Volume 8 Number 1 2025
Environmental Drivers of Bacterial Growth with Predictive Computational Models for Health and Industry: A Systematic Review
Prachi Gurudiwan 1*, Ragini Patel 1, Urvashi Jain 1
Microbial Bioactives 8 (1) 1-10 https://doi.org/10.25163/microbbioacts.8110461
Submitted: 23 May 2025 Revised: 14 July 2025 Accepted: 19 July 2025 Published: 20 July 2025
Bacteria shape every part of life—from sustaining ecosystems to influencing food safety, human health, and industrial innovation. Because their growth responds quickly to environmental changes, understanding how factors such as temperature, pH, nutrients, oxygen availability, and moisture influence bacterial behavior is essential across many scientific and practical fields. This systematic review brings together findings from experimental microbiology, environmental studies, and computational modeling to create a more complete picture of how bacteria grow, adapt, and survive. Using evidence collected from PubMed, Scopus, and Web of Science, the review examines both classical laboratory research and modern predictive approaches, including machine-learning models and omics-integrated frameworks. Across the literature, predictive tools consistently demonstrate their value: they help estimate microbial risks in food products, guide infection-control practices in healthcare, and optimize fermentation and bioprocessing in industry. Integrating genomics, transcriptomics, and metabolomics has further strengthened these models by accounting for natural biological diversity and metabolic shifts under stress. Yet, challenges remain. Microbial interactions, genetic variability, and complex real-world environments often behave differently than controlled laboratory systems, limiting the accuracy of existing predictions. The review emphasizes the need for larger, standardized datasets and more adaptive computational tools capable of capturing ecological complexity. Overall, this synthesis highlights how combining environmental microbiology with computational modeling provides powerful ways to anticipate bacterial behavior. Strengthening these approaches can support safer food systems, more effective public-health strategies, and sustainable industrial practices.
Keywords: bacterial growth; predictive modeling; machine learning; environmental factors; microbiology; omics integration; food safety
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