Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping.

Yang-Yang Cao,Bei-Wen Ying,Tetsuya Yomo

doi:10.3390/microorganisms8030331

Yang-Yang Cao, Bei-Wen Ying + Show 1 more

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https://doi.org/10.3390/microorganisms8030331

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Abstract

Bacterial growth curves, representing population dynamics, are still poorly understood. The growth curves are commonly analyzed by model-based theoretical fitting, which is limited to typical S-shape fittings and does not elucidate the dynamics in their entirety. Thus, whether a certain growth condition results in any particular pattern of growth curve remains unclear. To address this question, up-to-date data mining techniques were applied to bacterial growth analysis for the first time. Dynamic time warping (DTW) and derivative DTW (DDTW) were used to compare the similarity among 1015 growth curves of 28 Escherichia coli strains growing in three different media. In the similarity evaluation, agglomerative hierarchical clustering, assessed with four statistic benchmarks, successfully categorized the growth curves into three clusters, roughly corresponding to the three media. Furthermore, a simple benchmark was newly proposed, providing a highly improved accuracy (~99%) in clustering the growth curves corresponding to the growth media. The biologically reasonable categorization of growth curves suggested that DTW and DDTW are applicable for bacterial growth analysis. The bottom-up clustering results indicate that the growth media determine some specific patterns of population dynamics, regardless of genomic variation, and thus have a higher priority of shaping the growth curves than the genomes do.

Highlights

Bacterial population dynamics are known to be affected by growth conditions [1,2,3,4,5,6]; whether and how population dynamics are linked to growth conditions remain unclear
Since the growth curve of a bacterial population is usually expressed indirectly by measuring the optical turbidity [29,30], the most common method for growth assays, the growth data used as the time series data in the following analyses were the temporal changes in optical density (OD) detected at a wavelength of 600 nm
The temporal changes in the OD600 of E. coli cells growing in LB, M63, and MAA media were recorded as growth curves (Figure 1A)

Summary

Introduction

Bacterial population dynamics are known to be affected by growth conditions [1,2,3,4,5,6]; whether and how population dynamics are linked to growth conditions remain unclear. Growth curves have been studied to a large extent due to the simplicity and generality of their common features in a closed habitat as S-shaped curves [7] that are divided into four stages, i.e., the lag, exponential growth, stationary phase, and the death phase [2,8]. Such typical growth curves are often analyzed with various sigmoidal functions, e.g., Logistic, Gompertz, etc. Model-based growth analyses were unsuitable to create a linkage between the growth dynamics and the growth condition

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