bioRxivMicrobiology
Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains
Abstract
Although closely related genetically, bacterial strains belonging to the same species show significant variability in their growth and death dynamics. However, our understanding of the underlying processes that lead to this variability is still lacking. Here, we measured the growth and death dynamics of 11 strains of E. coli originating from different hosts and developed a mathematical model that captures their growth and death dynamics. Our model considers two environmental factors that determine growth dynamics: resource utilization efficiency and density-dependent growth inhibition. Here we show that both factors are required to capture the measured dynamics. Interestingly, our model results indicate that the main process that determines the major differences between the strains is the critical density at which they slow down their growth, rather than maximal growth rate or death rate. Finally, we found that bacterial growth and death dynamics can be reduced to only two dimensions and described by death rates and density-dependent growth inhibition alone. ImportanceUnderstanding the dynamics of bacterial growth has been an area of intense study. However, these dynamics have often been characterized through the narrow prism of describing growth rates, without considering parameters that may modulate these rates. Here, we generate a model that describes bacterial growth and death dynamics, incorporating two essential, growth-modulating factors: density-dependent reductions in growth rates and resource utilization efficiency. This model allows us to demonstrate that variation in the growth curves of closely related bacterial strains can be reduced to two dimensions and explained almost entirely by variation in the cellular density at which bacteria slow down their growth, combined with their death rates.
- organism: E. coli
- assay: long-read sequencing
- research focus: cell signaling & trafficking
- approach: statistical model
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