Artificial Intelligence for Predictive Microbiome Engineering: Integrating Community Design and Environmental Validation
- 1BCSIR Chattogram Laboratories, BCSIR, Chattogram, Bangladesh.
- 2Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh.
- 3Biotechnology and Genetic Engineering Discipline, Life Science School, Khulna University, Khulna, Bangladesh.
Abstract
Microbial ecosystems can now be measured across genomic, transcriptomic, proteomic, metabolomic, and environmental dimensions, yet their emergent functions remain difficult to predict and control. Artificial intelligence (AI) can integrate heterogeneous data, but predictive accuracy alone does not establish causality, transferability, or controllability. This review developed a five-layer framework: observe, infer, predict, design, and learn that connects microbial measurement to prospective intervention and experimental feedback. We critically synthesise evidence from sequence and protein learning, multi-omics integration, metabolic modelling, synthetic microbial communities, environmental biotechnology, and temporal forecasting. Recent primary studies illustrate progress in forecasting community trajectories, predicting microbial competition and designing consortia for pollutant degradation; they also reveal important boundaries between model performance and demonstrated ecosystem control. We examine risks arising from compositional data, information leakage, dependent observations, batch effects, uncertain calibration, sparse perturbation data, ecological instability, and evolutionary change. We propose operational criteria for ecosystem programmability and a staged validation pathway from controlled systems to field-relevant environments. Progress will depend on standardised context-rich data, hybrid AI-mechanistic models, prospective benchmarks, independent replication, and safety-constrained design.
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BibTeX
@article{bhuiyan2026artificial,
title = {{Artificial Intelligence for Predictive Microbiome Engineering: Integrating Community Design and Environmental Validation}},
author = {Mohammad Nazrul Islam Bhuiyan and Sheikh Julfikar Hossain},
journal = {Environmental microbiology reports},
year = {2026},
month = oct,
doi = {10.1111/1758-2229.70454},
url = {https://europepmc.org/article/PMC/PMC13719239},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence for Predictive Microbiome Engineering: Integrating Community Design and Environmental Validation
AU - Mohammad Nazrul Islam Bhuiyan
AU - Sheikh Julfikar Hossain
PY - 2026
DA - 2026/10/29
JO - Environmental microbiology reports
DO - 10.1111/1758-2229.70454
UR - https://europepmc.org/article/PMC/PMC13719239
AB - Microbial ecosystems can now be measured across genomic, transcriptomic, proteomic, metabolomic, and environmental dimensions, yet their emergent functions remain difficult to predict and control. Artificial intelligence (AI) can integrate heterogeneous data, but predictive accuracy alone does not establish causality, transferability, or controllability. This review developed a five-layer framework: observe, infer, predict, design, and learn that connects microbial measurement to prospective intervention and experimental feedback. We critically synthesise evidence from sequence and protein learning, multi-omics integration, metabolic modelling, synthetic microbial communities, environmental biotechnology, and temporal forecasting. Recent primary studies illustrate progress in forecasting community trajectories, predicting microbial competition and designing consortia for pollutant degradation; they also reveal important boundaries between model performance and demonstrated ecosystem control. We examine risks arising from compositional data, information leakage, dependent observations, batch effects, uncertain calibration, sparse perturbation data, ecological instability, and evolutionary change. We propose operational criteria for ecosystem programmability and a staged validation pathway from controlled systems to field-relevant environments. Progress will depend on standardised context-rich data, hybrid AI-mechanistic models, prospective benchmarks, independent replication, and safety-constrained design.
SN - 1758-2229
ER -