Integrative Bioinformatics Approaches in Environmental Biotechnology: A Review
TL;DR
This work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity and offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
Abstract
Environmental biotechnology increasingly relies on bioinformatics to address global challenges in pollution control or degradation, biodiversity conservation, and sustainable resource management. By integrating genomics, computational tools, and artificial intelligence, bioinformatics enables the analysis of complex biological datasets, such as metagenomes and environmental DNA (deoxyribonucleic acid), to uncover microbial diversity, pollutant degradation pathways, and ecological resilience. High‐throughput sequencing technologies and multiomics integration provide novel insights into microbial communities and their functional roles in bioremediation and ecosystem monitoring. Predictive modeling further enhances our ability to simulate microbial behavior in contaminated environments and assess the long‐term impacts of biotechnological interventions. Despite increased progress, challenges remain in managing large‐scale data, fostering interdisciplinary collaboration, and developing user‐friendly bioinformatics platforms. Future directions emphasize the application of machine learning, sustainable resource management, and collaborative frameworks to bridge bioinformatics and environmental sciences. Unlike traditional descriptive reviews, this work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity. It offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.