A computational approach to predicting cell growth on polymeric biomaterials

Abstract

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.

Neural Network Analysis Identifies Scaffold Properties Necessary for In Vitro Chondrogenesis in Elastin-like Polypeptide Biopolymer Scaffolds

“…[68][69][70] With most techniques, however, challenges exist with the simultaneous study of multiple, diverse, and=or competing outcomes. In contrast, ANN modeling offers advantages over many deterministic techniques that have been used to predict tissue engineering processes such as matrix synthesis 71 and metabolite consumption and evolution.…”

Pattern-based clustering and attribute analysis

“…In numerous case studies ( [1,2,5,6,9,12,16,24], etc) it was noticed that the most informative pandects were those with δ * equal 2 or 3, or (rarely) 4; usually δ * ≥ 5 lead to a phenomenon resembling statistical overfitting. The values of π * had substantial variations (5-80%) in the case studies examined, being usually in the 10-50% range.…”

Combinatorial and High‐Throughput Methods in Macromolecular Materials Research and Development

“…Coupling the experimental results with computer modeling techniques allowed for the development of predictive quantitative structureproperty relationships for these materials. [116][117][118][119][120][121] An active area of research in polymer science is the use of biodegradable polymers to deliver therapeutic agents. Poly(b-amino esters) have potential as biodegradable drug delivery polymers and are easily synthesized by the conjugate addition of amines and acrylates.…”

“…[116][117][118][119] A model using three-dimensional molecular descriptors for the polymer molecular structure demonstrated good predictive capability. [171] Predictive models such as this can be used to direct future experiments and identify promising compositions that may have not been synthesized in the initial experiments.…”