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Today, environmental pollution is a serious problem, and bioremediation can play an important role in cleaning contaminated sites. Remediation strategies, such as chemical and physical approaches, are not enough to mitigate pollution problems because of the continuous generation of novel recalcitrant pollutants due to anthropogenic activities. Bioremediation using microbes is an eco-friendly and socially acceptable alternative to conventional remediation approaches. Many microbes with a bioremediation potential have been isolated and characterized but, in many cases, cannot completely degrade the targeted pollutant or are ineffective in situations with mixed wastes. This review envisages advances in systems biology (SB), which enables the analysis of microbial behavior at a community level under different environmental stresses. By applying a SB approach, crucial preliminary information can be obtained for metabolic engineering (ME) of microbes for their enhanced bioremediation capabilities. This review also highlights the integrated SB and ME tools and techniques for bioremediation purposes.

Bioremediation is a better alternative and widely accepted approach used for efficient degradation of environmental pollutants released from industries, urban and agricultural activities due to its eco-friendly nature. Systems biology helps in the identification of new genes, proteins, metabolites, and metabolic pathways involved in bioremediation. Such information can be used for designing synthetic microbial communities that can degrade multiple recalcitrant pollutants simultaneously. This review gives a brief insight into various systems biology tools towards providing a greater understanding of microbial behaviour and improving the way of bioremediation. These techniques alone or in combination, provide a way to understand and improve the genetic potential of microorganisms to remediate various environmental contaminants efficiently. Further, this review also describes the successful employment of synthetic microbial consortium in the bioremediation. Moreover, In-silico tools are also described to analyse the data obtained through different laboratory experiments as well to predict the behaviour of microbial consortium towards the pollutants using different databases. [ABSTRACT FROM AUTHOR]