Abstract

The increasing contamination of ecosystems by a wide array of environmental pollutants such as heavy metals, hydrocarbons, pesticides, and pharmaceuticals poses significant challenges to environmental sustainability and human health. Traditional remediation methods are often costly, time-consuming, and environmentally invasive. Bioremediation, the use of microorganisms, plants, and their enzymes to degrade or transform pollutants, offers a more sustainable and efficient solution. However, to maximize its effectiveness, bioremediation strategies must be optimized, and this is where bioinformatics and computational tools play a critical role. By leveraging genomic, proteomic, and metagenomic data, bioinformatics tools can help identify potential bioremediation pathways, optimize microbial strains, and predict the degradation rates of pollutants under various environmental conditions. This review focuses on the application of computational approaches in bioremediation, exploring how bioinformatics tools can be used to analyze microbial genomes, predict metabolic pathways, and design efficient bioremediation strategies. The integration of computational techniques, such as machine learning, systems biology, and network modeling, with experimental bioremediation approaches holds great potential to enhance pollutant degradation, improve remediation efficiency, and guide the development of sustainable environmental management practices.