Special Issues

An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix

  • The basement membrane (BM) and extracellular matrix (ECM) play critical roles in developmental and cancer biology, and are of great interest in biomathematics. We introduce a model of mechanical cell-BM-ECM interactions that extends current (visco)elastic models (e.g. [8,16]), and connects to recent agent-based cell models (e.g. [2,3,20,26]). We model the BM as a linked series of Hookean springs, each with time-varying length, thickness, and spring constant. Each BM spring node exchanges adhesive and repulsive forces with the cell agents using potential functions. We model elastic BM-ECM interactions with analogous ECM springs. We introduce a new model of plastic BM and ECM reorganization in response to prolonged strains, and new constitutive relations that incorporate molecular-scale effects of plasticity into the spring constants. We find that varying the balance of BM and ECM elasticity alters the node spacing along cell boundaries, yielding a nonuniform BM thickness. Uneven node spacing generates stresses that are relieved by plasticity over long times. We find that elasto-viscoplastic cell shape response is critical to relieving uneven stresses in the BM. Our modeling advances and results highlight the importance of rigorously modeling of cell-BM-ECM interactions in clinically important conditions with significant membrane deformations and time-varying membrane properties, such as aneurysms and progression from in situ to invasive carcinoma.

    Citation: Gianluca D'Antonio, Paul Macklin, Luigi Preziosi. An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix[J]. Mathematical Biosciences and Engineering, 2013, 10(1): 75-101. doi: 10.3934/mbe.2013.10.75

    Related Papers:

    [1] Hasitha N. Weerasinghe, Pamela M. Burrage, Dan V. Nicolau Jr., Kevin Burrage . Agent-based modeling for the tumor microenvironment (TME). Mathematical Biosciences and Engineering, 2024, 21(11): 7621-7647. doi: 10.3934/mbe.2024335
    [2] Yujia Xi, Liying Song, Shuang Wang, Haonan Zhou, Jieying Ren, Ran Zhang, Feifan Fu, Qian Yang, Guosheng Duan, Jingqi Wang . Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma. Mathematical Biosciences and Engineering, 2023, 20(6): 10694-10724. doi: 10.3934/mbe.2023474
    [3] Sandesh Athni Hiremath, Christina Surulescu, Somayeh Jamali, Samantha Ames, Joachim W. Deitmer, Holger M. Becker . Modeling of pH regulation in tumor cells: Direct interaction between proton-coupled lactate transporters and cancer-associated carbonicanhydrase. Mathematical Biosciences and Engineering, 2019, 16(1): 320-337. doi: 10.3934/mbe.2019016
    [4] Marco Scianna, Luigi Preziosi, Katarina Wolf . A Cellular Potts model simulating cell migration on and in matrix environments. Mathematical Biosciences and Engineering, 2013, 10(1): 235-261. doi: 10.3934/mbe.2013.10.235
    [5] Abdulhamed Alsisi, Raluca Eftimie, Dumitru Trucu . Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions. Mathematical Biosciences and Engineering, 2021, 18(5): 5252-5284. doi: 10.3934/mbe.2021267
    [6] O. E. Adebayo, S. Urcun, G. Rolin, S. P. A. Bordas, D. Trucu, R. Eftimie . Mathematical investigation of normal and abnormal wound healing dynamics: local and non-local models. Mathematical Biosciences and Engineering, 2023, 20(9): 17446-17498. doi: 10.3934/mbe.2023776
    [7] Michael Leguèbe . Cell scale modeling of electropermeabilization by periodic pulses. Mathematical Biosciences and Engineering, 2015, 12(3): 537-554. doi: 10.3934/mbe.2015.12.537
    [8] Magdalena A. Stolarska, Aravind R. Rammohan . On the significance of membrane unfolding in mechanosensitive cell spreading: Its individual and synergistic effects. Mathematical Biosciences and Engineering, 2023, 20(2): 2408-2438. doi: 10.3934/mbe.2023113
    [9] Abdulhamed Alsisi, Raluca Eftimie, Dumitru Trucu . Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix. Mathematical Biosciences and Engineering, 2022, 19(6): 6157-6185. doi: 10.3934/mbe.2022288
    [10] Kazuhisa Ichikawa . Synergistic effect of blocking cancer cell invasionrevealed by computer simulations. Mathematical Biosciences and Engineering, 2015, 12(6): 1189-1202. doi: 10.3934/mbe.2015.12.1189
  • The basement membrane (BM) and extracellular matrix (ECM) play critical roles in developmental and cancer biology, and are of great interest in biomathematics. We introduce a model of mechanical cell-BM-ECM interactions that extends current (visco)elastic models (e.g. [8,16]), and connects to recent agent-based cell models (e.g. [2,3,20,26]). We model the BM as a linked series of Hookean springs, each with time-varying length, thickness, and spring constant. Each BM spring node exchanges adhesive and repulsive forces with the cell agents using potential functions. We model elastic BM-ECM interactions with analogous ECM springs. We introduce a new model of plastic BM and ECM reorganization in response to prolonged strains, and new constitutive relations that incorporate molecular-scale effects of plasticity into the spring constants. We find that varying the balance of BM and ECM elasticity alters the node spacing along cell boundaries, yielding a nonuniform BM thickness. Uneven node spacing generates stresses that are relieved by plasticity over long times. We find that elasto-viscoplastic cell shape response is critical to relieving uneven stresses in the BM. Our modeling advances and results highlight the importance of rigorously modeling of cell-BM-ECM interactions in clinically important conditions with significant membrane deformations and time-varying membrane properties, such as aneurysms and progression from in situ to invasive carcinoma.


    [1]  Advances in Molecular and Cell Biology, 6 (1993), 183-206.
    [2]  PLoS Comput. Biol., 7 (2011), e1001045.
    [3]  FEBS J. (2012, in press).
    [4]  Chemistry and Biology, 3 (1996), 895-904.
    [5]  Cell, 103 (2000), 481-490.
    [6]  J. Theor. Biol., 231 (2004), 203-222.
    [7]  in preparation (2012).
    [8]  J. Theor. Biol., 298 (2012), 82-91.
    [9]  Math. Med. Biol., 20 (2003), 277-308.
    [10]  J. Theor. Biol., 232 (2005), 523-543.
    [11]  Phys. Biol., 6 (2009).
    [12]  Phys. Rev. E, 47 (1993), 2128-2154.
    [13]  Phys. Rev. Lett., 69 (1992), 2013-2016.
    [14]  Carcinogenesis, 25 (2004), 1543-1549.
    [15]  Cancer and Metastasis Review, 25 (2006), 35-43.
    [16]  Progress in Biophysics and Molecular Biology, 106 (2011), 353-379.
    [17]  Natural Structural Biology, 8 (2001), 573-574.
    [18]  in "Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach'' (eds. V. Cristini and J. S. Lowengrub), Cambridge University Press (2010), 8-23.
    [19]  in "Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach'' (eds. V. Cristini and J. S. Lowengrub), Cambridge University Press (2010), 88-122.
    [20]  J. Theor. Biol., 301 (2012), 122-140.
    [21]  in "Computational Biology: Issues and Applications in Oncology'' (ed. T. Pham), Springer (2009), 77-112.
    [22]  in "Multiscale Computer Modeling in Biomechanics and Biomedical Engineering'' (ed. A. Gefen), Springer (2013), in press.
    [23]  J. Theor. Biol., 263 (2010), 393-406.
    [24]  Bull. Math. Biol., 71 (2009), 1189-1227.
    [25]  J. Theor. Biol., 262 (2010), 35-47.
    [26]  Math. Comp. Model., 47 (2006), 533-545.
    [27]  J. Theor. Biol., 243 (2006), 532-541.
    [28]  Phys. Biol., 5 (2008), 015002.
    [29]  Phys. Biol., 8 (2011), 045007.
    [30]  Multiscale Model. Sim., 10 (2012), 342-382.
    [31]  CRC/Academic Press, 2012.
    [32]  Math. Biosci. Eng., (2013, in press).
    [33]  Comptes Rendus Physique, 10 (2009), 790-811.
    [34]  Carcinogenesis, 20 (1999), 749-755.
  • This article has been cited by:

    1. Paul Macklin, Shannon Mumenthaler, John Lowengrub, 2013, Chapter 150, 978-3-642-36481-5, 349, 10.1007/8415_2012_150
    2. MunJu Kim, Damon Reed, Katarzyna A. Rejniak, The formation of tight tumor clusters affects the efficacy of cell cycle inhibitors: A hybrid model study, 2014, 352, 00225193, 31, 10.1016/j.jtbi.2014.02.027
    3. Patrícia Santos-Oliveira, António Correia, Tiago Rodrigues, Teresa M Ribeiro-Rodrigues, Paulo Matafome, Juan Carlos Rodríguez-Manzaneque, Raquel Seiça, Henrique Girão, Rui D. M. Travasso, Hans Van Oosterwyck, The Force at the Tip - Modelling Tension and Proliferation in Sprouting Angiogenesis, 2015, 11, 1553-7358, e1004436, 10.1371/journal.pcbi.1004436
    4. Gianluca Ascolani, Timothy M. Skerry, Damien Lacroix, Enrico Dall’Ara, Aban Shuaib, Revealing hidden information in osteoblast’s mechanotransduction through analysis of time patterns of critical events, 2020, 21, 1471-2105, 10.1186/s12859-020-3394-0
    5. Nick Štorgel, Matej Krajnc, Polona Mrak, Jasna Štrus, Primož Ziherl, Quantitative Morphology of Epithelial Folds, 2016, 110, 00063495, 269, 10.1016/j.bpj.2015.11.024
    6. Zuzanna Szymańska, Maciej Cytowski, Elaine Mitchell, Cicely K. Macnamara, Mark A. J. Chaplain, Computational Modelling of Cancer Development and Growth: Modelling at Multiple Scales and Multiscale Modelling, 2018, 80, 0092-8240, 1366, 10.1007/s11538-017-0292-3
    7. Paul Macklin, Hermann B. Frieboes, Jessica L. Sparks, Ahmadreza Ghaffarizadeh, Samuel H. Friedman, Edwin F. Juarez, Edmond Jonckheere, Shannon M. Mumenthaler, 2016, Chapter 12, 978-3-319-42021-9, 225, 10.1007/978-3-319-42023-3_12
    8. Stefan Hoehme, Adrian Friebel, Seddik Hammad, Dirk Drasdo, Jan G. Hengstler, 2017, Chapter 22, 978-1-4939-6504-5, 319, 10.1007/978-1-4939-6506-9_22
    9. P. Van Liedekerke, A. Buttenschön, D. Drasdo, 2018, 9780128117187, 245, 10.1016/B978-0-12-811718-7.00014-9
    10. Yafei Wang, Erik Brodin, Kenichiro Nishii, Hermann B. Frieboes, Shannon M. Mumenthaler, Jessica L. Sparks, Paul Macklin, Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach, 2021, 11, 2045-2322, 10.1038/s41598-020-78780-7
    11. Waseem Asghar, Rami El Assal, Hadi Shafiee, Sharon Pitteri, Ramasamy Paulmurugan, Utkan Demirci, Engineering cancer microenvironments for in vitro 3-D tumor models, 2015, 18, 13697021, 539, 10.1016/j.mattod.2015.05.002
    12. P. Van Liedekerke, M. M. Palm, N. Jagiella, D. Drasdo, Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results, 2015, 2, 2196-4378, 401, 10.1007/s40571-015-0082-3
    13. Annelies Lejon, Bert Mortier, Giovanni Samaey, Variance-Reduced Simulation of Multiscale Tumor Growth Modeling, 2017, 15, 1540-3459, 388, 10.1137/15M1043224
    14. H. L. Rocha, R. C. Almeida, E. A. B. F. Lima, A. C. M. Resende, J. T. Oden, T. E. Yankeelov, A hybrid three-scale model of tumor growth, 2018, 28, 0218-2025, 61, 10.1142/S0218202518500021
    15. Mark A. J. Chaplain, 2020, Chapter 7, 978-3-030-32856-6, 149, 10.1007/978-3-030-32857-3_7
    16. Chin F. Ng, Hermann B. Frieboes, Model of vascular desmoplastic multispecies tumor growth, 2017, 430, 00225193, 245, 10.1016/j.jtbi.2017.05.013
    17. Mohammad Soheilypour, Mohammad R. K. Mofrad, Agent‐Based Modeling in Molecular Systems Biology, 2018, 40, 0265-9247, 1800020, 10.1002/bies.201800020
    18. Andrés A. Barrea, Matias E. Hernández, Rubén Spies, Optimal chemotherapy schedules from tumor entropy, 2017, 36, 0101-8205, 991, 10.1007/s40314-015-0275-7
    19. Kelvin K.L. Wong, Three-dimensional discrete element method for the prediction of protoplasmic seepage through membrane in a biological cell, 2017, 65, 00219290, 115, 10.1016/j.jbiomech.2017.10.023
    20. Angela M. Jarrett, Ernesto A.B.F. Lima, David A. Hormuth, Matthew T. McKenna, Xinzeng Feng, David A. Ekrut, Anna Claudia M. Resende, Amy Brock, Thomas E. Yankeelov, Mathematical models of tumor cell proliferation: A review of the literature, 2018, 18, 1473-7140, 1271, 10.1080/14737140.2018.1527689
    21. Lee-Ling Sharon Ong, Justin Dauwels, H. Harry Asada, 2013, 2D data-driven stalk cell prediction model based on tip-stalk cell interaction in angiogenesis, 978-1-4577-0216-7, 4537, 10.1109/EMBC.2013.6610556
    22. J. Tinsley Oden, Ernesto A. B. F. Lima, Regina C. Almeida, Yusheng Feng, Marissa Nichole Rylander, David Fuentes, Danial Faghihi, Mohammad M. Rahman, Matthew DeWitt, Manasa Gadde, J. Cliff Zhou, Toward Predictive Multiscale Modeling of Vascular Tumor Growth, 2016, 23, 1134-3060, 735, 10.1007/s11831-015-9156-x
    23. Bing Wang, Fu Tan, Jia Zhu, Daijun Wei, A new structure entropy of complex networks based on nonextensive statistical mechanics and similarity of nodes, 2021, 18, 1551-0018, 3718, 10.3934/mbe.2021187
    24. A. Carrasco-Mantis, T. Alarcón, J.A. Sanz-Herrera, An in silico study on the influence of extracellular matrix mechanics on vasculogenesis, 2023, 231, 01692607, 107369, 10.1016/j.cmpb.2023.107369
    25. Cicely K. Macnamara, Biomechanical modelling of cancer: Agent‐based force‐based models of solid tumours within the context of the tumour microenvironment, 2021, 1, 2689-9655, 10.1002/cso2.1018
    26. Ernesto A. B. F. Lima, Danial Faghihi, Russell Philley, Jianchen Yang, John Virostko, Caleb M. Phillips, Thomas E. Yankeelov, Stacey Finley, Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth, 2021, 17, 1553-7358, e1008845, 10.1371/journal.pcbi.1008845
    27. Adrianne L. Jenner, Munisha Smalley, David Goldman, William F. Goins, Charles S. Cobbs, Ralph B. Puchalski, E. Antonio Chiocca, Sean Lawler, Paul Macklin, Aaron Goldman, Morgan Craig, Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy, 2022, 25, 25890042, 104395, 10.1016/j.isci.2022.104395
    28. Nina Verstraete, Malvina Marku, Marcin Domagala, Hélène Arduin, Julie Bordenave, Jean-Jacques Fournié, Loïc Ysebaert, Mary Poupot, Vera Pancaldi, An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia, 2023, 26, 25890042, 106897, 10.1016/j.isci.2023.106897
    29. Heber L. Rocha, Boris Aguilar, Michael Getz, Ilya Shmulevich, Paul Macklin, A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins, 2024, 10, 2056-7189, 10.1038/s41540-024-00472-z
  • Reader Comments
  • © 2013 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4217) PDF downloads(590) Cited by(28)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog