Modelling and prediction of bacterial attachment to polymers

V. C. Epa, A. L. Hook, Chien-Yi Chang, J. Yang, R. Langer, D. G. Anderson, P. Williams, M. C. Davies, M. R. Alexander, D. A. Winkler*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    43 Citations (Scopus)


    Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, "no touch" surgical procedures, and development of materials with inherent low pathogen attachment. The search for new materials is increasingly being carried out by high throughput methods. Efficient methods for extracting knowledge from these large data sets are essential. Data from a large polymer microarray exposed to three clinical pathogens is used to derive robust and predictive machine-learning models of pathogen attachment. The models can predict pathogen attachment for the polymer library quantitatively. The models also successfully predict pathogen attachment for a second-generation library, and identify polymer surface chemistries that enhance or diminish pathogen attachment.

    Original languageEnglish
    Pages (from-to)2085-2093
    Number of pages9
    JournalAdvanced Functional Materials
    Issue number14
    Publication statusPublished - 9 Apr 2014


    • high throughput
    • medical devices
    • nosocomial infections
    • pathogen attachment
    • sparse Bayesian methods
    • structure-property relationship

    ASJC Scopus subject areas

    • Biomaterials
    • Electrochemistry
    • Condensed Matter Physics
    • Electronic, Optical and Magnetic Materials


    Dive into the research topics of 'Modelling and prediction of bacterial attachment to polymers'. Together they form a unique fingerprint.

    Cite this