From metabolic burden to distributed function: AI-guided engineering of microbial consortia for industrial and hazardous waste remediation

This is the authors' abstract. We don't add key points for this paper.

Industrial waste streams are characterised by chemically heterogeneous, dynamically fluctuating mixtures of pollutants, which far exceed the functional capacity of conventional single-organism-based bioremediation. Although metabolic engineering has greatly expanded microbial degradation capabilities, metabolic burden, redox imbalance, and the formation of toxic intermediates still hinder microbial performance. Division of labour (DoL) microbial communities function as programmable distributed metabolic networks. In these systems, metabolic pathways, toxicity management, and ecological interactions are distributed across multiple strains. Recent advances in synthetic biology have enabled the design of programmable microbial consortia through the integration of responsive gene circuits, feedback regulation, and metabolic burden control. Synthetic communication platforms, including orthogonal quorum sensing, enhance population control and stability in multi-strain DoL communities. In addition, spatial reactor organization enhances degradation efficiency and long-term operational stability. The framework is particularly relevant to chemically heterogeneous waste streams, including polycyclic aromatic hydrocarbons (PAHs), chlorinated solvents, plastic-derived monomers (PET, urethanes), pharmaceutical residues, and mixed petrochemical effluents, where pollutant diversity and toxicity pose significant challenges to conventional treatment approaches. Quantitative benchmarks from recent studies illustrate this potential: engineered sequential consortia have achieved 90.7% phenanthrene degradation within 6 days (vs. 12.1% without cooperation), and two-member PET-upcycling systems reached ~74% conversion of monomers into polyhydroxyalkanoates, metrics unattainable by monocultures. However, the industrial deployment of these programmable consortia remains constrained by long-term genetic instability, signal crosstalk in complex environments, and the absence of validated scale-up frameworks. This review integrates synthetic biology, microbial ecology, bioprocess engineering, and AI-assisted optimization into a unified framework, critically evaluating both the demonstrated capabilities and the persistent translational barriers for sustainable remediation of industrial and hazardous pollutants.