Linked Life Data

15714501

Source:http://linkedlifedata.com/resource/pubmed/id/15714501

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-3-3
pubmed:abstractText
A predictive model that can correlate the chemical composition of a biomaterial with the biological response of cells that are in contact with that biomaterial would represent a major advance and would facilitate the rational design of new biomaterials. As a first step toward this goal, we report here on the use of Logical Analysis of Data (LAD) to model the effect of selected polymer properties on the growth of two different cell types, rat lung fibroblasts (RLF, a transformed cell line), and normal foreskin fibroblasts (NFF, nontransformed human cells), on 112 surfaces obtained from a combinatorially designed library of polymers. LAD is a knowledge extraction methodology, based on using combinatorics, optimization, and Boolean logic. LAD was trained on a subset of 62 polymers and was then used to predict cell growth on 50 previously untested polymers. Experimental validation indicated that LAD correctly predicted the high and low cell growth polymers and found optimal ranges for polymer chemical composition, surface chemistry, and bulk properties. Particularly noteworthy is that LAD correctly identified high-performing polymer surfaces, which surpassed commercial tissue culture polystyrene as growth substratum for normal foreskin fibroblasts. Our results establish the feasibility of using computational modeling of cell growth on flat polymeric surfaces to identify promising "lead" polymers for applications that require either high or low cell growth.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1549-3296
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2005 Wiley Periodicals, Inc.
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
73
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
116-24
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
A computational approach to predicting cell growth on polymeric biomaterials.
pubmed:affiliation
Department of Chemistry and Chemical Biology, and the New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 09803, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural