When a Persistent Organic Polluttant (POP) is released into the environment, intentionally or not, its fate and its ecological impact for both living organisms and environment can be hardly predicted. Polluttants enter protein pathways at the cell surface or inside organisms. Current trends in Systems Biology aim to study how living organisms adapt to environmental stressors (e.g. exposure to pollutants) considering classical food webs and molecular pathways as a whole [1]. We propose a computational framework to study long-term bioaccumulation dynamics in the marine ecosystem, and to identify keystone species in polluted food webs. Starting from a literature data and using the Linear Inverse Modeling (LIM) method [2], we have reconstructed the network structure, estimating trophic and contaminant flows. Then, the flow rates of the static contaminant network are used in the parametrization of a ODE-based dynamic bioaccumulation model. Keystone species identification is accomplished with network analysis tools (trophic and topological centrality indices), and with a newly introduced network index, Sensitivity Centrality (SC) [3], based on the sensitivity analysis technique. Results show that keystones, as identified by SC, have a prominent impact on global indices of the contaminated network, thus providing an effective way to detect sentinel species in a polluted environment.

Computational Bioaccumulation Modelling of Pops in the Adriatic Ecosystem: a Network Analysis Approach

TAFFI, MARIANNA;TESEI, Luca;MERELLI, Emanuela;PAOLETTI, Nicola;
2013

Abstract

When a Persistent Organic Polluttant (POP) is released into the environment, intentionally or not, its fate and its ecological impact for both living organisms and environment can be hardly predicted. Polluttants enter protein pathways at the cell surface or inside organisms. Current trends in Systems Biology aim to study how living organisms adapt to environmental stressors (e.g. exposure to pollutants) considering classical food webs and molecular pathways as a whole [1]. We propose a computational framework to study long-term bioaccumulation dynamics in the marine ecosystem, and to identify keystone species in polluted food webs. Starting from a literature data and using the Linear Inverse Modeling (LIM) method [2], we have reconstructed the network structure, estimating trophic and contaminant flows. Then, the flow rates of the static contaminant network are used in the parametrization of a ODE-based dynamic bioaccumulation model. Keystone species identification is accomplished with network analysis tools (trophic and topological centrality indices), and with a newly introduced network index, Sensitivity Centrality (SC) [3], based on the sensitivity analysis technique. Results show that keystones, as identified by SC, have a prominent impact on global indices of the contaminated network, thus providing an effective way to detect sentinel species in a polluted environment.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11581/337191
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