Using machine learning to exploit the potential of aquatic biotechnology and produce novel industrial products.
Project SECRETED: Sustainable exploitation of bio-based compounds revealed and engineered from natural sources.
SECRETED is a multidisciplinary project involving multiple stakeholders and with a strong participation of SMEs to fully exploit the potential of aquatic biotechnology and produce novel industrial products for the agrochemical, pharmaceutical, cosmetic and chemistry sectors.
SECRETED aims to develop novel hybrid molecules with tailor-made properties obtained from the combination of biosynthetic genes of amphiphilic compounds (biosurfactants and siderophores) produced by marine and extremophilic microorganisms. The project focusses on reutilizing previous sampling efforts by screening already collected microbial collections from previous European initiatives. Machine Learning algorithms are deployed to reveal the genetic mechanisms responsible for their biosynthesis and to expand the chemical diversity of such bio-based compounds.
To this end, databases inspection and new collected data are employed to construct a unique microbial amphiphilic compound chemical space comprehending molecular structures, physicochemical characteristics, associated bioactivities and revealed genetic mechanisms responsible for their biosynthesis. To expand the chemical diversity and enhance the industrial sustainable exploitation of such bio-based compounds, Biosynthetic gene clusters (in charge of the production of these molecules are reverse engineered by standardizing and modularizing the genetic elements comprising such clusters. Their potential benefits are broadened by looking for Industry-driven (agrochemical, cosmetic, nutrition and health) formulations based on the engineered combinations of the genetic elements expressed in suitable microbial hosts. New strains are then be designed, built, and tested in an iterative process for the development of viable and sustainable industrial processes.
Our main tasks
- Project coordination
- Machine learning approaches to survey databases for siderophore and biosurfactants information.
- Molecular families inspection
- Amphiphilic siderophores and biosurfactant biosynthetic gene cluster analysis.
- Predictions resulting from combinatorial simulations of standardized gene elements.
- Model-driven microbial chassis evaluation for biosurfactants and siderophores overproduction.
- Development of a holistic mathematical model integrating the core units of the process.
- Multidisciplinary design optimization.
- Process conceptual design and engineering.
- Regulatory assessment
ACCUPLEX DIAGNOSTICS | BIO BASE EUROPE PILOT PLANT | BLUESYN | EXELISIS | IDENER.AI | IMPERIAL COLLEGE LONDON | LUND UNIVERSITY | MATÍS | NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS | PHARMAMAR | SPHERA ENCAPSULATION | STAZIONE ZOOLOGICA ANTON DOHRN | SYLENTIS | UNIVERSITY OF SEVILLA | UNIVERSITY OF TÜBINGEN
Start date – finish date
06 / 2021 - 05 / 2025
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 101000794