Models of collective cell behaviour with crowding effects: comparing lattice-based and lattice-free approaches

Individual-based models describing the migration and proliferation of a population of cells frequently restrict the cells to a predefined lattice. An implicit assumption of this type of lattice-based model is that a proliferative population will always eventually fill the lattice. Here, we develop a new lattice-free individual-based model that incorporates cell-to-cell crowding effects. We also derive approximate mean-field descriptions for the lattice-free model in two special cases motivated by commonly used experimental set-ups. Lattice-free simulation results are compared with these mean-field descriptions and with a corresponding lattice-based model. Data from a proliferation experiment are used to estimate the parameters for the new model, including the cell proliferation rate, showing that the model fits the data well. An important aspect of the lattice-free model is that the confluent cell density is not predefined, as with lattice-based models, but an emergent model property. As a consequence of the more realistic, irregular configuration of cells in the lattice-free model, the population growth rate is much slower at high cell densities and the population cannot reach the same confluent density as an equivalent lattice-based model.

[1]  M. Chaplain,et al.  Continuous and discrete mathematical models of tumor-induced angiogenesis , 1998, Bulletin of mathematical biology.

[2]  M J Plank,et al.  A reinforced random walk model of tumour angiogenesis and anti-angiogenic strategies. , 2003, Mathematical medicine and biology : a journal of the IMA.

[3]  Matthew J Simpson,et al.  Nonlinear diffusion and exclusion processes with contact interactions. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  B. Grammaticos,et al.  A Model for Short- and Long-range Interactions of Migrating Tumour Cell , 2008, Acta biotheoretica.

[5]  James D. Murray Mathematical Biology: I. An Introduction , 2007 .

[6]  Philip K Maini,et al.  From a discrete to a continuum model of cell dynamics in one dimension. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  P. M. Lee,et al.  Random Walks and Random Environments: Volume 1: Random Walks , 1995 .

[8]  G. Barton The Mathematics of Diffusion 2nd edn , 1975 .

[9]  L. Sander,et al.  Growth patterns of microscopic brain tumors. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[11]  H. Berg Random Walks in Biology , 2018 .

[12]  K. Painter,et al.  Cellular automata and integrodifferential equation models for cell renewal in mosaic tissues , 2010, Journal of The Royal Society Interface.

[13]  B. Grammaticos,et al.  A cellular automaton model for the migration of glioma cells , 2006, Physical biology.

[14]  U. Dieckmann,et al.  POPULATION GROWTH IN SPACE AND TIME: SPATIAL LOGISTIC EQUATIONS , 2003 .

[15]  P. Strating,et al.  Brownian Dynamics Simulation of a Hard-Sphere Suspension , 1999 .

[16]  Yi Rao,et al.  Distinguishing between Directional Guidance and Motility Regulation in Neuronal Migration , 2003, The Journal of Neuroscience.

[17]  Philip K Maini,et al.  Traveling wave model to interpret a wound-healing cell migration assay for human peritoneal mesothelial cells. , 2004, Tissue engineering.

[18]  K. Painter,et al.  Volume-filling and quorum-sensing in models for chemosensitive movement , 2002 .

[19]  Matthew J Simpson,et al.  Simulating invasion with cellular automata: connecting cell-scale and population-scale properties. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  S. Jonathan Chapman,et al.  Mathematical Models of Avascular Tumor Growth , 2007, SIAM Rev..

[21]  R. Fisher THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES , 1937 .

[22]  Geoffrey W. Stevens,et al.  Cell migration and proliferation during monolayer formation and wound healing , 2009 .

[23]  Hans G. Othmer,et al.  Aggregation, Blowup, and Collapse: The ABC's of Taxis in Reinforced Random Walks , 1997, SIAM J. Appl. Math..

[24]  John Crank,et al.  The Mathematics Of Diffusion , 1956 .

[25]  P. Maini,et al.  A cellular automaton model for tumour growth in inhomogeneous environment. , 2003, Journal of theoretical biology.

[26]  Satya N Majumdar,et al.  Capture of particles undergoing discrete random walks. , 2008, The Journal of chemical physics.

[27]  Leonard M. Sander,et al.  The Role of Cell-Cell Adhesion in Wound Healing , 2006, q-bio/0610015.

[28]  Kerry A Landman,et al.  Multi-scale modeling of a wound-healing cell migration assay. , 2007, Journal of theoretical biology.

[29]  M. Plank,et al.  Lattice and non-lattice models of tumour angiogenesis , 2004, Bulletin of mathematical biology.

[30]  Matthew J Simpson,et al.  Correcting mean-field approximations for birth-death-movement processes. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  D A Lauffenburger,et al.  Analysis of the roles of microvessel endothelial cell random motility and chemotaxis in angiogenesis. , 1991, Journal of theoretical biology.

[32]  Matthew J Simpson,et al.  Migration of breast cancer cells: understanding the roles of volume exclusion and cell-to-cell adhesion. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  K. Hinsen,et al.  Dynamic computer simulation of concentrated hard sphere suspensions: I. Simulation technique and mean square displacement data , 1990 .

[34]  Thomas Callaghan,et al.  A Stochastic Model for Wound Healing , 2005, q-bio/0507035.

[35]  J. Dormand,et al.  A family of embedded Runge-Kutta formulae , 1980 .

[36]  Michael Chopp,et al.  Collective behavior of brain tumor cells: the role of hypoxia. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  J. Milbrandt,et al.  Dynamics of neural crest-derived cell migration in the embryonic mouse gut. , 2004, Developmental biology.

[38]  Matthew J Simpson,et al.  Cell proliferation drives neural crest cell invasion of the intestine. , 2007, Developmental biology.

[39]  Matthew J Simpson,et al.  Velocity-jump models with crowding effects. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Philip K Maini,et al.  Comparing a discrete and continuum model of the intestinal crypt , 2011, Physical biology.

[41]  Hans G. Othmer,et al.  The Diffusion Limit of Transport Equations Derived from Velocity-Jump Processes , 2000, SIAM J. Appl. Math..

[42]  H. Othmer,et al.  Models of dispersal in biological systems , 1988, Journal of mathematical biology.

[43]  M. Abercrombie,et al.  Contact inhibition and malignancy , 1979, Nature.

[44]  Christophe Deroulers,et al.  Modeling tumor cell migration: From microscopic to macroscopic models. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  T. Liggett,et al.  Stochastic Interacting Systems: Contact, Voter and Exclusion Processes , 1999 .

[46]  Matthew J Simpson,et al.  Corrected mean-field models for spatially dependent advection-diffusion-reaction phenomena. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Helen M. Byrne,et al.  Continuum approximations of individual-based models for epithelial monolayers. , 2010, Mathematical medicine and biology : a journal of the IMA.

[48]  Maria Bruna,et al.  Excluded-volume effects in the diffusion of hard spheres. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  Matthew J. Simpson,et al.  Cell invasion with proliferation mechanisms motivated bytime-lapse data , 2010 .

[50]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..