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Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells
Author(s) -
Konstantinos Koutsoumanis,
Αλεξάνδρα Λιανού
Publication year - 2013
Publication title -
applied and environmental microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.552
H-Index - 324
eISSN - 1070-6291
pISSN - 0099-2240
DOI - 10.1128/aem.03629-12
Subject(s) - dynamics (music) , colonialism , biology , evolutionary biology , geography , sociology , pedagogy , archaeology
Conventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells of Salmonella enterica serotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (λ) and maximum specific growth rate (μmax) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N(0)). For bacterial populations with N(0) of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.

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