Jean Clairambault: articles

  • Marion Rabé, Solenne Dumont, Arturo Álvarez-Arenas, Hicham Janati, Juan Belmonte-Beitia, Gabriel F. Calvo, Christelle Thibault-Carpentier, Quentin Séry, Cynthia Chauvin, Noémie Joalland, Floriane Briand, Stéphanie Blandin, Emmanuel Scotet, Claire Pecqueur, Jean Clairambault, Lisa Oliver, Victor Pérez García, Arulraj Nadaradjane, Pierre-François Cartron, Catherine Gratas, François M. Vallette. Identification of a transient state during the acquisition of temozolomide resistance in glioblastoma. Cell Death and Disease, 11:19, online January 2020.

  • Clairambault, J. An evolutionary perspective on cancer, with applications to anticancer drug resistance modelling and perspectives in therapeutic control. Journal of Mathematical Study, 52(4):470-496, online December 2019 (Selected works from the 17th International NUMACH workshop, Mulhouse, July 2018).

  • Clairambault, J., Pouchol, C. A survey of adaptive cell population dynamics models of emergence of drug resistance in cancer, and open questions about evolution and cancer. BIOMATH, vol. 8, issue 1, online May 2019 (23 pages).

  • Nguyen, T.N., Clairambault, J., Jaffredo, T., Perthame, B., Salort, D. Adaptive dynamics of hematopoietic stem cells and their supporting stroma: A model and mathematical analysis. BME, 16(5):4818-4845, online April 2019.

  • Almeida, L., Chisholm, R.H., Clairambault, J., Lorenzi, T., Lorz, A., Pouchol, C., Trélat, E. Why is evolution important in cancer and what mathematics should be used to treat cancer? Focus on drug resistance. In: ''Trends in Biomathematics: Modeling, Optimization and Computational Problems'', Rubem P. Mondaini Ed., pp. 107-120, Springer 2018, DOI: 10.1007/978-3-319-91092-5_8 (Selected works from the 17th International BIOMAT conference, Moscow, October-November 2017)

  • Djema, W., Bonnet, C., Mazenc, F., Clairambault, J., Fridman, E., Hirsch, P., Delhommeau, F. Control in Dormancy or Eradication of Cancer Stem Cells: Mathematical Modeling and Stability Issues. Journal of Theoretical Biology, 449:103-123, 2018.

  • Clairambault, J., Perthame, B., Quillas Maran, A. Analysis of a System Describing Proliferative-Quiescent Cell Dynamics. Chinese Annals of Mathematics, Ser. B, 39(2): 345-356, 2018. Preprint available

  • Pouchol, C., Clairambault, J., Lorz, A., Trélat, E. Asymptotic study and optimal control of integrodifferential systems modelling healthy and cancer cells exposed to chemotherapy. Journal de Mathématiques Pures et Appliquées, 116:268-308, 2018. Published online, October 2017, doi:10.1016/j.matpur.2017.10.007, preprint available, ArXiv reference

  • Goldman, A., Kohandel, M., Clairambault, J. Integrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer, Part 1: Biological Facts and Studies in Drug Resistance, Current Stem Cell Reports, 3:253-259 (read-only version; notice, DOI: 10.1007/s40778-017-0097-1), and Part 2: From Theoretical Biology to Mathematical Models, Current Stem Cell Reports, 3:260-268 (read-only version; notice, DOI: 10.1007/s40778-017-0098-0). Current Stem Cell Reports, published online, August 2017

  • Clairambault, J., Fercoq, O. Physiologically structured cell population dynamic models with applications to combined drug delivery optimisation in oncology. Mathematical Modelling of Natural Phenomena, 11(6):45-70, 2016. Preprint available

  • Bocharov, G., Bouchnita, A., Clairambault, J., Volpert, V. Mathematics of Pharmacokinetics and Pharmacodynamics: Diversity of Topics, Models and Methods. Mathematical Modelling of Natural Phenomena, December 2016, issue 6. Preprint available

  • Escargueil, A.E., Prado S., Dezaire, A., Clairambault, J., Larsen A.K., Soares, D.G. Genotype- or Phenotype-Targeting Anticancer Therapies? Lessons from Tumor Evolutionary Biology. Current Pharmaceutical Design, 22(44):6625-6644, 2016, doi:10.2174/1381612822666160831114002, PubMed abstract

  • Chisholm, R.H., Lorenzi, T., Clairambault, J. Cell population heterogeneity and evolution towards drug resistance in cancer: biological and mathematical assessment, theoretical treatment optimisation. BBA General Subjects, special issue on system genetics, 1860:2627-2645, 2016

  • Lorenzi, T., Chisholm, R.H., Clairambault, J. Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations. Biology Direct, Online, August 2016. Available online in open access.

  • Chisholm, R.H., Lorenzi, T., Lorz, A., Larsen, A.K., Almeida, L., Escargueil, A., Clairambault, J. Emergence of reversible drug tolerance in cancer cell populations: an evolutionary outcome of selection, non-genetic instability and stress-induced adaptation. Cancer Research, 75(6):930-939, 2015. Published on line January 2015

  • Lorz, A., Lorenzi, T., Clairambault, J., Escargueil, A., Perthame, B. Effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors. Bull. Math. Biol., 77(1):1-22, 2015. Preprint available

  • Eliaš, J., Clairambault, J. Reaction-diffusion systems for spatio-temporal intracellular protein networks: a beginner's guide with two examples. Computational and Structural Biotechnology Journal, 10:14-22, 2014. Available on line in open access.

  • Avila Alonso, J.L., Bonnet, C., Clairambault , J., Özbay, H., Niculescu, S.-I., Merhi, F., Ballesta, A., Tang, RP., Marie, J.-P. 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 eds, pp. 315-328, Advances in Delays and Dynamics series, Springer, New York, 2014. Preprint available.

  • Eliaš, J., Dimitrio, L., Clairambault, J., Natalini, R. Modelling p53 dynamics in single cells: physiologically based ODE and reaction-diffusion PDE models. IOP Physical Biology, vol. 11, number 4, 045001, 2014. Preprint available

  • Clairambault, J. Deterministic mathematical modelling for cancer chronotherapeutics: cell population dynamics and treatment optimisation. In "Mathematical Oncology 2013", A. d'Onofrio and A. Gandolfi Eds., Part III, pp. 265-294, Birkhäuser, New York, 2014. Preprint available

  • Eliaš, J., Dimitrio, L., Clairambault, J., Natalini, R. The p53 protein and its molecular network: modelling a missing link between DNA damage and cell fate. Biochimica et Biophysica Acta (BBA Proteins and Proteomics), 1844:232-247, 2014.

  • Ballesta, A., Clairambault, J. Physiologically based mathematical models to optimize therapies against metastatic colorectal cancer: a mini-review. Current Pharmaceutical Design, 2014; 20(1):37-48. Pubmed abstract, and corrected proofs, published on line, March 2013.

  • Billy, F., Clairambault, J., Delaunay, F., Feillet, C., Robert, N. Age-structured cell population model to study the influence of growth factors on cell cycle dynamics. Mathematical Biosciences and Engineering, 10(1):1-17, 2013. Preprint available

  • Billy, F., Clairambault, J. Designing proliferating cell population models with functional targets for control by anti-cancer drugs. DCDS-B, 18(4):865-889, 2013. In Special issue on cancer modelling or Preprint

  • Dimitrio, L., Clairambault, J., Natalini, R. A spatial physiological model for p53 intracellular dynamics. Journal of Theoretical Biology, 316:9-24, 2013. Preprint available

  • Lorz, A., Lorenzi, T., Hochberg, M.E., Clairambault, J., Perthame, B. Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies. Mathematical Modelling and Numerical Analysis, 47:377-399, 2013. DOI: http://dx.doi.org/10.1051/m2an/2012031 Preprint available

  • Ballesta, A., Clairambault, J., Dulong, S., Lévi, F. A systems biomedicine approach for chronotherapeutics optimization: focus on the anticancer drug irinotecan. In: ''New Challenges for Cancer Systems Biomedicine'', D'Onofrio, Alberto, Cerrai, Paola, Gandolfi, Alberto Eds., Part V, pp. 301-327, SIMAI Lecture Notes, Springer, New York, 2012. Preprint available

  • Billy, F., Clairambault, J., Fercoq, O. Optimisation of cancer drug treatments using cell population dynamics. In "Mathematical Models and Methods in Biomedicine'', A. Friedman, E. Kashdan, U. Ledzewicz and H. Schättler Eds., Part 4, pp. 265-309, Springer, New-York, 2013. Preprint available

  • Billy, F., Clairambault, J., Fercoq, O., Gaubert, S., Lepoutre, T., Ouillon, T., Saito, S. Synchronisation and control of proliferation in cycling cell population models with age structure. Mathematics and Computers in Simulation, 96:66-94, 2014. Available on line, April 2012, http://dx.doi.org/10.1016/j.matcom.2012.03.005.

  • Özbay, H., Bonnet, C., Benjelloun, H., Clairambault, J. Stability analysis of cell dynamics in leukemia. Mathematical Modelling of Natural Phenomena, 7(1):203-234, 2012.

  • Ballesta, A., Dulong, S., Abbara, C., Cohen, B., Okyar, A., Clairambault, J., Levi, F. A Combined Experimental and Mathematical Approach for Molecular-based Optimization of Irinotecan Circadian Delivery. PLoS Comput Biol 7(9): e1002143. doi:10.1371/journal.pcbi.1002143, 2011.

  • Clairambault, J. Optimising cancer pharmacotherapeutics using mathematical modelling and a systems biology approach. Personalized Medicine, 8(3):271-286, 2011.

  • Ballesta, A., Clairambault, J., Dulong, S., Lévi, F. Theoretical Optimization of Irinotecan-based Anticancer Strategies in case of Drug-induced Efflux. Applied Mathematics Letters, 24(7):1251-56, 2011.

  • Clairambault, J., Gaubert, S., Lepoutre, T. Circadian rhythm and cell population growth. Mathematical and Computer Modelling, 53:1558-1567, 2011.

  • Lévi, F., Okyar, A., Dulong, S., Innominato, P.F., Clairambault, J. Circadian Timing in Cancer Treatments. Annual Review of Pharmacology and Toxicology, Vol. 50:377-421, 2010.

  • Steimer, J.-L., Dahl, S.G., De Alwis, D., Gundert-Remy, U., Karlsson, M., Martinkova, J., Aarons, L., Ahr, H.-J., Clairambault, J., Freyer, G., Friberg, L. , Ludwig, W.-D., De Nicolao, G., Rocchetti, M., Troconiz, I.F. Modelling the genesis and treatment of cancer: the potential role of physiologically-based pharmacodynamics. European Journal of Cancer, 46(1):21-32, 2010.

  • Clairambault, J. Modelling physiological and pharmacological control on cell proliferation to optimise cancer treatments. Mathematical Modelling of Natural Phenomena, 4(3):12-67, 2009.

  • Clairambault, J., Gaubert, S., Lepoutre, T. Comparison of Perron and Floquet eigenvalues in age structured cell division models. Mathematical Modelling of Natural Phenomena, 4(3):183-209, 2009.

  • Lévi, F., Altinok, A., Clairambault, J., Goldbeter, A. Implications of circadian clocks for the rhythmic delivery of cancer therapeutics. Phil. Trans. Roy. Soc. A, 366(1880):3575-3598, 2008.

  • Adimy, M., Bernard, S., Clairambault, J., Crauste, F., Génieys, S., Pujo-Menjouet, L. Modélisation de la dynamique de l'hématopoïèse normale et pathologique. Hématologie, 14(5):339-350, 2008.

  • Clairambault, J. A Step Toward Optimization of Cancer Therapeutics. Physiologically Based Modeling of Circadian Control on Cell Proliferation. IEEE-EMB Magazine, 27(1):20-24, 2008.

  • Bekkal Brikci, F., Clairambault, J., Ribba, B., Perthame, B. An age-and-cyclin-structured cell population model for healthy and tumoral tissues, Journal of Mathematical Biology, 57(1):91-110, 2008.

  • Bekkal Brikci, F., Clairambault, J., Perthame, B. Analysis of a molecular structured population model with polynomial growth for the cell cycle, Mathematical and Computer Modelling, 47(7-8): 699-713, 2008.

  • Clairambault, J., Gaubert, S., Perthame, B. An inequality for the Perron and Floquet eigenvalues of monotone differential systems and age structured equations. C. R. Acad. Sci. (Paris) Ser. I Mathématique, 345(10):549-554, 2007.

  • Clairambault, J. Modeling oxaliplatin drug delivery to circadian rhythm in drug metabolism and host tolerance. Advanced Drug Delivery Reviews (ADDR), 59:1054-1068, 2007.

  • Clairambault, J., Michel, Ph., Perthame, B. Circadian rhythm and tumour growth. C. R. Acad. Sci. (Paris) Ser. I Mathématique, 342(1):17-22, 2006.

  • Basdevant, C., Clairambault, J., Lévi, F. Optimisation of time-scheduled regimen for anti-cancer drug infusion. Mathematical Modelling and Numerical Analysis, 39(6):1069-1086, 2005.

  • Clairambault, J., Claude, D., Filipski, E., Granda, T., Lévi, F. Toxicité et efficacité antitumorale de l'oxaliplatine sur l'ostéosarcome de Glasgow induit chez la souris : un modèle mathématique. Pathologie-Biologie, 51:212-215, 2003.

  • Claude, D., Clairambault, J. Period shift induction by intermittent stimulation in a Drosophila model of PER protein oscillations, Chronobiology International, 17(1):1-14, 2000.

  • Swynghedauw, B., Jasson, S., Clairambault, J., Chevalier, B., Heymes, C., Médigue, C., Carré, F., Mansier, P. Myocardial determinants in regulation of the heart rate. Journal of Molecular Medicine, 75:860-866, 1997.

  • Mansier, P., Médigue, C., Charlotte, N., Vermeiren, C., Coraboeuf, E., Deroubai, E., Ratner, E., Chevalier, B., Clairambault, J., Carré, F., Dahkli, T., Bertin, B., Briand, P., Strosberg, D., Swynghedauw, B. Decreased heart rate variability in transgenic mice overexpressing atrial $\beta_1$-adrenoceptors. American Journal of Physiology, 271:H1465-H1472, 1996.

  • Mansier, P., Clairambault, J., Charlotte, N., Médigue, C., Vermeiren, C., LePape, G., Carré, F., Gounaropoulou, A., Swynghedauw, B. Linear and non-linear analyses of heart rate variability: a minireview. Cardiovascular Research, 31:371-379, 1996.

  • Spassov, L., Curzi-Dascalova, L., Clairambault, J., Kauffmann, F., Médigue, C., Peirano, P. Heart rate and heart rate variability in small-for-gestational-age newborns. Pediatric Research, 35:500-505, 1994.

  • Eiselt, M., Curzi-Dascalova, L., Clairambault, J., Médigue, C., Peirano, P. Heart-rate variability in low-risk prematurely born infants reaching normal term: a comparison with full-term newborns. Early Human Development, 32, 183-195, 1993.

  • Clairambault, J., Curzi-Dascalova, L., Kauffmann, F. Médigue, C., Leffler, C. Heart Rate Variability in normal sleeping full-term and preterm neonates. Early Human Development, 28:169-183, 1992.

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