title: Stochastic receptor expression allows sensitive bacteria to evade phage attack. Part II: theoretical analyses.
abstract: Stochastic gene expression in bacteria can create a diverse protein distribution. Most of the current studies have focused on fluctuations around the mean, which constitutes the majority of a bacterial population. However, when the bacterial population is subject to a severe selection pressure, it is the properties of the minority cells that determine the fate of the population. The central question is whether phenotype heterogeneity, such as a spread in the expression level of a critical protein, is sufficient to account for the persistence of the bacteria under the selection. A related question is how long such persistence can last before genetic mutation becomes significant. In this work, survival statistics of a bacterial population with a diverse phage-receptor number distribution is theoretically investigated when the cells are subject to phage pressures. The calculations are compared with our experimental observations presented in Part I in this issue. The fundamental basis of our analysis is the Berg-Purcell theoretical result for the reaction rate between a phage particle and a bacterium with a discrete number of receptors, and the observation that most phage-resistant mutants isolated in laboratory cultures are defective in phage binding. It is shown that a heterogeneous bacterial population is significantly more fit compared to a homogeneous population when confronting a phage attack.
authors.affiliation: Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: E
authors.initials: E
authors.lastname: Chapman-McQuiston
authors.forename: X L
authors.initials: XL
authors.lastname: Wu
mesh_terms: D001425:Bacterial Outer Membrane Proteins / Q000378:metabolism*; D010582:Bacteriophage lambda / Q000502:physiology*; D003198:Computer Simulation; D004926:Escherichia coli / Q000502:physiology* / Q000821:virology*; D008954:Models, Biological; D015233:Models, Statistical; D018272:Porins / Q000378:metabolism*; D011991:Receptors, Virus / Q000378:metabolism*; D013269:Stochastic Processes
chemical_list: D001425:Bacterial Outer Membrane Proteins; D018272:Porins; D011991:Receptors, Virus; C016532:maltoporins