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

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.

Correlation between the spatial distribution and colony size was common for monogenetic bacteria in laboratory conditions

“…E. coli colonies growing on agar media can suitably mimic the ecosystem of microbes living on solid surfaces. Our previous studies experimentally and theoretically demonstrated the coordination of genome reduction and population growth in liquid media [ 21 , 22 ], as well as changes in growth due to nutritional conditions [ 22 , 23 ]. The reported E. coli strains and the corresponding media were adopted in the present study to investigate whether and how genome reduction and nutritional decline caused any changes in colony size variation and the rule of spatial distribution.…”

Degrading characteristics of oil‐degrading bacteria and its study of petroleum hydrocarbon metabolites

“…[ 20 ] However, this growth curve is based on the theoretical fitting of the model, and specific growth conditions will lead to different growth curves. [ 21 ] The experiment conducted revealed that the optical density value of the bacterial suspension dropped rapidly at the beginning of the culture. This was mainly because the bacterial liquid was a mixture of sludge and microorganisms at the beginning of the culture, with many impurities and excessive interference, and the microorganisms were partially dead in the process of adapting to the environment.…”

Global coordination of the mutation and growth rates across the genetic and nutritional variety in Escherichia coli

“…Alternatively, whether the medium and the genotype could be clustered according to the mutation and growth rates were investigated by machine learning ( Jordan and Mitchell, 2015 ), which was beneficial for discovering the dataset with unclear mechanisms relating to metabolism ( Cuperlovic-Culf, 2018 ; Kim et al, 2020 ), genetics ( Libbrecht and Noble, 2015 ; Schrider and Kern, 2018 ), evolution ( Wang et al, 2018 ) and population dynamics ( Ashino et al, 2019 ; Cao et al, 2020 ; Gilpin et al, 2020 ). Here, the support vector machine (SVM) was applied to classify the three media (i.e., LB, MAA, and M63) and the four genotypes (i.e., reduced genome, mutator, genome reduced mutator, and wild type).…”