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Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis

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...

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Autores principales: Taffi, Marianna, Paoletti, Nicola, Angione, Claudio, Pucciarelli, Sandra, Marini, Mauro, Liò, Pietro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162388/
https://www.ncbi.nlm.nih.gov/pubmed/25309577
http://dx.doi.org/10.3389/fgene.2014.00319
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author Taffi, Marianna
Paoletti, Nicola
Angione, Claudio
Pucciarelli, Sandra
Marini, Mauro
Liò, Pietro
author_facet Taffi, Marianna
Paoletti, Nicola
Angione, Claudio
Pucciarelli, Sandra
Marini, Mauro
Liò, Pietro
author_sort Taffi, Marianna
collection PubMed
description 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.
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spelling pubmed-41623882014-10-10 Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis Taffi, Marianna Paoletti, Nicola Angione, Claudio Pucciarelli, Sandra Marini, Mauro Liò, Pietro Front Genet Physiology 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. Frontiers Media S.A. 2014-09-12 /pmc/articles/PMC4162388/ /pubmed/25309577 http://dx.doi.org/10.3389/fgene.2014.00319 Text en Copyright © 2014 Taffi, Paoletti, Angione, Pucciarelli, Marini and Liò. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Taffi, Marianna
Paoletti, Nicola
Angione, Claudio
Pucciarelli, Sandra
Marini, Mauro
Liò, Pietro
Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title_full Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title_fullStr Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title_full_unstemmed Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title_short Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
title_sort bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162388/
https://www.ncbi.nlm.nih.gov/pubmed/25309577
http://dx.doi.org/10.3389/fgene.2014.00319
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