Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Mathematical modelling of bacterial quorum sensing: A review.
Bull. Math. Biol. 78, 1585-1639 (2016)
DOI Verlagsversion bestellen
Bacterial quorum sensing (QS) refers to the process of cell-to-cell bacterial communication enabled through the production and sensing of the local concentration of small molecules called autoinducers to regulate the production of gene products (e.g. enzymes or virulence factors). Through autoinducers, bacteria interact with individuals of the same species, other bacterial species, and with their host. Among QS-regulated processes mediated through autoinducers are aggregation, biofilm formation, bioluminescence, and sporulation. Autoinducers are therefore “master” regulators of bacterial lifestyles. For over 10 years, mathematical modelling of QS has sought, in parallel to experimental discoveries, to elucidate the mechanisms regulating this process. In this review, we present the progress in mathematical modelling of QS, highlighting the various theoretical approaches that have been used and discussing some of the insights that have emerged. Modelling of QS has benefited almost from the onset of the involvement of experimentalists, with many of the papers which we review, published in non-mathematical journals. This review therefore attempts to give a broad overview of the topic to the mathematical biology community, as well as the current modelling efforts and future challenges.
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Schlagwörter Antibacterial ; Autoinducers ; Bacteria ; Communication ; Mathematical Modelling ; Quorum Sensing ; Simulations; Cell-cell Communication; Gram-negative Bacteria; Pseudomonas-aeruginosa; Vibrio-fischeri; Staphylococcus-aureus; Gene-expression; Regulation Systems; Social Evolution; Biofilm Growth; Feedback Loops
ISSN (print) / ISBN 0092-8240
Zeitschrift Bulletin of Mathematical Biology
Quellenangaben Band: 78, Heft: 8, Seiten: 1585-1639
Verlagsort New York
Institut(e) Institute of Computational Biology (ICB)