Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds.
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The successful design of biomaterial scaffolds for articular cartilage tissue engineering requires an understanding of the impact of combinations of material formulation parameters on diverse and competing functional outcomes of biomaterial performance. This study sought to explore the use of a type of unsupervised artificial network, a self-organizing map, to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and 11 such outcomes (including mechanical properties, matrix accumulation, metabolite usage and production, and histological appearance) for scaffolds formed from crosslinked elastin-like polypeptide (ELP) hydrogels. The artificial neural network recognized patterns in functional outcomes and provided a set of relationships between ELP formulation parameters and measured outcomes. Mapping resulted in the best mean separation amongst neurons for mechanical properties and pointed to crosslink density as the strongest predictor of most outcomes, followed by ELP concentration. The map also grouped formulations together that simultaneously resulted in the highest values for matrix production, greatest changes in metabolite consumption or production, and highest histological scores, indicating that the network was able to recognize patterns amongst diverse measurement outcomes. These results demonstrated the utility of artificial neural network tools for recognizing relationships in systems with competing parameters, toward the goal of optimizing and accelerating the design of biomaterial scaffolds for articular cartilage tissue engineering.
Neural Networks (Computer)
Published Version (Please cite this version)10.1089/ten.tea.2009.0134
Publication InfoChilkoti, A; Haider, MA; Nettles, DL; & Setton, Lori A (2010). Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds. Tissue Eng Part A, 16(1). pp. 11-20. 10.1089/ten.tea.2009.0134. Retrieved from https://hdl.handle.net/10161/3372.
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Alan L. Kaganov Professor of Biomedical Engineering
Ashutosh Chilkoti is the Alan L. Kaganov Professor of Biomedical Engineering and Chair of the Department of Biomedical Engineering at Duke University. My research in biomolecular engineering and biointerface science focuses on the development of new molecular tools and technologies that borrow from molecular biology, protein engineering, polymer chemistry and surface science that we then exploit for the development of applications that span the range from bioseparations, plasmonic bio
Adjunct Professor of Biomedical Engineering
Research in Setton's laboratory is focused on the role of mechanical factors in the degeneration and repair of soft tissues of the musculoskeletal system, including the intervertebral disc, articular cartilage and meniscus. Work in the Laboratory is focused on engineering and evaluating materials for tissue regeneration and drug delivery. Studies combining engineering and biology are also used to determine the role of mechanical factors to promote and control healing of cartilaginous tissues. Re
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