Paul Macklin's Math Cancer Lab Website

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Publications (in reverse chronological order)

Journal Articles

  1. J. Wang, A. Delfarah, P. Gelbach, E. Fong, P. Macklin, S. Mumenthaler, N.A. Graham, and S.D. Finley. Elucidating Tumor-stromal Metabolic Crosstalk in Colorectal Cancer through Integration of Constraint-Based Models and LC-MS Metabolomics. Metabolic Eng., 2021 (accepted).
  2. E.J. Fertig, E.M. Jaffee, P. Macklin, V. Stearns, and C. Wang. Forecasting cancer: from precision to predictive medicine. Med 2(9):1004-10, 2021. DOI: 10.1016/j.medj.2021.08.007.
  3. H.L. Rocha, I. Godet, F. Kurtoglu, J. Metzcar, K. Konstantinopoulos, S. Bhoyar, D.M. Gilkes, and P. Macklin. A persistent invasive phenotype in post-hypoxic tumor cells is revealed by fate mapping and computational modeling. iScience 24(9):102935, 2021. DOI: 10.1016/j.isci.2021.102935.
  4. B. Duggan, J. Metzcar, and P. Macklin. DAPT: A Package Enabling Distributed Automated Parameter Testing. Gigabyte 2021, 2021. DOI: 10.46471/gigabyte.22.
  5. A. Madamanchi, M. Thomas, A. Magana, R. Heiland, and P. Macklin. Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab. PRIMUS, 2021. DOI: 10.1080/10511970.2021.1881849.
  6. C.-T. Chiang, R. Lau, A. Ghaffarizadeh, M. Brovold, D. Vyas, E.F. Juarez, A. Atala, D.B. Agus, S. Soker, P. Macklin, D. Ruderman, and S.M. Mumenthaler. High throughput microscopy reveals the impact of multifactorial environmental perturbations on colorectal cancer cell growth. GigaScience 10(4):giab026, 2021. DOI: 10.1093/gigascience/giab026.
  7. Y. Wang, E. Brodin, K. Nishii, H.B. Frieboes, S.M. Mumenthaler, J.L. Sparks, and P. Macklin. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Sci. Rep. 11:1710, 2021. DOI: 10.1038/s41598-020-78780-7.
  8. S. Madhavan, R.A. Beckman, M.D. McCoy, M.J. Pishvaian, J.R. Brody, and P. Macklin. Envisioning the future of precision oncology trials. Nat. Cancer, 2021. DOI: 10.1038/s43018-020-00163-8.
  9. Z. Hasnain, A.K. Fraser, D. Georgess, A. Choi, P. Macklin, J.S. Bader, S.R. Peyton, A.J. Ewald, and P.K. Newton. OrgDyn: Feature and model based characterization of spatial and temporal organoid dynamic. Bioinformatics 36(10):3292-4, 2020. DOI: 10.1093/bioinformatics/btaa096.
  10. J.B. Xavier, V.B. Young, J. Skufca, F. Ginty, T. Testerman, A.T. Pearson, P. Macklin, A. Mitchell, I. Shmulevich, L. Xie, J.G. Caporaso1, K.A. Crandall, N.L. Simone, F. Godoy-Vitorino, T.J. Griffin, K.L. Whiteson, H.H. Gustafson, D.J. Slade, T.M. Schmidt, M.R. Walther-Antonio, T. Korem, B.-J. Webb-Robertson, M.P. Styczynski, W.E. Johnson, C. Jobin, J.M. Ridlon, A.Y. Koh, M. Yu, L. Kelly, and J.A. Wargo. The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View. Trends in Cancer 6(3):192-204, 2020. DOI: 10.1016/j.trecan.2020.01.004.
  11. P. Macklin. Key challenges facing data-driven multicellular systems biology. GigaScience 8(10), 2019. DOI: 10.1093/gigascience/giz127.
  12. M.P. Snyder, S. Lin, A. Posgai, M. Atkinson, A. Regev, J. Rood, O. Rozenblatt-Rosen, L. Gaffney, A. Hupalowska, R. Satija, N. Gehlenborg, J. Shendure, J. Laskin, P. Harbury, N.A. Nystrom, J.C. Silverstein, Z. Bar-Joseph, K. Zhang, , Y. Lin, R. Conroy, D. Procaccini, A.L. Roy, A. Pillai, M. Brown, Z.S. Galis, L. Cai, J. Shendure, C. Trapnell, S. Lin, D. Jackson, M.P. Snyder, G. Nolan, W.J. Greenleaf, Y. Lin, S. Plevritis, S. Ahadi, S.A. Nevins, H. Lee, C.M. Schuerch, S. Black, V.G. Venkataraaman, E. Esplin, A. Horning, A. Bahmani, K. Zhang, X. Sun, S. Jain, J. Hagood, G. Pryhuber, P. Kharchenko, M. Atkinson, B. Bodenmiller, T. Brusko, M. Clare-Salzler, H. Nick, K. Otto, A. Posgai, C. Wasserfall, M. Jorgensen, M. Brusko, S. Maffioletti, R.M. Caprioli, J.M. Spraggins, D. Gutierrez, N.H. Patterson, E.K. Neumann, R. Harris, M. deCaestecker, A.B. Fogo, R. van de Plas, K. Lau, L. Cai, G.-C. Yuan, Q. Zhu, R. Dries, P. Yin, S.K. Saka, J.Y. Kishi, Y. Wang, I. Goldaracena, J. Laskin, D. Ye, K.E. Burnum-Johnson, P.D. Piehowski, C. Ansong, Y. Zhu, P. Harbury, T. Desai, J. Mulye, P. Chou, M. Nagendran, Z. Bar-Joseph, S.A. Teichmann, B. Paten, R.F. Murphy, J. Ma, V.Y. Kiselev, C. Kingsford, A. Ricarte, M. Keays, S.A. Akoju, M. Ruffalo, N. Gehlenborg, P. Kharchenko, M. Vella, C. McCallum, , L.E. Cross, S.H. Friedman, R. Heiland, B. Herr, P. Macklin, E.M. Quardokus, L. Record, J.P. Sluka, G.M. Weber, N.A. Nystrom, J.C. Silverstein, P.D. Blood, A.J. Ropelewski, W.E. Shirey, R.M. Scibek, P. Mabee, W.C. Lenhardt, K. Robasky, S. Michailidis, R. Satija, J. Marioni, A. Regev, A. Butler, T. Stuart, E. Fisher, S. Ghazanfar, J. Rood, L. Gaffney, G. Eraslan, T. Biancalani, E.D. Vaishnav, R. Conroy, D. Procaccini, A. Roy, A. Pillai, M. Brown, Z. Galis, P. Srinivas, A. Pawlyk, S. Sechi, E. Wilder, J. Anderson, . HuBMAP Consortium, . Writing Group, . Caltech-UW TMC, . Stanford-WashU TMC, . UCSD TMC, . University of Florida TMC, . Vanderbilt University TMC, . California Institute of Technology TTD, . Harvard TTD, . Purdue TTD, . Stanford TTD, . HuBMAP Integration Visualization and Engagement (HIVE) Collaboratory:, . Carnegie Mellon Tools Component, . Harvard Medical School Tools Component, . Indiana University Bloomington Mapping Component, . Pittsburgh Supercomputing Center and University of Pittsburgh Infrastructure and Engagement Component, . University of South Dakota Collaboration Core, . New York Genome Center Mapping Component, and . NIH HuBMAP Working Group. The Human Body at Cellular Resolution: The NIH Common Fund Human BioMolecular Atlas Program. Nature 574:187-192, 2019. DOI: 10.1038/s41586-019-1629-x.
  13. A. Madamanchi, R. Heiland, P. Macklin, and A.J. Magana. Students' Use of Metacognitive Skills in Undergraduate Research Experiences in Computational Modeling. IEEE Frontiers in Education (FIE) Conference - Cincinnati, OH USA, 2019 (accepted).
  14. R. Heiland, D. Mishler, T. Zhang, E. Bower, and P. Macklin. xml2jupyter: Mapping parameters between XML and Jupyter widgets. Journal of Open Source Software 4(39):1408, 2019. DOI: 10.21105/joss.01408.
  15. J. Ozik, N. Collier, R. Heiland, G. An, and P. Macklin. Learning-accelerated Discovery of Immune-Tumour Interactions. Molec. Syst. Design Eng. 4:747-60, 2019. DOI: 10.1039/c9me00036d.
  16. R. Rockne, A. Hawkins-Daarud, K. Swanson, J. Sluka, J. Glazier, P. Macklin, D. Hormuth, A. Jarrett, E. Lima, J. Oden, G. Biros, T. Yankeelov, K. Curtius, I. Bakir, D. Wodarz, N. Komarova, L. Aparicio, M. Bordyuh, R. Rabadan, S. Finely, H. Enderling, J. Caudell, E. Moros, A. Anderson, R. Gatenby, A. Kaznatcheev, P. Jeavons, N. Krishnan, J. Pelesko, R. Wadhwa, N. Yoon, D. Nichol, A. Marusyk, M. Hinczewski, and J. Scott. The 2019 Mathematical Oncology Roadmap. Phys. Biol. 16(4):041005, 2019. DOI: 10.1088/1478-3975/ab1a09.
  17. G. Letort, A. Montagud, G. Stoll, R. Heiland, E. Barillot, P. Macklin, A. Zinovyev, and L. Calzone. PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling. Bioinformatics 35(7):1188-96, 2019. DOI: 10.1093/bioinformatics/bty766.
  18. J. Metzcar, Y. Wang, R. Heiland, and P. Macklin. A review of cell-based computational modeling in cancer biology. JCO Clinical Cancer Informatics 3:1-13, 2019 (invited review). DOI: 10.1200/CCI.18.00069.
  19. E.F. Juarez, C. Garri, A. Ghaffarizadeh, P. Macklin, and K. Kani. Quantification of Cancer Cell Migration with an Integrated Experimental-Computational Pipeline. F1000Research 7:1296, 2018 (version 1, awaiting open peer review). DOI: 10.12688/f1000research.15599.1.
  20. J. Ozik, N. Collier, J. Wozniak, C. Macal, C. Cockrell, S.H. Friedman, A. Ghaffarizadeh, R. Heiland, G. An, and P. Macklin. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. BMC Bioinformatics 19:483, 2018. DOI: 10.1186/s12859-018-2510-x.
  21. R.R. Rawat, D. Ruderman, P. Macklin, D.L. Rimm, and D.B. Agus. Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens. npj Breast Cancer 4(1):32, 2018. DOI: 10.1038/s41523-018-0084-4.
  22. A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14(2):e1005991, 2018. DOI: 10.1371/journal.pcbi.1005991.
  23. P. Macklin. When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments. Cell Sys. 5(2):92-4, 2017. DOI: 10.1016/j.cels.2017.08.005.
  24. E.F. Juarez, R. Lau, S.H. Friedman, A. Ghaffarizadeh, E. Jonckheere, D.B. Agus, S.M. Mumenthaler, and P. Macklin. Quantifying differences in cell line population dynamics using CellPD. BMC Sys. Biol. 10(1):1-12, 2016. DOI: 10.1186/s12918-016-0337-5.
  25. P. Newton, J. West, Z. Hassnain, and P. Macklin. An evolutionary model of tumor cell kinetics and the emergence of molecular heterogeneity driving Gompertzian growth. SIAM Rev. 58(4):716-36, 2016. DOI: 10.1137/15M1044825.
  26. A. Ghaffarizadeh, S.H. Friedman, and P. Macklin. BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32(8):1256-8, 2016. DOI: 10.1093/bioinformatics/btv730.
  27. S.M. Mumenthaler, G. D'Antonio, L. Preziosi, and P. Macklin. The need for integrative computational oncology: An illustrated example through MMP-mediated tissue degradation. Front. Oncol. 3:194, 2013. DOI: 10.3389/fonc.2013.00194.
  28. A.Z. Hyun and P. Macklin. Improved patient-specific calibration for agent-based cancer modeling. J. Theor. Biol. 317:422-4, 2013. DOI: 10.1016/j.jtbi.2012.10.017.
  29. G. D'Antonio, P. Macklin, and L. Preziosi. An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix. Math. Biosci. Eng. 10(1):75-101, 2013. DOI: 10.3934/mbe.2013.10.75.
  30. P. Macklin, M.E. Edgerton, A.M. Thompson, and V. Cristini. Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression. J. Theor. Biol. 301:122-40, 2012. DOI: 10.1016/j.jtbi.2012.02.002.
  31. H. Hatzikirou, A. Chauviere, A.L. Bauer, A. Leier, M.T. Lewis, P. Macklin, T.T. Marquez-Lago, E.L. Bearer, and V. Cristini. Integrative physical oncology. WIREs Syst. Biol. Med. 4(1):1-14, 2012 (invited author: V. Cristini). DOI: 10.1002/wsbm.158.
  32. M.E. Edgerton, Y.-L. Chuang, P. Macklin, W. Yang, E.L. Bearer, and V. Cristini. A novel, patient-specific mathematical pathology approach for assessment of surgical volume: Application to ductal carcinoma in situ of the breast. Anal. Cell. Pathol. 34(5):247-63, 2011. DOI: 10.3233/ACP-2011-0019.
  33. T.S. Deisboeck, Z. Wang, P. Macklin, and V. Cristini. Multiscale Cancer Modeling. Annu. Rev. Biomed. Eng. 13:127-155, 2011 (invited author: T.S. Deisboeck). DOI: 10.1146/ANNUREV-BIOENG-071910-124729.
  34. J. Lowengrub, H.B. Frieboes, F. Jin, Y.-L. Chuang, X. Li, P. Macklin, S.M. Wise, and V. Cristini. Nonlinear modelling of cancer: Bridging the gap between cells and tumors. Nonlinearity 23(1):R1-R91, 2010 (invited author: J. Lowengrub). DOI: 10.1088/0951-7715/23/1/R01.
  35. P. Macklin, S.R. McDougall, A.R.A. Anderson, M.A.J. Chaplain, V. Cristini, and J.S. Lowengrub. Multiscale modelling and nonlinear simulation of vascular tumour growth. J. Math. Biol. 58(4-5):765-798, 2009. DOI: 10.1007/s00285-008-0216-9.
  36. P. Macklin and J.S. Lowengrub. A New Ghost Cell/Level Set Method for Moving Boundary Problems: Application to Tumor Growth. J. Sci. Comput. 35(2-3):266-299, 2008 (invited author: J.S. Lowengrub). DOI: 10.1007/s10915-008-9190-z.
  37. H.B. Frieboes, J.S. Lowengrub, S.M. Wise, X. Zheng, P. Macklin, E.L. Bearer, and V. Cristini. Computer Simulation of Glioma Growth and Morphology. NeuroImage 37:S59-S70, 2007. DOI: 10.1016/j.neuroimage.2007.03.008.
  38. P. Macklin and J.S. Lowengrub. Nonlinear simulation of the effect of microenvironment on tumor growth. J. Theor. Biol. 245(4):677-704, 2007. DOI: 10.1016/j.jtbi.2006.12.004.
  39. P. Macklin and J.S. Lowengrub. An improved geometry-aware curvature discretization for level set methods: application to tumor growth. J. Comput. Phys. 215(2):392-401, 2006. DOI: 10.1016/
  40. P. Macklin and J.S. Lowengrub. Evolving interfaces via gradients of geometry-dependent interior Poisson problems: application to tumor growth. J. Comput. Phys. 203(1):191-220, 2005. DOI: 10.1016/

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Book Chapters

  1. P. Macklin, H.B. Frieboes, J.L. Sparks, A. Ghaffarizadeh, S.H. Friedman, E.F. Juarez, E. Jockheere, and S.M. Mumenthaler. "Progress Towards Computational 3-D Multicellular Systems Biology". In: . Rejniak (ed.), Systems Biology of Tumor Microenvironment, chap. 12, pp. 225-46, Springer, 2016. ISBN: 978-3-319-42021-9. (invited author: P. Macklin). DOI: 10.1007/978-3-319-42023-3_12.
  2. J. Poleszczuk, P. Macklin, and H. Enderling. "Agent-based modeling of cancer stem cell driven solid tumor growth". In: K. Turksen (ed.), Methods and Protocols: Stem Cell Heterogeneity, chap. 24, pp. 335-46, Springer, 2016. ISBN: 978-1-4939-6549-6. (invited author: H. Enderling). DOI: 10.1007/7651_2016_346.
  3. P. Macklin, S. Mumenthaler, and J. Lowengrub. "Modeling multiscale necrotic and calcified tissue biomechanics in cancer patients: application to ductal carcinoma in situ (DCIS)". In: A. Gefen (ed.), Multiscale Computer Modeling in Biomechanics and Biomedical Engineering, chap. 13, pp. 349-80, Springer, Berlin, Germany, 2013. ISBN: 9783642364815. (invited author: P. Macklin). DOI: 10.1007/8415_2012_150.
  4. P. Macklin, M.E. Edgerton, and V. Cristini. "Agent-based cell modeling: application to breast cancer". In: V. Cristini and J.S. Lowengrub, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach, chap. 10, pp. 206-234, Cambridge University Press, Cambridge, UK, 2010. ISBN: 9780521884426. (invited author: P. Macklin). DOI: 10.1017/CBO9780511781452.011.
  5. P. Macklin, M.E. Edgerton, J.S. Lowengrub, and V. Cristini. "Discrete cell modeling". In: V. Cristini and J.S. Lowengrub, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach, chap. 6, pp. 88-122, Cambridge University Press, Cambridge, UK, 2010. ISBN: 9780521884426. (invited author: P. Macklin). DOI: 10.1017/CBO9780511781452.007.
  6. P. Macklin. "Biological background". In: V. Cristini and J.S. Lowengrub, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach, chap. 2, pp. 8-23, Cambridge University Press, Cambridge, UK, 2010. ISBN: 9780521884426. (invited author: P. Macklin). DOI: 10.1017/CBO9780511781452.003.
  7. P. Macklin, J. Kim, G. Tomaiuolo, M.E. Edgerton, and V. Cristini. "Agent-Based Modeling of Ductal Carcinoma in Situ: Application to Patient-Specific Breast Cancer Modeling". In: T. Pham (ed.), Computational Biology: Issues and Applications in Oncology, chap. 4, pp. 77-111, Springer, New York, NY USA, 2009. ISBN: 9781441908100. (invited author: P. Macklin). DOI: 10.1007/978-1-4419-0811-7_4.
  8. V. Cristini, H.B. Frieboes, X. Li, J.S. Lowengrub, P. Macklin, S. Sanga, S.M. Wise, and X. Zheng. "Nonlinear modeling and simulation of tumor growth". In: N. Bellomo, M.A.J. Chaplain, and E. de Angelis (eds.), Selected topics in cancer modeling: Genesis, evolution, immune competition, and therapy. Modelling and Simulation in Science, Engineering, and Technology, chap. 6, pp. 113-82, Birkhäuser, Boston, MA USA, 2008. ISBN: 9780817647124. (invited author: V. Cristini). DOI: 10.1007/978-0-8176-4713-1.

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Preprints and Works in Review

  1. M. Stack, P. Macklin, R. Searles, A.L. Gryshuk, and S. Chandrasekaran. OpenACC Acceleration of an Agent-Based Biological Simulation Framework. arXiv [Preprint] 2110.13368, 2021 (in review).
  2. T. Hernandez-Boussard, P. Macklin, E.J. Greenspan, A.L. Gryshuk, E. Stahlberg, T. Syeda-Mahmood, and I. Shmulevich. Digital Twins for Predictive Oncology will be a Paradigm Shift for Precision Cancer Care. Nat. Medicine, 2021.
  3. K.H. Risner, K.V. Tieu, Y. Wang, A. Bakovic, F. Alem, N. Bhalla, S. Nathan, D.E. Conway, P. Macklin, and A. Narayanan. Maraviroc inhibits SARS-CoV-2 multiplication and s-protein mediated cell fusion in cell culture. bioRxiv [preprint], 2020 (in review). DOI: 10.1101/2020.08.12.246389.
  4. M. Getz, Y. Wang, G. An, A. Becker, C. Cockrell, N. Collier, M. Craig, C.L. Davis, J. Faeder, A.N. Ford Versypt, J.F. Gianlupi, J.A. Glazier, S. Hamis, R. Heiland, T. Hillen, D. Hou, M.A. Islam, A. Jenner, F. Kurtoglu, B. Liu, F. Macfarlane, P. Maygrundter, P.A. Morel, A. Narayanan, J. Ozik, E. Pienaar, P. Rangamani, J.E. Shoemaker, A.M. Smith, and P. Macklin. Rapid community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv [preprint] 2020.04.02.019075, 2021. DOI: 10.1101/2020.04.02.019075.
  5. S.H. Friedman and P. Macklin. Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification. bioRxiv [preprint] 175703, 2017. DOI: 10.1101/175703.
  6. S.H. Friedman, A.R.A. Anderson, D.M. Bortz, A.G. Fletcher, H.B. Frieboes, A. Ghaffarizadeh, D.R. Grimes, A. Hawkins-Daarud, S. Hoehme, E.F. Juarez, C. Kesselman, R.M.H. Merks, S.M. Mumenthaler, P.K. Newton, K.-A. Norton, R. Rawat, R.C. Rockne, D. Ruderman, J. Scott, S.S. Sindi, J.L. Sparks, K. Swanson, D.B. Agus, and P. Macklin. MultiCellDS: a standard and a community for sharing multicellular data. bioRxiv [preprint] 090696, 2016 (in revision). DOI: 10.1101/090696.
  7. S.H. Friedman, A.R.A. Anderson, D.M. Bortz, A.G. Fletcher, H.B. Frieboes, A. Ghaffarizadeh, D.R. Grimes, A. Hawkins-Daarud, S. Hoehme, E.F. Juarez, C. Kesselman, R.M.H. Merks, S.M. Mumenthaler, P.K. Newton, K.-A. Norton, R. Rawat, R.C. Rockne, D. Ruderman, J. Scott, S.S. Sindi, J.L. Sparks, K. Swanson, D.B. Agus, and P. Macklin. MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data. bioRxiv [preprint] 090456, 2016 (in revision). DOI: 10.1101/090456.

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Published Abstracts

  1. J. Benson and P. Macklin, 43. Cell-based modeling of mechanical and chemical stress in tissues during cryoprotocols, Cryobiology 71 (1): 176, Abstract 43, 2015.
  2. A. Kumar, Y.-L. Chuang, P. Macklin, S. Sanga, J. Kim, G. Tomaiuolo, V. Cristini, and M.E. Edgerton, A model to predict the proliferation index of ductal carcinoma in situ, Proc. Am. Assoc. Cancer Res. (AACR) 2009 Abstract 2444, 2009.
  3. M.E. Edgerton, Y.-L. Chuang, P. Macklin, J. Kim, G. Tomaiuolo, A.D. Broom, S. Sanga, and V. Cristini, Simulation of growth of DCIS parameterized from IHC, Modern. Pathol. 22 (Suppl. 1):37A-38A, Abstract 157, 2009.
  4. M.E. Edgerton, Y.-L. Chuang, P. Macklin, S. Sanga, J. Kim, G. Tomaiuolo, W. Yang, A.D. Broom, K.-A. Do, and V. Cristini, Using Mathematical Models to Understand the Time Dependence of the Growth of Ductal Carcinoma in Situ, Canc. Res. 69 (Suppl. 2):Abstract 1165, 2009.

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  1. P. Macklin, Toward Computational Oncology: Nonlinear Simulation of Centimeter-Scale Tumor Growth in Complex, Heterogeneous Tissues, Ph.D. Dissertation, University of California, Irvine Department of Mathematics, 2007.
  2. P. Macklin, Nonlinear Simulation of Tumor Growth and Chemotherapy, M.S. Thesis, University of Minnesota School of Mathematics, 2003.
  3. P. Macklin, Analysis of an Explicit Finite Difference Scheme for a Groundwater Flow Problem, Undergraduate Honors Thesis, University of Nebraska-Lincoln Honors Program, 1999.

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Other Publications

  1. S.H. Friedman, A. Ghaffarizadeh, and P. Macklin, Simulating multi-substrate diffusive transport in 3-D tissues with BioFVM, In: NCI Handbook of Mathematical Methods in Cancer, 2015. DOI: 10.1101/035709.
  2. E.F. Juarez Rosales, A. Ghaffarizadeh, S.H. Friedman, E. Jonckheere and P. Macklin, Estimating cel cycle model parameters using systems identification, In: NCI Handbook of Mathematical Methods in Cancer, 2015. DOI: 10.1101/035766.
  3. A. Ghaffarizadeh, S.H. Friedman and P. Macklin, Agent-based simulation of large tumors in 3-D microenvironments, In: NCI Handbook of Mathematical Methods in Cancer, 2015. DOI: 10.1101/035733.

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