotaxis, in: Kinetic and Related Models , June 2015. [ DOI : 10.3934/krm.2015.8.359 ]

    https://hal.archives-ouvertes.fr/hal-00980594
  • 52A. Armiento, M. Doumic, P. Moireau, H. Rezaei.

    Estimation from Moments Measurements for Amyloid Depolymerisation, in: Journal of Theoretical Biology, March 2016. [ DOI : 10.1016/j.jtbi.2016.02.037 ]

    https://hal.archives-ouvertes.fr/hal-01248255
  • 53J. L. Avila Alonso, C. Bonnet, J. Clairambault, H. Ozbay, S.-I. Niculescu, F. Merhi, A. Ballesta, R. Tang, J.-P. Marie.

    Analysis of a New Model of Cell Population Dynamics in Acute Myeloid Leukemia, in: Delay Systems : From Theory to Numerics and Applications, T. Vyhlídal, J.-F. Lafay, R. Sipahi (editors), Advances in Delays and Dynamics, Springer, January 2014, vol. 1, pp. 315-328. [ DOI : 10.1007/978-3-319-01695-5_23 ]

    https://hal.inria.fr/hal-00932779
  • 54J. L. Avila Alonso, C. Bonnet, E. Fridman, F. Mazenc, J. Clairambault.

    Stability analysis of PDE's modelling cell dynamics in Acute Myeloid Leukemia, in: 53rd IEEE Conference on Decision and Control, Los Angeles, United States, December 2014.

    https://hal.inria.fr/hal-01110304
  • 55J. L. Avila Alonso, C. Bonnet, H. Ozbay, J. Clairambault, S.-I. Niculescu.

    A coupled model for healthy and cancer cells dynamics in Acute Myeloid Leukemia, in: The 19th World Congress of the International Federation of Automatic Control, Cape Town, Souh Africa, August 2014.

    https://hal.inria.fr/hal-00940245
  • 56J. L. Avila Alonso, C. Bonnet, H. Ozbay, J. Clairambault, S.-I. Niculescu.

    A discrete-maturity Interconnected Model of Healthy and Cancer Cell Dynamics in Acute Myeloid Leukemia, in: Mathematical Theory of Networks and Systems, Groningen, Netherlands, July 2014.

    https://hal.inria.fr/hal-00940305
  • 57H. T. Banks, M. Doumic, C. Kruse, S. Prigent, H. Rezaei.

    Information Content in Data Sets for a Nucleated-Polymerization Model, in: Journal of Biological Dynamics, June 2015, vol. 9, no 1, 26 p. [ DOI : 10.1080/17513758.2015.1050465 ]

    https://hal.inria.fr/hal-01123847
  • 58H. T. Banks, M. Doumic-Jauffret, C. Kruse.

    A numerical scheme for the early steps of nucleation-aggregation Models, in: Journal of Mathematical Biology, January 2017, vol. 74, no 1-2, pp. 259-287. [ DOI : 10.1007/s00285-016-1026-0 ]

    https://hal.inria.fr/hal-00954437
  • 59F. Bekkal Brikci, J. Clairambault, B. Perthame.

    Analysis of a molecular structured population model with possible polynomial growth for the cell division cycle, in: Math. Comput. Modelling, 2008, vol. 47, no 7-8, pp. 699–713.
  • 60F. Bertaux, S. Hoehme, W. Weens, B. Grasl-Kraupp, J. G. Hengstler, D. Drasdo.

    Model prediction and validation of an order mechanism controlling the spatio-temporal phenotype of early hepatocellular carcinoma, October 2016, working paper or preprint.

    https://hal.inria.fr/hal-01426629
  • 61F. Bertaux, S. Stoma, D. Drasdo, G. Batt.

    Modeling Dynamics of Cell-to-Cell Variability in TRAIL-Induced Apoptosis Explains Fractional Killing and Predicts Reversible Resistance, in: PLoS Computational Biology, 2014, vol. 10, no 10, 14 p. [ DOI : 10.1371/journal.pcbi.1003893.s016 ]

    https://hal.inria.fr/hal-00942885
  • 62J. Bertoin, A. R. Watson.

    Probabilistic aspects of critical growth-fragmentation equations, in: Advances in Applied Probability, 9 2015.
  • 63P.-A. Bliman, M. S. Aronna, F. C. Coelho, M. Da Silva.

    Global stabilizing feedback law for a problem of biological control of mosquito-borne diseases, in: 54th IEEE Conference on Decision and Control, Osaka, Japan, Proc. of the 54th IEEE Conference on Decision and Control, December 2015.

    https://hal.inria.fr/hal-01261162
  • 64C. Bonnet, J. L. Avila Alonso, H. Ozbay, J. Clairambault, S.-I. Niculescu, P. Hirsch.

    A Discrete-Maturity Interconnected Model of Healthy and Cancer Cell Dynamics in Acute Myeloid Leukemia, in: The 10th AIMS Conference on Dynamical Systems,Differential Equations and Applications, Madrid, Spain, July 2014.

    https://hal.inria.fr/hal-01110309
  • 65T. Bourgeron, M. Doumic, M. Escobedo.

    Estimating the division rate of the growth-fragmentation equation with a self-similar kernel, in: Inverse Problems, Jan 2014, vol. 30, no 2, 025007 p.

    http://dx.doi.org/10.1088/0266-5611/30/2/025007
  • 66T. Bourgeron, Z. Xu, M. Doumic, M. T. Teixeira.

    The asymmetry of telomere replication contributes to replicative senescence heterogeneity, in: Scientific Reports, October 2015, vol. 5, 15326 p. [ DOI : 10.1038/srep15326 ]

    http://hal.upmc.fr/hal-01272075
  • 67M. J. Caceres, B. Perthame.

    Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity, in: Journal of Theoretical Biology, 2014, vol. 350, pp. 81-89. [ DOI : 10.1016/j.jtbi.2014.02.005 ]

    http://hal.upmc.fr/hal-00874746
  • 68V. Calvez, M. Doumic, P. Gabriel.

    Self-similarity in a general aggregation–fragmentation problem. Application to fitness analysis, in: Journal de Mathématiques Pures et Appliquées, 2012, vol. 98, no 1, pp. 1 - 27. [ DOI : 10.1016/j.matpur.2012.01.004 ]

    http://www.sciencedirect.com/science/article/pii/S002178241200013X
  • 69V. Calvez, N. Lenuzza, M. Doumic, J.-P. Deslys, F. Mouthon, B. Perthame.

    Prion dynamic with size dependency - strain phenomena, in: J. of Biol. Dyn., 2010, vol. 4, no 1, pp. 28–42.
  • 70J. A. Carrillo, F. James, F. Lagoutière, N. Vauchelet.

    The Filippov characteristic flow for the aggregation equation with mildly singular potentials, in: Journal of Differential Equations, 2016, vol. 260, no 1, pp. 304-338, 33 pages.

    https://hal.archives-ouvertes.fr/hal-01061991
  • 71G. Cellière.

    Multi-scale modeling of hepatic drug toxicity and its consequences on ammonia detoxification, Université Paris 6 - Pierre et Marie Curie, July 2017.
  • 72J. Chevallier, M. J. Caceres, M. Doumic, P. Reynaud-Bouret.

    Microscopic approach of a time elapsed neural model, in: Mathematical Models and Methods in Applied Sciences, December 2015, 2669 p. [ DOI : 10.1142/S021820251550058X ]

    http://hal.upmc.fr/hal-01159215
  • 73R. H. Chisholm, T. Lorenzi, J. Clairambault.

    Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation, in: BBA - General Subjects, June 2016, vol. 1860, pp. 2627 - 2645. [ DOI : 10.1016/j.bbagen.2016.06.009 ]

    https://hal.inria.fr/hal-01321535
  • 74R. H. Chisholm, T. Lorenzi, A. Lorz, A. K. Larsen, L. N. de Almeida, A. Escargueil, J. Clairambault.

    Emergence of Drug Tolerance in Cancer Cell Populations: An Evolutionary Outcome of Selection, Nongenetic Instability, and Stress-Induced Adaptation, in: Cancer Research, March 2015, vol. 75, no 6, pp. 930-939. [ DOI : 10.1158/0008-5472.CAN-14-2103 ]

    https://hal.inria.fr/hal-01237893
  • 75J. Clairambault, O. Fercoq.

    Physiologically structured cell population dynamic models with applications to combined drug delivery optimisation in oncology, in: Mathematical Modelling of Natural Phenomena, 2016, 22 p, V2 d'un dépôt précédemment effectué sous la référence clairambault:hal-01321536v1. [ DOI : 10.1051/mmnp/201611604 ]

    https://hal.inria.fr/hal-01413791
  • 76W. Djema, F. Mazenc, C. Bonnet, J. Clairambault, P. Hirsch, F. Delhommeau.

    Stability of a Delay System Coupled to a Differential-Difference System Describing the Coexistence of Ordinary and Mutated Hematopoietic Stem Cells, in: Conference on Decision and Control , Las Vegas, United States, December 2016.

    https://hal.inria.fr/hal-01389870
  • 77M. Doumic, M. Escobedo.

    Time Asymptotics for a Critical Case in Fragmentation and Growth-Fragmentation Equations, in: Kinetic and Related Models , June 2016, vol. 9, no 2, 47 p. [ DOI : 10.3934/krm.2016.9.251 ]

    https://hal.inria.fr/hal-01080361
  • 78M. Doumic, S. Eugene, P. Robert.

    Asymptotics of Stochastic Protein Assembly Models, in: SIAM Journal on Applied Mathematics, November 2016, vol. 76, no 6, 20 p. [ DOI : 10.1137/16M1066920 ]

    https://hal.inria.fr/hal-01301266
  • 79M. Doumic, P. Gabriel.

    Eigenelements of a General Aggregation-Fragmentation Model, in: Mathematical Models and Methods in Applied Sciences, 2009, vol. 20, no 05, 757 p.

    http://arxiv.org/abs/0907.5467
  • 80M. Doumic, M. Hoffmann, N. Krell, L. Robert.

    Statistical estimation of a growth-fragmentation model observed on a genealogical tree, October 2012, 46 pages, 4 figures.

    https://hal.archives-ouvertes.fr/hal-00763601
  • 81M. Doumic, B. Perthame, J. Zubelli.

    Numerical Solution of an Inverse Problem in Size-Structured Population Dynamics, in: Inverse Problems, 2009, vol. 25, no 4, 045008 p.
  • 82D. Drasdo, S. Hoehme, J. G. Hengstler.

    How predictive quantitative modeling of tissue organization can inform liver disease pathogenesis, in: Journal of Hepatology, October 2014, vol. 61, no 4, pp. 951–956. [ DOI : 10.1016/j.jhep.2014.06.013 ]

    https://hal.inria.fr/hal-01110644
  • 83J. Elias, J. Clairambault.

    Reaction-diffusion systems for spatio-temporal intracellular protein networks: a beginner's guide with two examples, in: Computational and structural biotechnology journal, June 2014, 11 p. [ DOI : 10.1016/j.csbj.2014.05.007 ]

    https://hal.inria.fr/hal-00957344
  • 84J. Elias, L. Dimitrio, J. Clairambault, R. Natalini.

    Dynamics of p53 in single cells: physiologically based ODE and reaction-diffusion PDE models, in: Physical Biology, July 2014, 22 p, Phys. Biol. 11 (2014) 045001. [ DOI : 10.1088/1478-3975/11/4/045001 ]

    https://hal.inria.fr/hal-00859412
  • 85J. Elias.

    Mathematical model of the role and temporal dynamics of protein p53 after drug-induced DNA damage, Université Pierre et Marie Curie - Paris VI, September 2015.

    https://tel.archives-ouvertes.fr/tel-01237604
  • 86C. Emako, C. Gayrard, A. Buguin, L. Neves De Almeida, N. Vauchelet.

    Traveling Pulses for a Two-Species Chemotaxis Model, in: PLoS Computational Biology, April 2016, vol. 12, no 4, e1004843 p. [ DOI : 10.1371/journal.pcbi.1004843 ]

    https://hal.archives-ouvertes.fr/hal-01302632
  • 87C. Emako, M. Tang.

    Well-balanced and asymptotic preserving schemes for kinetic models , March 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01265029
  • 88C. Emako Kazianou.

    Study of two-species chemotaxis models, Université Pierre et Marie Curie - Paris VI, March 2016.

    https://tel.archives-ouvertes.fr/tel-01365414
  • 89S. Eugene.

    Stochastic modelling in molecular biology: a probabilistic analysis of protein polymerisation and telomere shortening, UPMC LJLL, September 2016.

    https://hal.inria.fr/tel-01377561
  • 90S. Eugene, W.-F. Xue, P. Robert, M. Doumic-Jauffret.

    Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly, in: Journal of Chemical Physics, May 2016, vol. 144, no 17, 12 p. [ DOI : 10.1063/1.4947472 ]

    https://hal.inria.fr/hal-01205549
  • 91A. Friebel, J. Neitsch, T. Johann, S. Hammad, D. Drasdo, S. Hoehme.

    TiQuant: software for tissue analysis, quantification and surface reconstruction, in: Bioinformatics, June 2015, vol. 31, no 19, pp. 3234-3236. [ DOI : 10.1093/bioinformatics/btv346 ]

    https://hal.inria.fr/hal-01257137
  • 92A. Ghallab, G. Cellière, S. Henkel, D. Driesch, S. Hoehme, U. Hofmann, S. Zellmer, P. Godoy, A. Sachinidis, M. Blaszkewicz, R. Reif, R. Marchan, L. Kuepfer, D. Häussinger, D. Drasdo, G. Gebhardt, J. G. Hengstler.

    Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases, in: Journal of Hepatology, November 2015, vol. 64, no 4, pp. 860–871. [ DOI : 10.1016/j.jhep.2015.11.018 ]

    https://hal.archives-ouvertes.fr/hal-01257127
  • 93L. Gosse, N. Vauchelet.

    Hydrodynamic singular regimes in 1+1 kinetic models and spectral numerical methods, in: Journal of Mathematical Analysis and Applications, 2016. [ DOI : 10.1016/j.jmaa.2016.07.059 ]

    https://hal.archives-ouvertes.fr/hal-01354980
  • 94S. Hammad, S. Hoehme, A. Friebel, I. von Recklinghausen, A. Othman, B. Begher-Tibbe, R. Reif, P. Godoy, T. Johann, A. Vartak, K. Golka, P. O. Bucur, E. Vibert, R. Marchan, B. Christ, S. Dooley, C. Meyer, I. Ilkavets, U. Dahmen, O. Dirsch, J. Böttger, R. Gebhardt, D. Drasdo, J. G. Hengstler.

    Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis., in: Archives of Toxicology, May 2014, vol. 88, no 5, pp. 1161-1183. [ DOI : 10.1007/s00204-014-1243-5 ]

    https://hal.inria.fr/hal-01110657
  • 95V. H. Hoang.

    Adaptive estimation for inverse problems with applications to cell divisions, Université de Lille 1 – Sciences et Technologies, November 2016.

    https://tel.archives-ouvertes.fr/tel-01417780
  • 96M. Hoffmann, A. Olivier.

    Nonparametric estimation of the division rate of an age dependent branching process, in: Stochastic Processes and their Applications, December 2015. [ DOI : 10.1016/j.spa.2015.11.009 ]

    https://hal.archives-ouvertes.fr/hal-01254203
  • 97N. Jagiella.

    Parameterization of Lattice-Based Tumor Models from Data., Université Pierre et Marie Curie - Paris VI, September 2012.

    https://tel.archives-ouvertes.fr/tel-00779981
  • 98N. Jagiella, B. Müller, M. Müller, I. E. Vignon-Clementel, D. Drasdo.

    Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data, in: PLoS Computational Biology, 2016, vol. 12, no 2, e1004412 p. [ DOI : 10.1371/journal.pcbi.1004412 ]

    http://hal.upmc.fr/hal-01244593
  • 99F. James, N. Vauchelet.

    Numerical methods for one-dimensional aggregation equations, in: SIAM Journal on Numerical Analysis, 2015, vol. 53, no 2, pp. 895-916. [ DOI : 10.1137/140959997 ]

    https://hal.archives-ouvertes.fr/hal-00955971
  • 100F. James, N. Vauchelet.

    Equivalence between duality and gradient flow solutions for one-dimensional aggregation equations, in: Discrete and Continuous Dynamical Systems - Series A, 2016, vol. 36, no 3, pp. 1355-1382.

    https://hal.archives-ouvertes.fr/hal-00803709
  • 101M.-J. Kang, B. Perthame, D. Salort.

    Dynamics of time elapsed inhomogeneous neuron network model, in: Comptes Rendus Mathématique, September 2015, no 353, pp. 1111-1115. [ DOI : 10.1016/j.crma.2015.09.029 ]

    http://hal.upmc.fr/hal-01241300
  • 102I. C. Kim, B. Perthame, P. E. Souganidis.

    Free boundary problems for tumor growth: a viscosity solutions approach, in: Nonlinear Analysis: Theory, Methods and Applications, 2016, vol. 138, pp. 207-228. [ DOI : 10.1016/j.na.2016.01.019 ]

    http://hal.upmc.fr/hal-01155696
  • 103M. Kolwalczyk, B. Perthame, N. Vauchelet.

    Transversal instability for the thermodiffusive reaction-diffusion system, in: Chinese Annals of Mathematics - Series B, 2015, vol. 36, no 5, pp. 871-882, 13 pages.

    https://hal.archives-ouvertes.fr/hal-00939013
  • 104J. C. Lopez Alfonso, N. Jagiella, L. Núñez, M. Herrero, D. Drasdo.

    Estimating Dose Painting Effects in Radiotherapy: AMathematical Model, in: PLoS ONE, February 2014, vol. 9, no 2, 22 p. [ DOI : 10.1371/journal.pone.0089380 ]

    https://hal.inria.fr/hal-01109675
  • 105T. Lorenzi, R. H. Chisholm, J. Clairambault.

    Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations, in: Biology Direct, December 2016, vol. 11, no 1, 43 p. [ DOI : 10.1186/s13062-016-0143-4 ]

    https://hal.inria.fr/hal-01355314
  • 106T. Lorenzi, R. H. Chisholm, L. Desvillettes, B. D. Hughes.

    Dissecting the dynamics of epigenetic changes in phenotype-structured populations exposed to fluctuating environments, in: Journal of Theoretical Biology, September 2015, vol. 386, pp. 166-176. [ DOI : 10.1016/j.jtbi.2015.08.031 ]

    https://hal.inria.fr/hal-01237890
  • 107T. Lorenzi, R. H. Chisholm, A. Lorz.

    Effects of an advection term in nonlocal Lotka-Volterra equations, December 2015.

    https://hal.archives-ouvertes.fr/hal-01237529
  • 108A. Lorz, T. Lorenzi, J. Clairambault, A. Escargueil, B. Perthame.

    Modeling the effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors, in: Bulletin of Mathematical Biology, January 2015, vol. 77, no 1, pp. 1-22. [ DOI : 10.1007/s11538-014-0046-4 ]

    http://hal.upmc.fr/hal-00921266
  • 109A. Lorz, T. Lorenzi, M. E. Hochberg, J. Clairambault, B. Perthame.

    Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies, in: ESAIM: Mathematical Modelling and Numerical Analysis, March 2013, 23 p. [ DOI : 10.1051/m2an/2012031 ]

    https://hal.archives-ouvertes.fr/hal-00714274
  • 110A. Mellet, B. Perthame, F. Quiros.

    A Hele-Shaw Problem for Tumor Growth, December 2015, working paper or preprint.

    http://hal.upmc.fr/hal-01241309
  • 111A. Olivier.

    Statistical analysis of growth-fragmentation models, Université Paris Dauphine - Paris IX, November 2015.

    https://hal.archives-ouvertes.fr/tel-01235239
  • 112K. Pakdaman, B. Perthame, D. Salort.

    Adaptation and Fatigue Model for Neuron Networks and Large Time Asymptotics in a Nonlinear Fragmentation Equation, in: Journal of Mathematical Neuroscience, 2014, vol. 4, no 1, 14 p. [ DOI : 10.1186/2190-8567-4-14 ]

    https://hal.inria.fr/hal-01054561
  • 113B. Perthame.

    Transport equations in biology, Frontiers in Mathematics, Birkhäuser Verlag, Basel, 2007, x+198 p.
  • 114B. Perthame, F. Quirós, M. Tang, N. Vauchelet.

    Derivation of a Hele-Shaw type system from a cell model with active motion, July 2013.

    http://hal.upmc.fr/hal-00906168
  • 115B. Perthame, F. Quirós, J.-L. Vázquez.

    The Hele-Shaw asymptotics for mechanical models of tumor growth, in: Archive for Rational Mechanics and Analysis, 2014, vol. 212, pp. 93-127. [ DOI : 10.1007/s00205-013-0704-y ]

    http://hal.upmc.fr/hal-00831932
  • 116B. Perthame, D. Salort.

    On a voltage-conductance kinetic system for integrate and fire neural networks, in: Kinetic and Related Models , December 2013, vol. 6, no 4, pp. 841-864. [ DOI : 10.3934/krm.2013.6.841 ]

    http://hal.upmc.fr/hal-00871609
  • 117B. Perthame, D. Salort, G. Wainrib.

    Distributed synaptic weights in a LIF neural network and learning rules, in: Physica D: Nonlinear Phenomena, 2017, vol. 353-354, pp. 20-30. [ DOI : 10.1016/j.physd.2017.05.005 ]

    http://hal.upmc.fr/hal-01541093
  • 118B. Perthame, M. Tang, N. Vauchelet.

    Traveling wave solution of the Hele-Shaw model of tumor growth with nutrient, in: Mathematical Models and Methods in Applied Sciences, 2014, vol. 24, no 13, pp. 2601-2626, 25 pages.

    https://hal.archives-ouvertes.fr/hal-00931399
  • 119B. Perthame, M. Tang, N. Vauchelet.

    Derivation of the bacterial run-and-tumble kinetic equation from a model with biochemical pathway, in: Journal of Mathematical Biology, 2016.

    http://hal.upmc.fr/hal-01131101
  • 120B. Perthame, N. Vauchelet.

    Incompressible limit of mechanical model of tumor growth with viscosity, in: Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences (1934–1990), 2015, vol. 373, 20140283 p, 16 pages. [ DOI : 10.1098/rsta.2014.0283 ]

    https://hal.archives-ouvertes.fr/hal-01066494
  • 121B. Perthame, J. Zubelli.

    On the inverse problem for a size-structured population model, in: Inverse Problems, 2007, vol. 23, no 3, pp. 1037–1052.
  • 122S. Prigent, A. Ballesta, F. Charles, N. Lenuzza, P. Gabriel, L. M. Tine, H. Rezaei, M. Doumic.

    An efficient kinetic model for assemblies of amyloid fibrils and its application to polyglutamine aggregation., in: PLoS ONE, 2012, vol. 7, no 11, e43273 p. [ DOI : 10.1371/journal.pone.0043273 ]

    https://hal.archives-ouvertes.fr/hal-00778052
  • 123L. Robert, M. Hoffmann, N. Krell, S. Aymerich, J. Robert, M. Doumic.

    Division in Escherichia coli is triggered by a size-sensing rather than a timing mechanism, in: BMC Biology, 2014, vol. 12, no 1, 17 p. [ DOI : 10.1186/1741-7007-12-17 ]

    https://hal.inria.fr/hal-00981312
  • 124F. Schliess, S. Hoehme, S. Henkel, A. Ghallab, D. Driesch, J. Böttger, R. Guthke, M. Pfaff, J. Hengstler, R. Gebhardt, D. Häussinger, D. Drasdo, S. Zellmer.

    Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration, in: Hepatology, December 2014, vol. 60, no 6, pp. 2040–2051. [ DOI : 10.1002/hep.27136 ]

    https://hal.inria.fr/hal-01110646
  • 125M. Strugarek, N. Vauchelet.

    Reduction to a single closed equation for 2 by 2 reaction-diffusion systems of Lotka-Volterra type, in: SIAM Journal on Applied Mathematics, 2016, vol. 76, no 5, pp. 2060-2080.

    https://hal.archives-ouvertes.fr/hal-01264980
  • 126M. Strugarek, N. Vauchelet, J. Zubelli.

    Quantifying the Survival Uncertainty of Wolbachia-infected Mosquitoes in a Spatial Model *, August 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01355118
  • 127P. Van Liedekerke, J. Neitsch, T. Johann, K. Alessandri, P. Nassoy, D. Drasdo.

    Quantitative modeling identifies robust predictable stress response of growing CT26 tumor spheroids under variable conditions, December 2016, working paper or preprint.

    https://hal.inria.fr/hal-01421179
  • 128P. Van Liedekerke, M. M. Palm, N. Jagiella, D. Drasdo.

    Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results, in: Computational Particle Mechanics, Nov 2015, vol. 2, no 4, pp. 401–444.

    http://dx.doi.org/10.1007/s40571-015-0082-3


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