Abstract
The design of optimal bioreactor systems for tissue engineering applications requires a sophisticated understanding of the complexities of the bioreactor environment and the role that it plays in the formation of engineered tissues. To this end, a tissue growth model is developed to characterize the tissue growth and extracellular matrix synthesis by chondrocytes seeded and cultivated on polyglycolic acid scaffolds in a wavy-walled bioreactor for a period of 4 weeks. This model consists of four components: (1) a computational fluid dynamics (CFD) model to characterize the complex hydrodynamic environment in the bioreactor, (2) a kinetic growth model to characterize the cell growth and extracellular matrix production dynamics, (3) an artificial neural network (ANN) that empirically correlates hydrodynamic parameters with kinetic constants, and (4) a second ANN that correlates the biochemical composition of constructs with their material properties. In tandem, these components enable the prediction of the dynamics of tissue growth, as well as the final compositional and mechanical properties of engineered cartilage. The growth model methodology developed in this study serves as a tool to predict optimal bioprocessing conditions required to achieve desired tissue properties.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bueno EM, Bilgen B, Barabino GA (2009) Hydrodynamic parameters modulate biochemical, histological, and mechanical properties of engineered cartilage. Tissue Eng A 15(4):773–785
Pazzano D, Mercier KA, Moran JA, Fong SS, DoBiasio DD, Rulfs JX, Kohles SS, Bonassar LJ (2000) Comparison of chondrogenesis in static and perfused bioreactor culture. Biotechnol Prog 16:893–896
Vunjak-Novakovic G, Freed LE, Biron RJ, Langer R (1996) Effects of mixing on the composition and morphology of tissue-engineered cartilage. AIChE J 42:850–860
Vunjak-Novakovic G, Obradovic B, Martin I, Freed LE (2002) Bioreactor studies of native and tissue engineered cartilage. Biorheology 39:259–268
Freed LE, Marquis JC, Vunjak-Novakovic G, Emmanual J, Langer R (1994) Composition of cell-polymer cartilage implants. Biotechnol Bioeng 43:605–614
Wilson CG, Bonassar LJ, Kohles SS (2002) Modeling the dynamic composition of engineered cartilage. Arch Biochem Biophys 208:246–254
Kino-Oka M, Maeda Y, Yamamoto T, Sugawara K, Taya M (2005) A kinetic modeling of chondrocyte culture for manufacture of tissue-engineered cartilage. J Biosci Bioeng 99(3):197–207
Saha AK, Mazumdar J, Kohles SS (2004) Prediction of growth factor effects on engineered cartilage composition using deterministic and stochastic modeling. Ann Biomed Eng 32:871–879
Bilgen B, Uygun K, Bueno EM, Sucosky P, Barabino GA (2009) Tissue growth modeling in a wavy-walled bioreactor. Tissue Eng A 15(4):761–771
Hutmacher DW, Singh H (2008) Computational fluid dynamics for improved bioreactor design and 3D culture. Trends Biotechnol 26:166–172
Bilgen B, Barabino GA (2007) Location of scaffolds in bioreactors modulates the hydrodynamic environment experienced by engineered tissues. Biotechnol Bioeng 98:282–294
Hagan MT, Demuth HB, Beale M (1995) Neural Network Design. PWS Publishing Company, Boston, MA
Bilgen B, Sucosky P, Neitzel GP, Barabino GA (2006) Flow characterization of a wavy-walled bioreactor for cartilage tissue engineering. Biotechnol Bioeng 95:1009–1022
Bilgen B (2006) Hydrodynamic characterization of a novel bioreactor for tissue engineering. Chemical Engineering, Northeastern University, Boston, MA
Fluent Inc. (2003) Fluent 6.1 User's Guide, Lebanon, NH
Shih T-H, Liou WW, Shabbir A, Yang Z, Zhu J (1995) A new k-e Eddy-viscosity model for high reynolds number turbulent flows – model development and validation. Comput Fluids 24:227–238
Sucosky P, Osorio DF, Brown JB, Neitzel GP (2004) Fluid mechanics of a spinner-flask bioreactor. Biotechnol Bioeng 85:34–46
Sucosky P (2005) Flow characterization and modeling of cartilage development in a spinner-flask bioreactor. Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
Bueno EM, Bilgen B, Carrier RL, Barabino GA (2004) Increased rate of chondrocyte aggregation in a wavy-walled bioreactor. Biotechnol Bioeng 88:767–777
Monod J (1949) The growth of bacterial cultures. Ann Rev Microbiol 3:371–394
Bailey JE, Ollis DF (1977) Biochemical engineering fundamentals. McGraw-Hill, New York
Wassermann PD (1993) Advanced methods in neural computing. Van Nostrand Reinhold, New York
Habib T, Zhang C, Yang JY, Yang MQ, Deng Y (2008) Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition. BMC Genomics 9(Suppl 1):S16
Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques with Java implementations, 1st edn. Morgan Kaufmann, San Francisco, CA
Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2008) Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data. BMC Bioinformatics 9:203
Lee MC, Nelson SJ (2008) Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy. Artif Intell Med 43:61–74
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Bilgen, B., Barabino, G.A. (2012). Modeling of Bioreactor Hydrodynamic Environment and Its Effects on Tissue Growth. In: Liebschner, M. (eds) Computer-Aided Tissue Engineering. Methods in Molecular Biology, vol 868. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-764-4_14
Download citation
DOI: https://doi.org/10.1007/978-1-61779-764-4_14
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-61779-763-7
Online ISBN: 978-1-61779-764-4
eBook Packages: Springer Protocols