(651a) Modeling Drug Resistance Transmission, a Quorum Sensing System, with Population Balance

We address an important problem connected with the transmission of drug resistance from drug-resistant (donor) cells to non-resistant (recipient) cells. It occurs by a process of conjugation between the two populations induced by a substance called Pheromone released by the recipient cells. Cell fetal decided by both population heterogeneity and stochastic effect shows strong resistance to antibiotic treatment. Population balance model in which the particle state varies randomly as determined by a system of stochastic differential equations shed light to understanding of the spread of infection and to the future design of drugs.

Extended Abstract:

Transfer of plasmid, pCF10, in Enterococcus faecalis is regulated by the ratio of pheromone, cCF10, to inhibitor, iCF10, through conformation change of DNA on plasmid in donors, cells harboring plasmids. Membrane protein, PrgB, helping donors attach to recipients, cells without plasmid, triggers the conjugation process and converts the recipients to donors. The increase in the population of plasmid-harboring donor cells decreases the concentration of pheromone which is only produced by recipients. Then the donors adjust inhibitor concentrations, with transcription level regulation and RNA anti-sense effect, to respond the change of pheromone concentration. A delicate concentration balance between pheromone and inhibitor controlled by cell population dominate the system behaviors.

Bistability behavior is predicted by the model built on the mechanism proposed in the literature [1][2][3][4][5] with physiologically meaningful values of parameters [6]. For certain ranges of the pheromone concentration, there are two alternative stable steady-states accessible to cells and which one they adopt depends on intracellular concentrations.

We formulated the population balance model, PBM[7]. Bimodal distribution calculated from PBM shows consistent to deterministic bistability. By applying some clinical situations related assumption, some useful information can be extracted from the model. For example, the prediction of retention time of bacterium from off state to on state gives hints for drug design about the frequency for a patient to take medicine; the simulation of the influence of human blood on the balance of inhibitor and pheromone points out quite different behaviors of bacterium when it invade into vein thus emergency treatment should be conduct when it happens.

Reference: [1] J. Nakayama et al., J. Bacteriology 176 (1994) 7405-7408. [2] T. Bae et al., Molecular Microbiology 51 (2004) 271-281. [3] B.A. Buttaro et al., J. Bacteriology 182 (2000) 4926-4933. [4] J. R. Chandler & G. M. Dunny, J. Bacteriology 189 (2007) 1399-1406. [5] B. A. Bensing & Gary Dunny, Molecular Microbiology 24 (1997) 295-308. [6] J. Tomshine & Y. Kaznessis, Biophysical Journal 91 (2006) 3196-3206. [7] D. Ramkrishna, Population Balances (2000).