Mapping single‐cell responses to population‐level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

A Mathematical Model of Campylobacter Dynamics Within a Broiler Flock

Globally, the bacterial genus Campylobacter is one of the leading causes of human gastroenteritis, with its primary route of infection being through poultry meat. The application of biosecurity measures is currently limited by a lack of understanding of the transmission dynamics within a flock. Our work is the first to undertake a mathematical modeling approach to Campylobacter population dynamics within a flock of broilers (chickens bred specifically for meat). A system of stochastic differential equations is used to model the routes of infection between co-housed birds. The presented model displays the strong correlation between housing density and Campylobacter prevalence, and shows how stochastic variation is the driving factor determining which strains of Campylobacter will emerge first within a flock. The model also shows how the system will rapidly select for phenotypic advantages, to quickly eliminate demographically-weaker strains. A global sensitivity analysis is performed, highlighting that the growth and death rate of other native bacterial species likely contributes the greatest to preventing flock outbreaks, presenting a promising approach to hypothesizing new methods of combatting disease transmission.

Long-term dynamics of the bacterial community in a Swedish full-scale wastewater treatment plant

The operational efficiency of activated sludge wastewater treatment plants depends to a large extent on the microbial community structure of the activated sludge. The aims of this paper are to describe the composition of the bacterial community in a Swedish full-scale activated sludge wastewater treatment plant, to describe the dynamics of the community and to elucidate possible causes for bacterial community composition changes. The bacterial community composition in the activated sludge was described using 16S rRNA gene libraries and monitored for 15 months by a terminal restriction fragment (T-RF) length polymorphism (T-RFLP) analysis of the 16S rRNA gene. Despite variable environmental conditions, a large fraction of the observed T-RFs were present at all times, making up at least 50% in all samples, possibly representing a relatively stable core fraction of the bacterial community. However, the proportions of the different T-RFs in this fraction as well as the T-RFs in the more variable fraction showed a significant variation over time and temperature. The difference in community composition between summer and winter coincided with observed differences in floc structure. These observations suggest a relationship between floc properties and bacterial community composition, although additional experiments are required to determine causality.

Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a “mean-field-like” procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.