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BIOREM is focus on the generation of a common solid knowledge, arising from the cross-sectorial synergy between recognized research centres, industry and academies involved in bioremediation, computational biotechnology and computing, using a multidisciplinary team, integrating environmental sciences, engineering biotechnology, molecular systems biology and artificial intelligence (AI) tools.
BIOREM is focus on the generation of a common solid knowledge, arising from the cross-sectorial synergy between recognized research centres, industry and academies involved in bioremediation, computational biotechnology and computing, using a multidisciplinary team, integrating environmental sciences, engineering biotechnology, molecular systems biology and artificial intelligence (AI) tools.
Hypothesis
Hypothesis
1. The species present in polluted sites are able to obtain food and energy from pollutants.
1. The species present in polluted sites are able to obtain food and energy from pollutants.
2. The characterization of the metabolic routes used by the species found in polluted sites is worthy to select species to be used in bioremediation.
2. The characterization of the metabolic routes used by the species found in polluted sites is worthy to select species to be used in bioremediation.
3. The species present in polluted sites are able to obtain food and energy from pollutants.
3. The species present in polluted sites are able to obtain food and energy from pollutants.
4. Bioremediation is boosted by the Improved Growing of Synthetic Communities with metabolic functions in common to those found in contaminated sites.
4. Bioremediation is boosted by the Improved Growing of Synthetic Communities with metabolic functions in common to those found in contaminated sites.
Concept
Concept
Objectives
Objectives
BIOREM aims to identify natural metabolic pathways for organic pollutants (TPHs and PAHs) and mixed contamination (organic pollutants and toxic metals) removal through metagenomic analysis to develop genome-scale metabolic (GEM) models for the identification of microbial biodegradation networks, to generate and improve different microbial assisted remediation strategies.
BIOREM aims to identify natural metabolic pathways for organic pollutants (TPHs and PAHs) and mixed contamination (organic pollutants and toxic metals) removal through metagenomic analysis to develop genome-scale metabolic (GEM) models for the identification of microbial biodegradation networks, to generate and improve different microbial assisted remediation strategies.
1.
1.
To improve the efficiency of microbial-assisted phytoremediation. The synergistic effect of plant-microbial interaction will be evaluated on contamination reduction.
2.
2.
To apply Artificial Intelligence (AI) tools for data analysis. AI will be combined with computational biology to support omics data processing and to build predictive pathway models.
3.
3.
New protocols for the isolation and development of microbial synthetic community. The consortium will work through in silico identification of pollutants’ biodegradation networks.
4.
4.
To identify microbial degradation networks. Autochthonous microbial communities of polluted sites will be characterized by advanced molecular and bioinformatics computational techniques.
5.
5.
To empower an international, inter-sectorial, multidisciplinary, and innovative research team in the field of bioremediation. Trainings, workshops and seminars will take place.
6.
6.
To obtain insight into the degradation routes by stable-isotope probing (SIP). To gain knowledge on pollutants degradation, the biogeochemical cycling of the pollutants will be used.
7.
7.
To integrate different techniques for soil characterization. Samples from different polluted sites will be used in the project to evaluate parameters and factors that might influence bioremediation.
Knowledge transfer
Knowledge transfer
Impact
Impact
1. Enhance the career perspective of staff members through collaboration between industry and academia and on-site and on-line training activities.
1. Enhance the career perspective of staff members through collaboration between industry and academia and on-site and on-line training activities.
2. Long-Lasting collaboration network in the field of bioremediation, technologies, omics and computational modelling.
2. Long-Lasting collaboration network in the field of bioremediation, technologies, omics and computational modelling.
3. Knowledge transfer through Intersectoral and interdisciplinary secondments.
3. Knowledge transfer through Intersectoral and interdisciplinary secondments.
4. Research and Innovation potential in the field of in situ bioremediation of organic pollutants and mixed contamination.
4. Research and Innovation potential in the field of in situ bioremediation of organic pollutants and mixed contamination.
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