Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T19:51:32.026Z Has data issue: false hasContentIssue false
  • English
  • Français

Scaffold Architecture and Properties for Osteoblasts Cell Culture: An Optimization Model and Application by Genetic Algorithm

Published online by Cambridge University Press:  24 February 2015

Maraolina Domínguez-Díaz  and
Marco Antonio Cruz-Chavez
Maraolina Domínguez-Díaz*
Affiliation:
Centro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos, 62209, MEXICO.
Marco Antonio Cruz-Chavez
Affiliation:
Centro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos, 62209, MEXICO.
*
*To whom correspondence should be addressed: mdominguez@uaem.mx
Get access

Abstract

In the developing of scaffolds for cell culture, a large number of architectures with different combinations of properties should be tested to determine the best. This can be costly in time, money and materials. In this paper we have proposed an optimization model that aims to maximize the growth of osteoblasts on polymeric scaffolds by regulating their properties and architecture. Based on the optimization model it was implemented a genetic algorithm to calculate the architecture and properties of the scaffolds. The fiber diameter, pore diameter, porosity, Young's modulus and contact angle of the scaffolds were calculated through four electrospinning parameters: voltage (kV), concentration (% w/v), flow rate (ml/h) and distance (cm). A fitness value was assigned to each scaffold and the highest one was chosen as the best condition for osteoblast growth. The preliminary results obtained by the Genetic Algorithm were consistent with the tendencies reported experimentally in other studies. Also, the methodology established here can be easily adapted to different types of polymers and cells. Finally, the optimization model can be applied not only by means of heuristic method, like a Genetic Algorithm, but also by exact methods.

Keywords

polymerfibertissue
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1753: Symposium NN – Mathematical and Computational Aspects of Materials Science , 2015 , mrsf14-1753-nn03-10
Copyright
Copyright © Materials Research Society 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Greiner, A.M., Ritchter, B., Bastmeyer, M.. Macromol Biosci 12, 1301 (2012).CrossRefGoogle Scholar
Ma, P.X., Elisseeff, J., in Scaffolding in Tissue Engineering, (CRC Press Taylor & Francis Group, 2006), p. 6.Google Scholar
Karp, J.M., Dalton, P.D., Schoichet, M.S.. MRS Bulletin 28, 301 (2003). doi:10.1557/mrs2003.85.CrossRefGoogle Scholar
Korte, B., Vygen, J., in Combinatorial Optimization Theory and Algorithms, (Springer 2000).Google Scholar
Meredith, J.C., Karim, A., Amis, E.J.. MRS Bulletin 27(4), 330 (2002). doi:10.1557/mrs2002.101.CrossRefGoogle Scholar
Broderick, S., Suh, C., Nowers, J., Vogel, B., Mallapragada, S., Narasimhan, B., Rajan, K.. JOM 60(3), 56 (2008). doi 10.1007/s11837-008-0035-x CrossRefGoogle Scholar
Juárez-Chávez, J.Y., Cruz-Chávez, M.A., Serna-Barquera, S.A., Campillo-Illanes, B., Peralta-Abarca, J., Martínez-Bahena, B., Moreno-Bernal, P.. Electronics, Robotics and Automotive Mechanics Conference, CERMA2012, IEEE-Computer Society México, (2012). doi 10.1109/CERMA.2012.31 Google Scholar
Ranucci, C.S., Kumar, A., Batra, S.P., Moghe, P.V.. Biomaterials 21, 783 (2000).CrossRefGoogle Scholar
Chang, H.-I., Wang, Y., in Regenerative Medicine and Tissue Engineering - Cells and Biomaterials, (InTech, 2011), p. 569. doi 10.5772/837 Google Scholar
Irwin, E.F., Saha, K., Rosenbluth, M., Gamble, L.J., Castner, D.G., Healy, K.E.. J Biomater Sci Polymer Edn 19(10), 1363 (2008).CrossRefGoogle Scholar
Chatterjee, K., Lin-Gibson, S., Wallace, W.E., Parekh, S.H., Lee, Y.J., Cicerone, M.T., Young, M.F., Simon, C.G. Jr. Biomaterials 31, 5051 (2010).CrossRefGoogle Scholar
Takai, E., Costa, K.D., Shaheen, A-, Hung, C.T., Guo, X.E.. Ann Biomed Eng 33(7), 963 (2005).CrossRefGoogle Scholar
Xu, L.-C., Siedlecki, C.A.. Biomaterials 28, 3273 (2007).CrossRefGoogle Scholar
Murphy, C.M., Haugh, M.G., O’Brien, F.J.. Biomaterials 31, 461 (2010).CrossRefGoogle Scholar
Karageorgiou, V., Kaplan, D.. Biomaterials 26, 5474 (2005).CrossRefGoogle Scholar
Wanasekara, N., Chen, M., Chalivendra, V., Bhowmick, S., in MEMS and Nanotechnology, Volume 2, (Springer, 2011) p. 156–162.Google Scholar
Amor, H.B., in Intelligent Exploration for Genetic Algorithms, (VDM Verlag Müller, 2008), p. 13–22.Google Scholar
Sivanandam, S.N., Deepa, S.N., in Introduction to Genetic Algorithms, (Springer-Verlag Berlin Heidelbeg, 2008).Google Scholar
Cramariuc, B., Cramariuc, R., Scarlet, R., Manea, L.R., Lupu, I.G., Cramariuc, O.. Journal of Electrostatics 71(3), 189 (2013).CrossRefGoogle Scholar
Domínguez-Díaz, M., Meneses-Acosta, A., Romo-Uribe, A., Peña, C., Segura, D., Espin, G.. Eur Polym J 63, 101(2015).CrossRefGoogle Scholar
Eichhorn, S.J., Sampson, W.W.. J R Soc Interface 2, 309 (2005).CrossRefGoogle Scholar
Eichhorn, S.J., Sampson, W.W.. J R Soc Interface 7, 641 (2010).CrossRefGoogle Scholar
Hollister, S.J., Lin, C.Y.. Comput Methods Appl Mech Engrg 196, 2991 (2007).CrossRefGoogle Scholar
Choi, W., Tuteja, A., Mabry, J.M., Cohen, R.E., McKinley, G.H.. J Colloid Interface Science 339, 208 (2009).CrossRefGoogle Scholar