Paul Macklin's Math Cancer Lab Website

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Recent news

Friday, Sep 9th, 2016
Coarse-graining discrete cell cycle models: Introduction One observation that often goes underappreciated in computational biology discussions is that a computational model is often a model of a model of a model ... [read more]

Tuesday, Aug 30th, 2016
Some quick math to calculate numerical convergence rates: I find myself needing to re-derive this often enough that it’s worth jotting down for current and future students. Introduction A very common task in our ... [read more]

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Publications (in reverse chronological order)

Journal Articles

  1. 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.
  2. 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., 2016 (in press) *Corresponding author.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. J. Lowengrub, H.B. Frieboes, F. Jin, Y.-L. Chuang, X. Li, P. Macklin, S.M. Wise, and V. Cristini. Nonlinear modeling 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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/j.jcp.2005.11.016.
  17. 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/j.jcp.2004.08.010.

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Book Chapters

  1. J. Poleszczuk, P. Macklin, and H. Enderling. "Agent-based modeling of cancer stem cell driven solid tumor growth". In: K. Turksen (ed.), Stem Cell Dynamics and Heterogeneity: Methods and Protocols, Springer, 2016. (in press) (invited author: H. Enderling).
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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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Works in Review

  1. A. Ghaffarizadeh, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems. , 2016 (in submission).

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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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Theses

  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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