Multi-omics and advance technologies in biodegradation of emerging contaminants and eco-estrogens in environmental waste

Abstract

It has recently been determined that environmental contamination caused by xenobiotics and other associated resistant substances poses a serious concern to environment and human health. There are several considerations that determine whether to utilize in situ or ex situ bioremediation, including cost, kind of contaminant, and concentration. Polluted areas can naturally be recovered because microorganisms can adapt to a range of situations and can produce metabolites that break down and modify poisons. Today’s omics technologies—transcriptomics, proteomics, and metagenomics, for example—are utilized to develop methods for researching the diversity and ecology of microorganisms and their possible uses in environmental monitoring and bioremediation. The use of the functional characteristics of cells in vivo to specifically choose experimental targets and mould analytical aims and procedures is what defines functional multi-omics. Because of the enormous libraries of compounds and biological activity based on quantitative structure–activity connections, computational prediction models are essential for understanding the difficult work of ECs screening. Emerging machine learning and artificial intelligence trends based on more effective predictive models may increase the accuracy of EC prediction and their biological role in the environment. Machine learning methods may help with the validation and performance assessment of generated models, as well as selecting their application regions.