Search

Menu
Search
in Ecology

Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves

56 Views


Abstract

Bacterial growth dynamics, typically represented by growth curves, are fundamental yet complex features of living populations. Traditional analyses focusing on specific parameters often overlook the full temporal patterns of growth. Here, we systematically investigated how genomic and environmental factors shape bacterial growth dynamics by analyzing 870 growth curves from five Escherichia coli strains with varying genome sizes cultured in 29 chemically defined media. Using dynamic time warping, clustering, and gradient boosting decision trees, we found that environmental components, especially glucose, primarily determine overall growth curve patterns, while genome size governs detailed growth parameters such as lag time, growth rate, and carrying capacity. Notably, finer clustering revealed increased genomic influence and decreased environmental impact, suggesting a hierarchical interaction where the environment modulates broad growth behavior and the genome fine-tunes specific growth responses. These findings provide insights into the coordinated roles of genome and environment in bacterial population dynamics, advancing our understanding of microbial growth regulation.

Similar content being viewed by others

Population Dynamics of Escherichia coli Growing under Chemically Defined Media

Article
Open access
11 June 2025

Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth

Article
Open access
24 December 2024

Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints

Article
Open access
23 September 2025

Data availability

All data generated or analyzed in this study are available within the paper and its Supplementary Information.

Code availability

The codes are available at https://github.com/g2kajun-dev/growth-curve-analysis.

References

  1. Klumpp, S. & Hwa, T. Bacterial growth: Global effects on gene expression, growth feedback and proteome partition. Curr. Opin. Biotechnol. 28, 96–102. https://doi.org/10.1016/j.copbio.2014.01.001 (2014).

    Google Scholar 

  2. Scott, M. & Hwa, T. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22(4), 559–565. https://doi.org/10.1016/j.copbio.2011.04.014 (2011).

    Google Scholar 

  3. Ferenci, T. Growth of bacterial cultures’ 50 years on: Towards an uncertainty principle instead of constants in bacterial growth kinetics. Res. Microbiol. 150(7), 431–438. https://doi.org/10.1016/S0923-2508(99)00114-X (1999).

    Google Scholar 

  4. Maier, R. M. Bacterial growth. In: Maier RM, Pepper IL, Gerba CP, editors. Environmental Microbiology 37–54 (2009).

  5. Ughy, B. et al. Reconsidering dogmas about the growth of bacterial populations. Cells 12(10), 1430. https://doi.org/10.3390/cells12101430 (2023).

    Google Scholar 

  6. Winsor, C. P. The Gompertz curve as a growth curve. Proc. Natl. Acad. Sci. U.S.A. 18(1), 1–8. https://doi.org/10.1073/pnas.18.1.1 (1932).

    Google Scholar 

  7. Zwietering, M. H., Jongenburger, I., Rombouts, F. M. & van ‘t Riet, K. Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56(6), 1875–1881. https://doi.org/10.1128/aem.56.6.1875-1881.1990 (1990).

    Google Scholar 

  8. Tsuchiya, K. et al. A decay effect of the growth rate associated with genome reduction in Escherichia coli. BMC Microbiol. 18(1), 101. https://doi.org/10.1186/s12866-018-1242-4 (2018).

    Google Scholar 

  9. Verissimo, A., Paixao, L., Neves, A. R. & Vinga, S. BGFit: Management and automated fitting of biological growth curves. BMC Bioinform. 14, 283. https://doi.org/10.1186/1471-2105-14-283 (2013).

    Google Scholar 

  10. Navarro-Perez, M. L., Fernandez-Calderon, M. C. & Vadillo-Rodriguez, V. Decomposition of growth curves into growth rate and acceleration: A novel procedure to monitor bacterial growth and the time-dependent effect of antimicrobials. Appl. Environ. Microbiol. 88(3), e0184921. https://doi.org/10.1128/AEM.01849-21 (2022).

    Google Scholar 

  11. Sprouffske, K. & Wagner, A. Growthcurver: An R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinform. 17, 172. https://doi.org/10.1186/s12859-016-1016-7 (2016).

    Google Scholar 

  12. Towbin, B. D. et al. Optimality and sub-optimality in a bacterial growth law. Nat. Commun. 8, 14123. https://doi.org/10.1038/ncomms14123 (2017).

    Google Scholar 

  13. Vadia, S. & Levin, P. A. Growth rate and cell size: A re-examination of the growth law. Curr. Opin. Microbiol. 24, 96–103. https://doi.org/10.1016/j.mib.2015.01.011 (2015).

    Google Scholar 

  14. Kurokawa, M., Seno, S., Matsuda, H. & Ying, B. W. Correlation between genome reduction and bacterial growth. DNA Res. Int. J. Rapid Publ. Rep. Genes Genomes. 23(6), 517–525. https://doi.org/10.1093/dnares/dsw035 (2016).

    Google Scholar 

  15. Aida, H., Hashizume, T., Ashino, K. & Ying, B. W. Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity. Elife 11, e76846. https://doi.org/10.7554/eLife.76846 (2022).

  16. Peleg, M. & Corradini, M. G. Microbial growth curves: What the models tell us and what they cannot. Crit. Rev. Food Sci. Nutr. 51(10), 917–945. https://doi.org/10.1080/10408398.2011.570463 (2011).

    Google Scholar 

  17. Huang, L. Optimization of a new mathematical model for bacterial growth. Food Control. 32, 283–288. https://doi.org/10.1016/j.foodcont.2012.11.019 (2013).

    Google Scholar 

  18. Sant’Ana, A. S., Franco, B. D. & Schaffner, D. W. Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenes in ready-to-eat lettuce. Food Microbiol. 30(1), 267–273. https://doi.org/10.1016/j.fm.2011.11.003 (2012).

    Google Scholar 

  19. Koseki, S. & Nonaka, J. Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration. Appl. Environ. Microbiol. 78(17), 6103–6112. https://doi.org/10.1128/AEM.01245-12 (2012).

    Google Scholar 

  20. Kremling, A., Geiselmann, J., Ropers, D. & de Jong, H. An ensemble of mathematical models showing diauxic growth behaviour. BMC Syst. Biol. 12(1), 82. https://doi.org/10.1186/s12918-018-0604-8 (2018).

    Google Scholar 

  21. Tonner, P. D. et al. A bayesian non-parametric mixed-effects model of microbial growth curves. PLoS Comput. Biol. 16(10), e1008366. https://doi.org/10.1371/journal.pcbi.1008366 (2020).

    Google Scholar 

  22. Loomis, W. F. & Magasanik, B. Glucose-Lactose Diauxie in Escherichia coli. J. Bacteriol. 93(4), 1397–1401. https://doi.org/10.1128/jb.93.4.1397-1401.1967 (1967).

    Google Scholar 

  23. Wong, P., Gladney, S. & Keasling, J. D. Mathematical model of thelacoperon: inducer exclusion, catabolite repression, and diauxic growth on glucose and lactose. Biotechnol. Prog. 13, 132–143. https://doi.org/10.1021/bp970003 (1997).

    Google Scholar 

  24. Russell, J. B. & Cook, G. M. Energetics of bacterial growth: Balance of anabolic and catabolic reactions. Microbiol. Rev. 59(1), 48–62 (1995).

    Google Scholar 

  25. Chu, D. & Barnes, D. J. The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate. Sci. Rep. 6(1), 25191. https://doi.org/10.1038/srep25191 (2016).

    Google Scholar 

  26. Salvy, P. & Hatzimanikatis, V. Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism. Proc. Natl. Acad. Sci. 118(8), e2013836118. https://doi.org/10.1073/pnas.2013836118 (2021).

    Google Scholar 

  27. Aida, H. & Ying, B-W. Population dynamics of Escherichia coli growing under chemically defined media. Sci. Data 12, 984 https://doi.org/10.1038/s41597-025-05356-3 (2025).

  28. Hilau, S., Katz, S., Wasserman, T., Hershberg, R. & Savir, Y. Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains. PLoS Comput. Biol. 18(10), e1010565. https://doi.org/10.1371/journal.pcbi.1010565 (2022).

    Google Scholar 

  29. Ashino, K., Sugano, K., Amagasa, T. & Ying, B. W. Predicting the decision making chemicals used for bacterial growth. Sci. Rep. 9(1), 7251. https://doi.org/10.1038/s41598-019-43587-8 (2019).

    Google Scholar 

  30. Senin, P. Dynamic time warping algorithm review. Inf. Comput. Sci. Dep. Univ. Hawaii Manoa Honolulu, USA 855(1–23), 40 (2008).

  31. Salvador, S. & Chan, P. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11, 561–580. https://doi.org/10.3233/IDA-2007-11508 (2007).

    Google Scholar 

  32. Sakoe, H. & Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49. https://doi.org/10.1109/TASSP.1978.1163055 (1978).

    Google Scholar 

  33. Cao, Y. Y., Yomo, T. & Ying, B. W. Clustering of bacterial growth dynamics in response to growth media by dynamic time warping. Microorganisms 8(3), 331. https://doi.org/10.3390/microorganisms8030331 (2020).

    Google Scholar 

  34. Aida, H. & Ying, B-W. Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth. Commun. Biology. 7(1), 1691. https://doi.org/10.1038/s42003-024-07347-3 (2024).

  35. Matsui, Y., Nagai, M. & Ying, B. W. Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions. Front. Microbiol. 14, 1145673. https://doi.org/10.3389/fmicb.2023.1145673 (2023).

    Google Scholar 

  36. Lundberg, S. M. & Lee, S-I. A unified approach to interpreting model predictions. https://doi.org/10.48550/arXiv.1705.07874 (2017).

  37. Fraebel, D. T. et al. Environment determines evolutionary trajectory in a constrained phenotypic space. Elife 6, e24669. https://doi.org/10.7554/eLife.24669 (2017).

  38. Nagai, M., Kurokawa, M. & Ying, B. W. The highly conserved chromosomal periodicity of transcriptomes and the correlation of its amplitude with the growth rate in Escherichia coli. DNA Research: Int. J. Rapid Publication Rep. Genes Genomes. 27(3), dsaa018. https://doi.org/10.1093/dnares/dsaa018 (2020).

  39. Liu, L., Kurokawa, M., Nagai, M., Seno, S. & Ying, B. W. Correlated chromosomal periodicities according to the growth rate and gene expression. Sci. Rep. 10(1), 15531. https://doi.org/10.1038/s41598-020-72389-6 (2020).

    Google Scholar 

  40. Long, A. M., Hou, S., Ignacio-Espinoza, J. C. & Fuhrman, J. A. Benchmarking microbial growth rate predictions from metagenomes. ISME J. 15(1), 183–195. https://doi.org/10.1038/s41396-020-00773-1 (2021).

    Google Scholar 

  41. Wytock, T. P. & Motter, A. E. Predicting growth rate from gene expression. Proc. Natl. Acad. Sci. U.S.A. 116(2), 367–372. https://doi.org/10.1073/pnas.1808080116 (2019).

    Google Scholar 

  42. Wytock, T. P. et al. Extreme antagonism arising from gene-environment interactions. Biophys. J. 119(10), 2074–2086. https://doi.org/10.1016/j.bpj.2020.09.038 (2020).

    Google Scholar 

  43. Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11(1), 754. https://doi.org/10.1038/s41467-020-14570-z (2020).

  44. Bank, C., Epistasis and adaptation on fitness landscapes. Annu. Rev. Ecol. Evol. Syst. 53, 457–479. https://doi.org/10.1146/annurev-ecolsys-102320-112153 (2022).

  45. Cvijovic, I., Nguyen Ba, A. N. & Desai, M. M. Experimental studies of evolutionary dynamics in microbes. Trends Genet. 34(9), 693–703. https://doi.org/10.1016/j.tig.2018.06.004 (2018).

    Google Scholar 

  46. DelaFuente, J., Diaz-Colunga, J., Sanchez, A. & San Millan, A. Global epistasis in plasmid-mediated antimicrobial resistance. Mol. Syst. Biol. 20(4), 311–320. https://doi.org/10.1038/s44320-024-00012-1 (2024).

    Google Scholar 

  47. Ying, B. W., Seno, S., Kaneko, F., Matsuda, H. & Yomo, T. Multilevel comparative analysis of the contributions of genome reduction and heat shock to the Escherichia coli transcriptome. BMC Genom. 14, 25. https://doi.org/10.1186/1471-2164-14-25 (2013).

    Google Scholar 

  48. Fink, J. W. & Manhart, M. How do microbes grow in nature? The role of population dynamics in microbial ecology and evolution. Curr. Opin. Syst. Biol. 36, 100470. https://doi.org/10.1016/j.coisb.2023.100470 (2023).

  49. Borer, B. & Or, D. Spatiotemporal metabolic modeling of bacterial life in complex habitats. Curr. Opin. Biotechnol. 67, 65–71. https://doi.org/10.1016/j.copbio.2021.01.004 (2021).

    Google Scholar 

  50. Pentney, W. & Meilă, M. Spectral clustering of biological sequence data. In: Proceedings of the AAAI Conference on Artificial Intelligence, 20 845–850 (2005).

  51. Löffler, M., Zhang, A. Y. & Zhou, H. H. Optimality of spectral clustering in the Gaussian mixture model. Ann. Stat. 49(5), 2506–2530. https://doi.org/10.1214/20-aos2044 (2021).

  52. Mahmood, H., Mehmood, T. & Al-Essa, L. A. Optimizing clustering algorithms for anti-microbial evaluation data: A majority score-based evaluation of K-means, Gaussian mixture model, and multivariate T-distribution mixtures. IEEE Access. ;11, 79793–79800. https://doi.org/10.1109/access.2023.3288344 (2023).

  53. Sharma, A. et al. Hierarchical maximum likelihood clustering approach. IEEE Trans. Biomed. Eng. 64(1), 112–122. https://doi.org/10.1109/TBME.2016.2542212 (2017).

    Google Scholar 

  54. Warren Liao, T. Clustering of time series data: A survey. Pattern Recogn. 38, 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025 (2005).

    Google Scholar 

  55. De Souto, M. C., Costa, I. G., De Araujo, D. S., Ludermir, T. B. & Schliep, A. Clustering cancer gene expression data: A comparative study. BMC Bioinform. 9(1), 497. https://doi.org/10.1186/1471-2105-9-497 (2008).

    Google Scholar 

  56. Ashari, I. F., Dwi Nugroho, E., Baraku, R., Novri Yanda, I. & Liwardana, R. Analysis of elbow, silhouette, Davies-Bouldin, Calinski-Harabasz, and rand-index evaluation on K-means algorithm for classifying flood-affected areas in Jakarta. J. Appl. Inf. Comput. 7(1), 89–97. https://doi.org/10.30871/jaic.v7i1.4947 (2023).

    Google Scholar 

  57. Li, J., Hassan, D., Brewer, S. & Sitzenfrei, R. Is clustering time-series water depth useful? An exploratory study for flooding detection in urban drainage systems. Water 12(9), 2433. https://doi.org/10.3390/w12092433 (2020).

    Google Scholar 

  58. Suarez-Alvarez, M. M., Pham, D-T., Prostov, M. Y. & Prostov, Y. I. Statistical approach to normalization of feature vectors and clustering of mixed datasets. Proc. R. Soc. A Math. Phys. Eng. Sci. 468(2145), 2630–2651. https://doi.org/10.1098/rspa.2011.0704 (2012).

  59. Wongoutong, C. The impact of neglecting feature scaling in k-means clustering. PLoS One. 19(12), e0310839. https://doi.org/10.1371/journal.pone.0310839 (2024).

    Google Scholar 

  60. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 20060008. https://doi.org/10.1038/msb4100050 (2006).

    Google Scholar 

  61. Kato, J. & Hashimoto, M. Construction of consecutive deletions of the Escherichia coli chromosome. Mol. Syst. Biol. 3, 132. https://doi.org/10.1038/msb4100174 (2007).

    Google Scholar 

  62. Jagdish, T. & Nguyen Ba, A. N. Microbial experimental evolution in a massively multiplexed and high-throughput era. Curr. Opin. Genet. Dev. 75, 101943. https://doi.org/10.1016/j.gde.2022.101943 (2022).

    Google Scholar 

  63. Smith, T. P. et al. High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants. Nat. Microbiol. https://doi.org/10.1038/s41564-024-01626-9 (2024).

    Google Scholar 

  64. Zhang, S. & Ying, B-W. Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints. Sci. Rep. 15(1), 32634. https://doi.org/10.1038/s41598-025-17103-0 (2025).

  65. Keogh, E. J. & Pazzani, M. J. Derivative dynamic time warping. In: Proceedings of the 2001 SIAM International Conference on Data Mining 1–11 (SIAM, 2001).

  66. Jeong, Y. S., Jeong, M. K. & Omitaomu, O. A. Weighted dynamic time warping for time series classification. Pattern Recogn. 44(9), 2231–2240. https://doi.org/10.1016/j.patcog.2010.09.022 (2011).

    Google Scholar 

  67. Zhao, J. P. & Itti, L. shapeDTW: Shape dynamic time warping. Pattern Recogn. 74, 171–184. https://doi.org/10.1016/j.patcog.2017.09.020 (2018).

    Google Scholar 

  68. Mizoguchi, H., Sawano, Y., Kato, J. & Mori, H. Superpositioning of deletions promotes growth of Escherichia coli with a reduced genome. DNA Res. Int. J. Rapid Publ. Rep. Genes Genomes. 15(5), 277–284. https://doi.org/10.1093/dnares/dsn019 (2008).

    Google Scholar 

  69. Kurokawa, M., Nishimura, I. & Ying, B. W. Experimental evolution expands the breadth of adaptation to an environmental gradient correlated with genome reduction. Front. Microbiol. 13, 826894. https://doi.org/10.3389/fmicb.2022.826894 (2022).

    Google Scholar 

  70. Kurokawa, M. & Ying, B. W. Precise, high-throughput analysis of bacterial growth. J. Vis. Exp. 127, 56197. https://doi.org/10.3791/56197 (2017).

  71. Gupta, Y. M., Kirana, S. N. & Homchan, S. Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings. Biochem. Mol. Biol. Educ. 53(2), 142–146. https://doi.org/10.1002/bmb.21870 (2025).

    Google Scholar 

Download references

Acknowledgements

We thank the National BioResource Project, National Institute of Genetics (Shizuoka, Japan), for providing the E. coli strains and Issei Nishimura for his experimental assistance in acquiring the growth curves.

Funding

This work was supported by the JSPS KAKENHI grant number 25K02259 (to BWY).

Author information

Authors and Affiliations

  1. School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan

    Jiarun Gong & Bei-Wen Ying

  2. MiCS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan

    Bei-Wen Ying

Authors

  1. Jiarun Gong
    View author publications

    Search author on:PubMed Google Scholar

  2. Bei-Wen Ying
    View author publications

    Search author on:PubMed Google Scholar

Contributions

JG conducted data mining, validation, and drafted the manuscript. BWY conceived the research, experiments, and rewrote the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to
Bei-Wen Ying.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Supplementary Material 6

Supplementary Material 7

Supplementary Material 8

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Gong, J., Ying, BW. Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32144-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-025-32144-1

Keywords

  • Bacterial growth dynamics
  • Time series data mining
  • Genome-environment interaction
  • Dynamic time warping
  • Machine learning in biology

Source: Ecology - nature.com

Soil organic carbon fractions and their associated bacterial and fungal abundance in alpine ecosystems

Genes, shells, and AI: using computer vision to detect cryptic morphological divergence between genetically distinct populations of limpets

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close