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.


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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 18A. Lorz, B. Perthame, C. Taing.

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

  • 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.

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]


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 ]

  • 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 ]


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.

  • 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 ]


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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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.

  • 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.

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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.

  • 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.

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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.

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 87C. Emako, M. Tang.

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

  • 88C. Emako Kazianou.

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

  • 89S. Eugene.

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

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 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 ]

  • 95V. H. Hoang.

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

  • 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 ]

  • 97N. Jagiella.

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

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 107T. Lorenzi, R. H. Chisholm, A. Lorz.

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

  • 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 ]

  • 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 ]

  • 110A. Mellet, B. Perthame, F. Quiros.

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

  • 111A. Olivier.

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

  • 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 ]

  • 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.

  • 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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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.

  • 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 ]

  • 121B. Perthame, J. Zubelli.

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