Publications

  1. Perlstein D, Shlagman O, Kogan Y, Halevi-Tobias K, Yakobson A, Lazarev I, et al. Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug. PLoS One. 2019;14(12):e0226869. doi: 10.1371/journal.pone.0226869. PubMed PMID: 31877168.
  2. Tsur N, Kogan Y, Rehm M, Agur Z. Response of Patients with Melanoma to Immune Checkpoint Blockade – Insights Gleaned from Analysis of a New Mathematical Mechanistic Model. J Journal of Theoretical Biology 2019:110033. doi: 10.1016/j.jtbi.2019.110033. PubMed PMID: 31580835.
  3. Tsur N, Kogan Y, Avizov-Khodak E, Vaeth D, Vogler N, Utikal J, Lotem M, Agur Z. Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm. J Journal of Translational Medicine 2019;17(1):338. doi: 10.1186/s12967-019-2081-2. PubMed PMID: 31590677.
  4. Kogan Y, Elishmereni M, Taub E, Agur Z. P-401 The tumor burden rise at radiological disease progression associates with poor overall survival in advanced colorectal cancer. Annals of Oncology (2019) 30(Supplement_4). doi: 10.1093/annonc/mdz183.006.
  5. Kogan Y, Shannon S, Taub E, Kleiman M, Elishmereni M, Agur Z. Predicting imminent disease progression in advanced colorectal cancer by a machine-learning algorithm. J Clin Oncol 37, 2019 (suppl 4; abstr 645)
  6. Kogan Y, Kleiman M, Shannon S, Elishmereni E, Taub E, Aptekar L, Brenner R. M, Berger R, Nechushtan H, Agur Z. A new algorithm predicting imminent disease progression in advanced NSCLC patients by machine-learning integration of five serum biomarkers. J Clin Oncol 36, 2018 (suppl; abstr e21190).
  7. Hochman G, Halevi-Tobias K, Kogan Y, Agur Z. Extracellular inhibitors can attenuate tumorigenic Wnt pathway activity in adenomatous polyposis coli mutants: Predictions of a validated mathematical model. J PLoS One 2017 Jul 14;12(7):e0179888.
  8. Agur Z, Halevi-Tobias K, Kogan Y, et al. Employing Dynamical Computational Models for Personalizing Cancer Immunotherapy. J Expert Opinion on Biological Therapy 2016 16(11) pp.1373-1385.
  9. Forys U, Bodnar M, Kogan Y.Asymptotic dynamics of some t-periodic one-dimensional model with application to prostate cancer immunotherapy. J Math Biol 2016 73(4) pp. 867-83 doi: 10.1007/s00285-016-0978-4.
  10. Kogan Y, Agur Z, Elishmereni M. A mathematical model for the immunotherapeutic control of the Th1/Th2 imbalance in melanoma. Discrete and Continuous Dynamics – Series B 2013, 18(4) pp. 1017-1030.doi:10.3934/dcdsb.2013.18.1017.
  11. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk Pavlović S, Agur Z. Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-Time Personalization Cancer Res. 2012 Mar 19. 72(9) pp.2218-2227, PMID: 22422938
  12. Kogan Y, Halevi-Tobias KE, Hochman G, Baczmanska AK, Leyns L, Agur, Z. A new validated mathematical model of the Wnt signaling pathway predicts effective combinational therapy by sFRP and Dkk Biochem J 2012. 444 pp. 115–125 PMID; 22356261
  13. Vainstein V, Kirnasovsky OU, Kogan Y, Agur Z. Strategies for cancer stem cell elimination: Insights from mathematical modeling. J Theor Biol. 2012 7(298) pp.32-41.
  14. Kronik N, Kogan Y, Schlegel PG, Wölfl M. Improving T-cell Immunotherapy for Melanoma Through a Mathematically Motivated Strategy: Efficacy in Numbers? Journal of Immunotherapy 2012 35(2) 110.1097/CJI.1090b1013e318236054c.
  15. Agur Z, Kirnasovsky OU, Vasserman G, Tencer-Hershkowicz L, Kogan Y, Harrison H, et al. Dickkopf1 regulates fate decision and drives breast cancer stem cells to differentiation: an experimentally supported mathematical model. PLoS one 2011 6(9) e24225
  16. Kronik N., Kogan Y., Elishmereni M., Halevi-Tobias K., Vuk Pavlović S., Agur Z. Predicting Effect of Prostate Cancer Immunotherapy by Personalized Mathematical Models PLoS one 2010 5(12) e15482
  17. Agur Z., Kogan Y., Levi L., Harrison H., Lamb R., Kirnasovsky O.U., Clarke R.B. Disruption of a Quorum Sensing Mechanism Triggers Tumorigenesis: a Simple Discrete Model Corroborated by Experiments in Mammary Cancer Stem Cells. Biol Direct 2010 5(1) pp.20-41.
  18. Kogan Y., Forys U., Shukron O., Kronik N., Agur Z. Cellular immunotherapy for high grade Gliomas: mathematical analysis deriving efficacious infusion rates based on patient requirements. SIAM J. Appl. Math. 2010 70(6) pp. 1953-1976.
  19. Kirnasovsky O.U., Kogan Y, Agur Z. Analysis of a mathematical model for the molecular mechanism of mammary stem cell fate decision. Mathematical Modelling of Natural Phenomena 2008 3(7) pp. 78-89.
  20. Kirnasovsky O.U., Kogan Y., Agur Z. Resilience in Stem Cell renewal: Development of the Agur – Daniel – Ginossar Model. Disc. Cont. Dyn. Systems 2008 10 pp.129- 148.
  21. Kogan Y., Ribba B., Dahan N., Marron K., Vainstein V., Agur Z. Intensifed Doxorubicin-Based Regimen Efficacy in Residual Non-Hodgkin’s Lymphoma Disease: Towards a Computationally Supported Treatment Improvement. Mathematical Modelling of Natural Phenomena 2007 2(3), pp. 47-68.
  22. Kronik N., Kogan Y., Vainstein V., Agur Z. Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics. Cancer Immunol Immunother 2007 Mar;57(3):425-439 (2008). Epub 2007 Sep 7.
  23. Vainstein V., Ginosar Y., Shoham M., Ianovski A., Rabinovich A., Kogan Y., Selitser V., Agur Z. Improving cancer therapy by Doxorubicin and Granulocyte colony-stimulating factor: Insights from a Computerized Model of Human Granulopoiesis. Mathematical Modelling of Natural Phenomena 2006 1(2), pp.70-80.
  24. Forys U., Kheifetz Y., Kogan Y. Critical-point analysis for three-variable cancer angiogenesis modeling. Mathematical Biosciences and Engineering. 2005; 2 (3): 511-525.
  25. Kheifetz Y., Kogan Y. & Agur Z. Long-range predictability in models of cell populations subjected to phase-specific drugs: Growth-rate approximation using properties of positive compact operators. Mathematical Models & Methods in the Applied Sciences. 2006; 16 (7), 1-18.
  26. Kheifetz Y., Kogan Y., Agur Z. Matrix and compact operator description of resonance and antiresonance in cell populations subjected to phase specific drugs. Jour. Medical Informatics & Technologies. 2004; 8, MM-11 – MM-29.