Metabolism-Based Bacterial Detection in Water Using First-Principles Modeling and Predictive Machine Learning
- 1National Research Council of Canada , , , ,
Abstract
Bacterial contamination in water remains a global health concern. This study introduces a simplified and scalable approach for detecting Escherichia coli by coupling bacterial metabolism with predictive modeling. We developed a first-principle model to examine how E. coli consumes glucose as growth and metabolic rates vary over time. Detection time was primarily governed by bacterial growth rates, which exerted a substantially greater influence than variations in glucose consumption. Experimental validation on laboratory and environmental samples was performed using repurposed commercial glucose meters and portable pH sensors to monitor glucose depletion and proton production as indicators of bacterial viability. The model accurately captured these changes and fitted experimental data across varying growth rates induced by temperature differences (R2 ≥ 0.96, RMSE < 0.16 mM). Under optimal conditions, detection occurred within 3 h compared to 23 h under unfavorable conditions. Mixed cultures of Escherichia coli and Bacillus subtilis showed only a marginal effect on detection time relative to temperature. Selective detection of Escherichia coli was achieved using nutrient-specific media. Integrating metabolic data with pH, temperature, and time into machine-learning models enhanced predictive capability, with the decision tree model achieving a mean squared error of 0.0802 and identifying pH, time, and temperature as dominant predictors. Overall, this work redefines bacterial culture as a data-driven, field-ready diagnostic system, transforming simple consumer devices into quantitative biosensors for rapid, low-cost microbial monitoring in environmental and public-health applications.
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BibTeX
@article{osman2026metabolismbased,
title = {{Metabolism-Based Bacterial Detection in Water Using First-Principles Modeling and Predictive Machine Learning}},
author = {Enas Osman and Valentyna Semenchenko and Jennifer Bruce and Rajesh G. Pillai and Darren Homeniuk and Julio Valdes and Guillaume Durand and Abebaw B. Jemere and Adam Johan Bergren},
journal = {ACS Omega},
year = {2026},
month = nov,
doi = {10.1021/acsomega.6c07373},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07373/5465187/Metabolism-Based-Bacterial-Detection-in-Water},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Metabolism-Based Bacterial Detection in Water Using First-Principles Modeling and Predictive Machine Learning
AU - Enas Osman
AU - Valentyna Semenchenko
AU - Jennifer Bruce
AU - Rajesh G. Pillai
AU - Darren Homeniuk
AU - Julio Valdes
AU - Guillaume Durand
AU - Abebaw B. Jemere
AU - Adam Johan Bergren
PY - 2026
DA - 2026/11/05
JO - ACS Omega
DO - 10.1021/acsomega.6c07373
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07373/5465187/Metabolism-Based-Bacterial-Detection-in-Water
AB - Bacterial contamination in water remains a global health concern. This study introduces a simplified and scalable approach for detecting Escherichia coli by coupling bacterial metabolism with predictive modeling. We developed a first-principle model to examine how E. coli consumes glucose as growth and metabolic rates vary over time. Detection time was primarily governed by bacterial growth rates, which exerted a substantially greater influence than variations in glucose consumption. Experimental validation on laboratory and environmental samples was performed using repurposed commercial glucose meters and portable pH sensors to monitor glucose depletion and proton production as indicators of bacterial viability. The model accurately captured these changes and fitted experimental data across varying growth rates induced by temperature differences (R2 ≥ 0.96, RMSE < 0.16 mM). Under optimal conditions, detection occurred within 3 h compared to 23 h under unfavorable conditions. Mixed cultures of Escherichia coli and Bacillus subtilis showed only a marginal effect on detection time relative to temperature. Selective detection of Escherichia coli was achieved using nutrient-specific media. Integrating metabolic data with pH, temperature, and time into machine-learning models enhanced predictive capability, with the decision tree model achieving a mean squared error of 0.0802 and identifying pH, time, and temperature as dominant predictors. Overall, this work redefines bacterial culture as a data-driven, field-ready diagnostic system, transforming simple consumer devices into quantitative biosensors for rapid, low-cost microbial monitoring in environmental and public-health applications.
SN - 2470-1343
ER -