1. Dynamics of cell-mediated aggregation
Cristóvão Dias
Centro de Física Teórica e Computacional, Universidade de Lisboa, Portugal
Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Portugal
Encontro CIÊNCIA 2017
Lisboa, Portugal
4/Julho/2017

2. Regenerative Medicine
Morphogenesis
Genetics 156, 1671 (2000)
Wound healing
3D models
National Geographics
Regenerative Medicine
Andrew Pelling (

3. Scaffolds 3D printing flat substrates self-assembly
flat substrates
Soft Matter 9, 2985 (2013)
self-assembly
Biomaterials 43, 23 (2015)

4. Cell-mediated self-assembled scaffolds
chitosan particles: 120 micron (diameter)
cells: micron in the bulk (diameter) and 50 micron flat on the substrate
C. A. Custódio, M. T. Cerqueira, A. P. Marques, R. L. Reis, and J. F. Mano, Biomaterials 43, 23 (2015)

5. Aggregation dynamics particle-based simulations
n sites on the surface (n = 6);
one site only adheres to one cell;
cell-site interaction is attractive;
colloid-colloid interaction is repulsive.
cell-mediated
particle-particle
bond
Cell adheres to
the particle

6. Aggregation dynamics particle-based simulations Stochastic force
Cell/colloid interaction
Cell/Cell-colloid interaction
Langevin Dynamics:
Stochastic force
drag term
inter-particle force

7. Aggregation dynamics particle-based simulations (results)
512 particles
total time = 100
Dcell/Dparticle = 10
10 samples
particle-based simulations (results)
Size of the
largest
aggregate
cell/site ratio

8. Aggregation dynamics particle-based simulations (results)
512 particles
total time = 100
Dcell/Dparticle = 10
10 samples
particle-based simulations (results)
with and with cell-cell

9. Particle-cell-particle
Aggregation dynamics
Free cell
mean-field approximation
Cell-particle
Cell adheres to
the particle
Particle-cell-particle
\dot{C}_0=-k_0C_0P \\
\dot{C}_1=+k_0C_0P-k_1C_1P \\
\dot{C}_2=+k_1C_1P
cell-mediated
particle-particle
bond

10. Aggregation dynamics mean-field approximation 512 particles
total time = 100
Dcell/Dparticle = 10
10 samples
mean-field approximation
mean field grasps the cell dynamics
\dot{C}_0=-k_0C_0P \\
\dot{C}_1=+k_0C_0P-k_1C_1P \\
\dot{C}_2=+k_1C_1P

11. Aggregation dynamics mean-field approximation k1/k0=1 k1/k0=10-1
bond probability strongly depends on the kinetics for high \Phi
\dot{C}_0=-k_0C_0P \\
\dot{C}_1=+k_0C_0P-k_1C_1P \\
\dot{C}_2=+k_1C_1P 5
k1/k0=10-1
k1/k0=10-2

12. Aggregation dynamics
lattice model (without loops)

13. Aggregation dynamics
lattice model (without loops)

14. Aggregation dynamics lattice model (without loops)
without loops all particles belong to the giant component

15. Future… Spatial correlations competing timescales aggregation dynamics
Lattice model
Molecular dynamics
Spatial correlations
competing timescales
aggregation dynamics
size distribution
Aggregates
loops
diffusion coefficient
mechanical properties
Proliferation model
Cell dynamics
adhesion
chemotaxis
division
extrusion

16. Margarida Telo da Gama
Nuno Araújo
Gonçalo Antunes
Universidade
de Aveiro
João Mano
Catarina Custódio
Financial support from the Portuguese Foundation for Science and Technology (FCT) under Contracts nos. EXCL/FIS-NAN/0083/2012.