Probing eukaryotic cell mechanics via mesoscopic simulations

Cell mechanics has proven to be important in many biological processes. Although there is a number of experimental techniques which allow us to study mechanical properties of cell, there is still a lack of understanding of the role each sub-cellular component plays during cell deformations. We present a new mesoscopic particle-based eukaryotic cell model which explicitly describes cell membrane, nucleus and cytoskeleton. We employ Dissipative Particle Dynamics (DPD) method that provides us with the unified framework for modeling of a cell and its interactions in the flow. Data from micropipette aspiration experiments were used to define model parameters. The model was validated using data from microfluidic experiments. The validated model was then applied to study the impact of the sub-cellular components on the cell viscoelastic response in micropipette aspiration and microfluidic experiments.

[1]  Chunning Ji,et al.  Large scale simulation of red blood cell aggregation in shear flows. , 2013, Journal of biomechanics.

[2]  C. Sussman,et al.  Nuclear Structure, Organization, and Oncogenesis , 2011, Journal of gastrointestinal cancer.

[3]  Roland Zengerle,et al.  Leukocyte enrichment based on a modified pinched flow fractionation approach , 2013 .

[4]  Johannes E. Schindelin,et al.  The ImageJ ecosystem: An open platform for biomedical image analysis , 2015, Molecular reproduction and development.

[5]  Roger D Kamm,et al.  Measuring molecular rupture forces between single actin filaments and actin-binding proteins , 2008, Proceedings of the National Academy of Sciences.

[6]  T. Krüger,et al.  Breakdown of deterministic lateral displacement efficiency for non-dilute suspensions: A numerical study. , 2015, Medical engineering & physics.

[7]  Rashid Bashir,et al.  Biophysical properties of human breast cancer cells measured using silicon MEMS resonators and atomic force microscopy. , 2015, Lab on a chip.

[8]  George E. Karniadakis,et al.  Inflow/Outflow Boundary Conditions for Particle-Based Blood Flow Simulations: Application to Arterial Bifurcations and Trees , 2015, PLoS Comput. Biol..

[9]  Alan Hall,et al.  The cytoskeleton and cancer , 2009, Cancer and Metastasis Reviews.

[10]  George Em Karniadakis,et al.  Single-particle hydrodynamics in DPD: A new formulation , 2008 .

[11]  George Em Karniadakis,et al.  Quantifying the rheological and hemodynamic characteristics of sickle cell anemia. , 2012, Biophysical journal.

[12]  George Em Karniadakis,et al.  Accurate coarse-grained modeling of red blood cells. , 2008, Physical review letters.

[13]  George E Karniadakis,et al.  Probing vasoocclusion phenomena in sickle cell anemia via mesoscopic simulations , 2013, Proceedings of the National Academy of Sciences.

[14]  Na Zhang,et al.  A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma , 2015, J. Comput. Phys..

[15]  G. Karniadakis,et al.  A new method to impose no-slip boundary conditions in dissipative particle dynamics , 2005 .

[16]  Massimo Bernaschi,et al.  The in-silico lab-on-a-chip: petascale and high-throughput simulations of microfluidics at cell resolution , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[17]  I. Pivkin,et al.  A polarizable coarse-grained water model for dissipative particle dynamics. , 2014, The Journal of chemical physics.

[18]  Ben Fabry,et al.  Linear and Nonlinear Rheology of Living Cells , 2011 .

[19]  O. Kim,et al.  Three-dimensional multi-scale model of deformable platelets adhesion to vessel wall in blood flow , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  Shigeo Wada,et al.  Computational studies on strain transmission from a collagen gel construct to a cell and its internal cytoskeletal filaments , 2015, Comput. Biol. Medicine.

[21]  Peter V Coveney,et al.  Deformability-based red blood cell separation in deterministic lateral displacement devices-A simulation study. , 2014, Biomicrofluidics.

[22]  Hongshen Ma,et al.  Microfluidic analysis of red blood cell deformability. , 2014, Journal of biomechanics.

[23]  He Li,et al.  Erythrocyte membrane model with explicit description of the lipid bilayer and the spectrin network. , 2014, Biophysical journal.

[24]  Subra Suresh,et al.  Biomechanics of red blood cells in human spleen and consequences for physiology and disease , 2016, Proceedings of the National Academy of Sciences.

[25]  Dino Di Carlo,et al.  Hydrodynamic stretching of single cells for large population mechanical phenotyping , 2012, Proceedings of the National Academy of Sciences.

[26]  Español,et al.  Hydrodynamics from dissipative particle dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  Nus Graduate Cell biomechanics and its applications in human disease diagnosis , 2015 .

[28]  Dmitry A. Fedosov,et al.  Multiscale Modeling of Blood Flow and Soft Matter , 2010 .

[29]  Subra Suresh,et al.  Lipid bilayer and cytoskeletal interactions in a red blood cell , 2013, Proceedings of the National Academy of Sciences.

[30]  D. Talaga,et al.  DPD Simulation of Protein Conformations: From α-Helices to β-Structures. , 2012, The journal of physical chemistry letters.

[31]  Teng Yong Ng,et al.  Simulating flow of DNA suspension using dissipative particle dynamics , 2006 .

[32]  J. Koelman,et al.  Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics , 1992 .

[33]  P. Richardson,et al.  Effect of red blood cells on platelet aggregation , 2009, IEEE Engineering in Medicine and Biology Magazine.

[34]  Reinhard Lipowsky,et al.  Dissipative particle dynamics simulations of polymersomes. , 2005, The journal of physical chemistry. B.

[35]  Toru Hyakutake,et al.  Numerical simulation of red blood cell distributions in three-dimensional microvascular bifurcations. , 2015, Microvascular research.

[36]  Roger D. Kamm,et al.  Computational Analysis of a Cross-linked Actin-like Network , 2009 .

[37]  J. Lammerding,et al.  Mechanical properties of the cell nucleus and the effect of emerin deficiency. , 2006, Biophysical journal.

[38]  Fei Liu,et al.  Analyses of the cell mechanical damage during microinjection. , 2015, Soft matter.

[39]  D. Alcorta,et al.  Cytoskeletal F-actin patterns quantitated with fluorescein isothiocyanate-phalloidin in normal and transformed cells. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[40]  H. Noguchi,et al.  Shape transitions of fluid vesicles and red blood cells in capillary flows. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[41]  G. Karniadakis,et al.  Shape Transformations of Membrane Vesicles from Amphiphilic Triblock Copolymers: A Dissipative Particle Dynamics Simulation Study , 2009 .

[42]  Mario Markus,et al.  Multipeaked probability distributions of recurrence times. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  R M Nerem,et al.  The application of a homogeneous half-space model in the analysis of endothelial cell micropipette measurements. , 1988, Journal of biomechanical engineering.

[44]  Yohsuke Imai,et al.  Modeling of hemodynamics arising from malaria infection. , 2010, Journal of biomechanics.

[45]  Aleksander S Popel,et al.  Red blood cell aggregation and dissociation in shear flows simulated by lattice Boltzmann method. , 2008, Journal of biomechanics.

[46]  Roger D. Kamm,et al.  Dynamic Mechanisms of Cell Rigidity Sensing: Insights from a Computational Model of Actomyosin Networks , 2012, PloS one.

[47]  H. Lodish Molecular Cell Biology , 1986 .

[48]  P. B. Warren,et al.  DISSIPATIVE PARTICLE DYNAMICS : BRIDGING THE GAP BETWEEN ATOMISTIC AND MESOSCOPIC SIMULATION , 1997 .

[49]  Cyrus K. Aidun,et al.  Parallel performance of a lattice-Boltzmann/finite element cellular blood flow solver on the IBM Blue Gene/P architecture , 2010, Comput. Phys. Commun..

[50]  G. Karniadakis,et al.  Controlling density fluctuations in wall-bounded dissipative particle dynamics systems. , 2006, Physical review letters.

[51]  R. Kamm,et al.  Multiscale impact of nucleotides and cations on the conformational equilibrium, elasticity and rheology of actin filaments and crosslinked networks , 2015, Biomechanics and Modeling in Mechanobiology.

[52]  Subra Suresh,et al.  A microfabricated deformability-based flow cytometer with application to malaria. , 2011, Lab on a chip.

[53]  G. Karniadakis,et al.  Combined Simulation and Experimental Study of Large Deformation of Red Blood Cells in Microfluidic Systems , 2010, Annals of Biomedical Engineering.

[54]  Alexander Alexeev,et al.  Mesoscale modelling of environmentally responsive hydrogels: emerging applications. , 2015, Chemical communications.

[55]  Craig A. Simmons,et al.  Integrative Mechanobiology: Micro- and Nano- Techniques in Cell Mechanobiology , 2015 .

[56]  D. Weitz,et al.  Elastic Behavior of Cross-Linked and Bundled Actin Networks , 2004, Science.

[57]  Ed Munro,et al.  Determinants of fluidlike behavior and effective viscosity in cross-linked actin networks. , 2012, Biophysical journal.

[58]  Thomas D Pollard,et al.  Real-time measurements of actin filament polymerization by total internal reflection fluorescence microscopy. , 2005, Biophysical journal.

[59]  Eva M. Wojcik,et al.  Follicular neoplasm of the thyroid—vanishing cytologic diagnosis? , 2007, Diagnostic cytopathology.

[60]  Milica Radisic,et al.  In situ mechanical characterization of the cell nucleus by atomic force microscopy. , 2014, ACS nano.

[61]  Françoise Brochard-Wyart,et al.  Aspiration , 2019, Differential Diagnosis of Cardiopulmonary Disease.

[62]  S. Manalis,et al.  Deformability of Tumor Cells versus Blood Cells , 2015, Scientific Reports.

[63]  K. Chiam,et al.  A three-dimensional random network model of the cytoskeleton and its role in mechanotransduction and nucleus deformation , 2012, Biomechanics and modeling in mechanobiology.

[64]  I. Pivkin,et al.  A polarizable coarse-grained protein model for dissipative particle dynamics. , 2015, Physical chemistry chemical physics : PCCP.

[65]  Sanjay Kumar,et al.  Mechanics, malignancy, and metastasis: The force journey of a tumor cell , 2009, Cancer and Metastasis Reviews.

[66]  He Li,et al.  Two-component coarse-grained molecular-dynamics model for the human erythrocyte membrane. , 2012, Biophysical journal.

[67]  I. Pivkin,et al.  Hydrodynamic effects on flow-induced polymer translocation through a microfluidic channel , 2013 .

[68]  D. Tildesley,et al.  The compression of polymer brushes under shear: the friction coefficient as a function of compression, shear rate and the properties of the solvent , 2005 .

[69]  C. Lim,et al.  Biomechanics approaches to studying human diseases. , 2007, Trends in biotechnology.

[70]  Pep Español,et al.  Dissipative Particle Dynamics , 2005 .

[71]  Stefan Schinkinger,et al.  Optical deformability as an inherent cell marker for testing malignant transformation and metastatic competence. , 2005, Biophysical journal.

[72]  Roger D. Kamm,et al.  Computational Analysis of Viscoelastic Properties of Crosslinked Actin Networks , 2009, PLoS Comput. Biol..

[73]  J. Lammerding,et al.  Nuclear mechanics in cancer. , 2014, Advances in experimental medicine and biology.

[74]  R M Nerem,et al.  Application of the micropipette technique to the measurement of cultured porcine aortic endothelial cell viscoelastic properties. , 1990, Journal of biomechanical engineering.

[75]  S. Suresh,et al.  Spectrin-level modeling of the cytoskeleton and optical tweezers stretching of the erythrocyte. , 2005, Biophysical journal.

[76]  Markus Gusenbauer,et al.  A tunable cancer cell filter using magnetic beads: cellular and fluid dynamic simulations , 2011, ArXiv.

[77]  P. Koumoutsakos,et al.  The Fluid Mechanics of Cancer and Its Therapy , 2013 .

[78]  G. I. Bell Models for the specific adhesion of cells to cells. , 1978, Science.

[79]  P. Theodoropoulos,et al.  Differential nuclear shape dynamics of invasive andnon-invasive breast cancer cells are associated with actin cytoskeleton organization and stability. , 2014, Biochemistry and cell biology = Biochimie et biologie cellulaire.

[80]  M. Dupin,et al.  Modeling the flow of dense suspensions of deformable particles in three dimensions. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[81]  Dierk Raabe,et al.  Crossover from tumbling to tank-treading-like motion in dense simulated suspensions of red blood cells. , 2013, Soft matter.