The pressure to search effective bioremediation methodologies for contaminated ecosystems has led to the large-scale identification of microbial species and metabolic degradation pathways. However, minor attention has been paid to the study of bioremediation in marine food webs and to the definition of integrated strategies for reducing bioaccumulation in species. We propose a novel computational framework for analysing the multiscale effects of bioremediation at the ecosystem level, based on coupling food web bioaccumulation models and metabolic models of degrading bacteria. The combination of techniques from synthetic biology and ecological network analysis allows the specification of arbitrary scenarios of contaminant removal and the evaluation of strategies based on natural or synthetic microbial strains. In this study, we derive a bioaccumulation model of polychlorinated biphenyls (PCBs) in the Adriatic food web, and we extend a metabolic reconstruction of Pseudomonas putida KT2440 (iJN746) with the aerobic pathway of PCBs degradation. We assess the effectiveness of different bioremediation scenarios in reducing PCBs concentration in species and we study indices of species centrality to measure their importance in the contaminant diffusion via feeding links. The analysis of the Adriatic sea case study suggests that our framework could represent a practical tool in the design of effective remediation strategies, providing at the same time insights into the ecological role of microbial communities within food webs.

Modelling bioaccumulation processes at the food web level is the main step to analyse the effects of pollutants at the global ecosystem level. A crucial question is understanding which species play a key role in the trophic transfer of contaminants to disclose the contribution of feeding linkages and the importance of trophic dependencies in bioaccumulation dynamics. In this work we present a computational framework to model the bioaccumulation of organic chemicals in aquatic food webs, and to discover key species in polluted ecosystems. As a result, we reconstruct the first PCBs bioaccumulation model of the Adriatic food web, estimated after an extensive review of published concentration data. We define a novel index aimed to identify the key species in contaminated networks, Sensitivity Centrality, and based on sensitivity analysis. The index is computed from a dynamic ODE model parametrised from the estimated PCBs bioaccumulation model and compared with a set of established trophic indices of centrality. Results evidence the occurrence of PCBs biomagnification in the Adriatic food web, and highlight the dependence of bioaccumulation on trophic dynamics and external factors like fishing activity. We demonstrate the effectiveness of the introduced Sensitivity Centrality in identifying the set of species with the highest impact on the total contaminant flows and on the efficiency of contaminant transport within the food web.