A poroelastic mixture model of mechanobiological processes in biomass growth: theory and application to tissue engineering

Abstract In this article we propose a novel mathematical description of biomass growth that combines poroelastic theory of mixtures and cellular population models. The formulation, potentially applicable to general mechanobiological processes, is here used to study the engineered cultivation in bioreactors of articular chondrocytes, a process of Regenerative Medicine characterized by a complex interaction among spatial scales (from nanometers to centimeters), temporal scales (from seconds to weeks) and biophysical phenomena (fluid-controlled nutrient transport, delivery and consumption; mechanical deformation of a multiphase porous medium). The principal contribution of this research is the inclusion of the concept of cellular “force isotropy” as one of the main factors influencing cellular activity. In this description, the induced cytoskeletal tensional states trigger signalling transduction cascades regulating functional cell behavior. This mechanims is modeled by a parameter which estimates the influence of local force isotropy by the norm of the deviatoric part of the total stress tensor. According to the value of the estimator, isotropic mechanical conditions are assumed to be the promoting factor of extracellular matrix production whereas anisotropic conditions are assumed to promote cell proliferation. The resulting mathematical formulation is a coupled system of nonlinear partial differential equations comprising: conservation laws for mass and linear momentum of the growing biomass; advection–diffusion–reaction laws for nutrient (oxygen) transport, delivery and consumption; and kinetic laws for cellular population dynamics. To develop a reliable computational tool for the simulation of the engineered tissue growth process the nonlinear differential problem is numerically solved by: (1) temporal semidiscretization; (2) linearization via a fixed-point map; and (3) finite element spatial approximation. The biophysical accuracy of the mechanobiological model is assessed in the analysis of a simplified 1D geometrical setting. Simulation results show that: (1) isotropic/anisotropic conditions are strongly influenced by both maximum cell specific growth rate and mechanical boundary conditions enforced at the interface between the biomass construct and the interstitial fluid; (2) experimentally measured features of cultivated articular chondrocytes, such as the early proliferation phase and the delayed extracellular matrix production, are well described by the computed spatial and temporal evolutions of cellular populations.

[1]  Robert L Sah,et al.  Perfusion increases cell content and matrix synthesis in chondrocyte three-dimensional cultures. , 2002, Tissue engineering.

[2]  Andrea Tosin Multiphase modeling and qualitative analysis of the growth of tumor cords , 2008, Networks Heterog. Media.

[3]  P J Prendergast,et al.  How can cells sense the elasticity of a substrate? An analysis using a cell tensegrity model. , 2011, European cells & materials.

[4]  Stephen M. Klisch,et al.  A Theory of Volumetric Growth for Compressible Elastic Biological Materials , 2001 .

[5]  C. Chung,et al.  Hybrid cellular automaton modeling of nutrient modulated cell growth in tissue engineering constructs. , 2010, Journal of theoretical biology.

[6]  P. Causin,et al.  A multiscale approach in the computational modeling of the biophysical environment in artificial cartilage tissue regeneration , 2013, Biomechanics and modeling in mechanobiology.

[7]  C. Oomens,et al.  An integrated finite-element approach to mechanics, transport and biosynthesis in tissue engineering. , 2004, Journal of biomechanical engineering.

[8]  Francesco Agostini,et al.  A multiscale thermo-fluid computational model for a two-phase cooling system , 2014, 1404.0587.

[9]  W. G. Bickley Mathematical Theory Of Elasticity , 1946, Nature.

[10]  J. M. García-Aznar,et al.  On the Modelling of Biological Patterns with Mechanochemical Models: Insights from Analysis and Computation , 2010, Bulletin of mathematical biology.

[11]  James M. Roberts,et al.  A New Description of Cellular Quiescence , 2006, PLoS biology.

[12]  M. Raimondi,et al.  A miniaturized, optically accessible bioreactor for systematic 3D tissue engineering research , 2012, Biomedical microdevices.

[13]  D A Lauffenburger,et al.  Mathematical model for the effects of adhesion and mechanics on cell migration speed. , 1991, Biophysical journal.

[14]  Ajh Arjan Frijns,et al.  A four-component mixture theory applied to cartilaginous tissues : numerical modelling and experiments , 2000 .

[15]  T. Hughes,et al.  Streamline upwind/Petrov-Galerkin formulations for convection dominated flows with particular emphasis on the incompressible Navier-Stokes equations , 1990 .

[16]  Greg Lemon,et al.  Mathematical modelling of engineered tissue growth using a multiphase porous flow mixture theory , 2006, Journal of mathematical biology.

[17]  J. Berryman Comparison of Upscaling Methods in Poroelasticity and Its Generalizations , 2005 .

[18]  A. Abdel-azim Fundamentals of Heat and Mass Transfer , 2011 .

[19]  D. E. Contois Kinetics of bacterial growth: relationship between population density and specific growth rate of continuous cultures. , 1959, Journal of general microbiology.

[20]  A. Mauri,et al.  ELECTRO-THERMO-CHEMICAL COMPUTATIONAL MODELS FOR 3D HETEROGENEOUS SEMICONDUCTOR DEVICE SIMULATION , 2013, 1307.3096.

[21]  S. Klisch,et al.  A cartilage growth mixture model for infinitesimal strains: solutions of boundary-value problems related to in vitro growth experiments , 2005, Biomechanics and modeling in mechanobiology.

[22]  M. Raimondi,et al.  Computational prediction of strain-dependent diffusion of transcription factors through the cell nucleus , 2015, Biomechanics and modeling in mechanobiology.

[23]  G. Lemon,et al.  A validated model of GAG deposition, cell distribution, and growth of tissue engineered cartilage cultured in a rotating bioreactor , 2009, Biotechnology and bioengineering.

[24]  高等学校計算数学学報編輯委員会編 高等学校計算数学学報 = Numerical mathematics , 1979 .

[25]  Bojana Obradovic,et al.  Glycosaminoglycan deposition in engineered cartilage: Experiments and mathematical model , 2000 .

[26]  J. Webster,et al.  Analysis of Nonlinear Poro-Elastic and Poro-Visco-Elastic Models , 2016 .

[27]  Vassilios Sikavitsas,et al.  Tissue Engineering Bioreactors , 2006 .

[28]  S. Klisch,et al.  A growth mixture theory for cartilage with application to growth-related experiments on cartilage explants. , 2003, Journal of biomechanical engineering.

[29]  Margherita Cioffi,et al.  The effect of hydrodynamic shear on 3D engineered chondrocyte systems subject to direct perfusion. , 2006, Biorheology.

[30]  S. Whitaker The method of volume averaging , 1998 .

[31]  C P Chen,et al.  Enhancement of cell growth in tissue‐engineering constructs under direct perfusion: Modeling and simulation , 2007, Biotechnology and bioengineering.

[32]  G. Oster,et al.  Mechanical aspects of mesenchymal morphogenesis. , 1983, Journal of embryology and experimental morphology.

[33]  D. Wendt,et al.  The role of bioreactors in tissue engineering. , 2004, Trends in biotechnology.

[34]  M. Raimondi,et al.  Engineered cartilage constructs subject to very low regimens of interstitial perfusion. , 2008, Biorheology.

[35]  Mark Taylor,et al.  Computational modelling of cell spreading and tissue regeneration in porous scaffolds. , 2007, Biomaterials.

[36]  Shaofan Li,et al.  Soft Matter Modeling of Biological Cells , 2012 .

[37]  H M Byrne,et al.  The influence of bioreactor geometry and the mechanical environment on engineered tissues. , 2010, Journal of biomechanical engineering.

[38]  Philip K. Maini,et al.  Mathematical Models for Cell-Matrix Interactions during Dermal Wound Healing , 2002, Int. J. Bifurc. Chaos.

[39]  Paola Causin,et al.  A computational model for biomass growth simulation in tissue engineering , 2011 .

[40]  Manuela T. Raimondi,et al.  Controlling Self-Renewal and Differentiation of Stem Cells via Mechanical Cues , 2012, Journal of biomedicine & biotechnology.

[41]  R Langer,et al.  Dynamic Cell Seeding of Polymer Scaffolds for Cartilage Tissue Engineering , 1998, Biotechnology progress.

[42]  Riccardo Sacco,et al.  3D finite element modeling and simulation of industrial semiconductor devices including impact ionization , 2015 .

[43]  Pedro Moreo,et al.  Modeling mechanosensing and its effect on the migration and proliferation of adherent cells. , 2008, Acta biomaterialia.

[44]  Francesco Migliavacca,et al.  The effect of media perfusion on three-dimensional cultures of human chondrocytes: integration of experimental and computational approaches. , 2004, Biorheology.

[45]  M. Sefton,et al.  Tissue engineering. , 1998, Journal of cutaneous medicine and surgery.

[46]  J R King,et al.  Multiphase modelling of cell behaviour on artificial scaffolds: effects of nutrient depletion and spatially nonuniform porosity. , 2007, Mathematical medicine and biology : a journal of the IMA.

[47]  R D Kamm,et al.  Mechano-sensing and cell migration: a 3D model approach , 2011, Physical biology.

[48]  C. Please,et al.  A continuum model for the development of tissue-engineered cartilage around a chondrocyte. , 2009, Mathematical medicine and biology : a journal of the IMA.

[49]  Francesco Migliavacca,et al.  Micro Fluid Dynamics in Three Dimensional Engineered Cell Systems in Bioreactors. , 2006 .

[50]  Kyriacos Zygourakis,et al.  Cell population dynamics modulate the rates of tissue growth processes. , 2006, Biophysical journal.

[51]  G A Ateshian,et al.  Experimental verification and theoretical prediction of cartilage interstitial fluid pressurization at an impermeable contact interface in confined compression. , 1998, Journal of biomechanics.

[52]  Rebecca J. Shipley,et al.  Effective governing equations for poroelastic growing media , 2014 .

[53]  Ludmil T. Zikatanov,et al.  A monotone finite element scheme for convection-diffusion equations , 1999, Math. Comput..

[54]  R. Osellame,et al.  Synthetic niche substrates engineered via two‐photon laser polymerization for the expansion of human mesenchymal stromal cells , 2016, Journal of tissue engineering and regenerative medicine.

[55]  Riccardo Sacco,et al.  Three-Dimensional Simulation of Biological Ion Channels Under Mechanical, Thermal and Fluid Forces , 2015, 1509.07301.

[56]  Kyriacos Zygourakis,et al.  A 3D hybrid model for tissue growth: the interplay between cell population and mass transport dynamics. , 2009, Biophysical journal.

[57]  M. Raimondi,et al.  Bio-chemo-mechanical models for nuclear deformation in adherent eukaryotic cells , 2014, Biomechanics and Modeling in Mechanobiology.

[58]  L. Preziosi,et al.  ON THE CLOSURE OF MASS BALANCE MODELS FOR TUMOR GROWTH , 2002 .

[59]  P. Causin,et al.  A poroelastic model for the perfusion of the lamina cribrosa in the optic nerve head. , 2014, Mathematical biosciences.

[60]  D. Wendt,et al.  Computational evaluation of oxygen and shear stress distributions in 3D perfusion culture systems: macro-scale and micro-structured models. , 2008, Journal of biomechanics.

[61]  Michel Quintard,et al.  Calculation of effective diffusivities for biofilms and tissues , 2002, Biotechnology and bioengineering.

[62]  R Pietrabissa,et al.  Computational modeling of combined cell population dynamics and oxygen transport in engineered tissue subject to interstitial perfusion , 2007, Computer methods in biomechanics and biomedical engineering.

[63]  B. Fabry,et al.  Mechanotransduction: use the force(s) , 2015, BMC Biology.

[64]  Margherita Cioffi,et al.  An in silico bioreactor for simulating laboratory experiments in tissue engineering , 2008, Biomedical microdevices.

[65]  D. Bader,et al.  Biomechanical Influence of Cartilage Homeostasis in Health and Disease , 2011, Arthritis.

[66]  S. Klisch Internally Constrained Mixtures of Elastic Continua , 1999 .

[67]  G. Webb,et al.  AN IN VITRO CELL POPULATION DYNAMICS MODEL INCORPORATING CELL SIZE, QUIESCENCE, AND CONTACT INHIBITION , 2011 .

[68]  Luigi Preziosi,et al.  Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications , 2009, Journal of mathematical biology.

[69]  Lutz Tobiska,et al.  Numerical Methods for Singularly Perturbed Differential Equations , 1996 .

[70]  L. Preziosi,et al.  Modelling Solid Tumor Growth Using the Theory of Mixtures , 2001, Mathematical medicine and biology : a journal of the IMA.

[71]  Paola Causin,et al.  A multiphysics/multiscale 2D numerical simulation of scaffold-based cartilage regeneration under interstitial perfusion in a bioreactor , 2011, Biomechanics and modeling in mechanobiology.

[72]  A. Quarteroni,et al.  Numerical Approximation of Partial Differential Equations , 2008 .

[73]  D. L. Sean McElwain,et al.  A Mixture Theory for the Genesis of Residual Stresses in Growing Tissues I: A General Formulation , 2005, SIAM J. Appl. Math..

[74]  M. Biot General Theory of Three‐Dimensional Consolidation , 1941 .

[75]  C. Archer,et al.  Differences in Repair Responses Between Immature and Mature Cartilage , 2001, Clinical orthopaedics and related research.

[76]  G. Vunjak‐Novakovic,et al.  Frontiers in tissue engineering. In vitro modulation of chondrogenesis. , 1999, Clinical orthopaedics and related research.

[77]  Jean E. Roberts,et al.  Mixed and hybrid finite element methods , 1987 .

[78]  Cwj Cees Oomens,et al.  Modeling the development of tissue engineered cartilage , 2001 .

[79]  G. Mercer,et al.  Flow and deformation in poroelasticity-I unusual exact solutions , 1999 .