Multiscale interactions between genes, cells, communities, hosts, and environments result in Antimicrobial Resistance (AMR), which are hard to predict using classical, single-paradigm models. Our concept of a combined predictive microbiological framework is a synthesis of mechanistic kinetics and Pharmacodynamics/Pharmacokinetics (PK/PK) limits and the present-day AI to predict microbial growth and death, as well as resistance development. The biophysical structure and safety limits are put in place through the mechanistic core ordinary differential equations, reaction-diffusion transport, and agent-based biofilm modules. Graph Neural Networks(GNNs) data-driven Components graph neural networks over gene-drug-plasmid graphs, Protein/DNA language models, Resistome profiling Bayesian deep learners, Minimum Inhibitory Concentration (MIC) regression provide flexible function approximation with calibrated uncertainty. These layers are connected by probabilistic data assimilation, and the dosing strategies are assessed, and causal inference and counterfactual simulation attribute resistance mechanisms (efflux, target modification, enzymatic degradation, and permeability changes). Active learning will pick experiments (e.g., time-kill assays, lab-on-chip gradients) that minimize posterior uncertainty to the maximum, thus leading to an iterative digital twin of microbiology. Validate across stratified pathogen-site splits and external challenge sets, demonstrating improved MIC accuracy, better calibration, and more faithful phase timing versus sequence-only or kinetics-only baselines. The framework assists with stewardship and food-safety decisions by predicting the outcome of treatments using mono- and combination therapies, the collateral sensitivity, and stress-testing the policies in conditions of environmental variability. This method is based on interpretable biophysics and scalable AI to speed up hypothesis generation, optimize antibiotic regimens, and enhance surveillance in AMR. Predictive Microbiology, Antimicrobial Resistance (AMR), MIC Prediction, Agent-Based Biofilm Simulation, Graph Neural Networks, Active Learning.
Research Article | Open Access | Download Full Text
Volume 1 | Issue 2 | Year 2025 | Article Id: RLS-V1I2P105 DOI: https://doi.org/10.59232/RLS-V1I2P105
Predictive Microbiology Using AI to Model Microbial Dynamics and Antibiotic Resistance Mechanisms
Mohana. S. J
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 22 Jul 2025 | 24 Aug 2025 | 09 Sep 2025 | 30 Sep 2025 |
Citation
Mohana. S. J. “Predictive Microbiology Using AI to Model Microbial Dynamics and Antibiotic Resistance Mechanisms.” DS Reviews of Research in Life Sciences, vol. 1, no. 2, pp. 43-54, 2025.
Abstract
Keywords
Predictive Microbiology, Antimicrobial Resistance (AMR), MIC Prediction, Pharmacodynamics/Pharmacokinetics (PK/PD), Mechanistic Modeling, Graph Neural Networks (GNNs), Agent-Based Biofilm Simulation, Active Learning, Digital Twin, Resistance Mechanisms.
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