Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds.
Abstract
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.
Type
Journal articleSubject
AnimalsCartilage
Chondrogenesis
Elastin
Humans
Hydrogels
Neural Networks (Computer)
Tissue Engineering
Permalink
https://hdl.handle.net/10161/3372Published Version (Please cite this version)
10.1089/ten.tea.2009.0134Publication Info
Nettles, Dana L; Haider, Mansoor A; Chilkoti, Ashutosh; & 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.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Ashutosh Chilkoti
Alan L. Kaganov Distinguished 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
Lori A. Setton
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
Alphabetical list of authors with Scholars@Duke profiles.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info