Microbiology for Sustainability
Combining microbiology and molecular biology expertise to address current challenges in food biosustainability.
Microbiology for Sustainability at iBET
iBET’s Microbiology for Sustainability R&D area combines in-depth knowledge of conventional microbiology, microbial communities, and molecular biology with state-of-the-art technologies to develop innovative solutions for a healthier and more sustainable food future.
More specifically, we target the development of antimicrobial solutions to tackle current threats in food safety and quality. We also explore the immense potential of microbes and microbial communities to obtain valuable bioactive extracts that can be used in agrifood, pharma and cosmetic applications.
Our team focuses its activities on three main areas: Microbial Ecology, Microalgae Microbiome & Bioprocesses and Food Quality & Safety.
Microbial Ecology for a Sustainable Future
Sustainable food systems begin with minimizing the impact of agricultural practices on the environment and in human health.
To address this challenge, we study the intricate relationships between microbes, plants, and their surroundings.
A key area lies in exploring the potential of microorganisms to create bioactive extracts for agriculture applications within a sustainable framework.
These versatile extracts can serve multiple purposes, such as:
- Enhancing soil fertility (biofertilizers),
- Boost plant growth (bio-stimulants),
- Help manage diseases and pests (biocontrol agents or co-formulants).
Microbial Powerhouses
Bacteria and Microalgae hold great potential due to their ability to synthetize diverse high-value bioactive compounds, like antioxidants, proteins, lipids, and polysaccharides.
At iBET, we pioneer the development of efficient microalgae bioprocesses for optimal production of bioactive compounds.
A key step in this process is the optimization of microalgae growth through the understanding of their complex symbiosis with bacteria, utilizing advanced techniques such as spectroscopy and machine learning models to monitor these microalgae-bacteria interactions.
Safeguarding Food Systems
In today’s globalized food industry, Food Quality & Safety are paramount concerns.
New trends like fresh and ready-to-eat meals, exotic ingredients, and large-scale production present unique challenges in preventing foodborne illnesses, food fraud, and nutritional deficiencies.
We tackle these concerns through a multi-faceted approach:
- Detection of Microbial Threats: by assessing the presence, behavior, and potential health risks of microbes in food and water sources. We achieve this by utilizing cutting-edge “foodomics” technologies (genomics, metabolomics, and proteomics) alongside conventional microbiological and analytical chemistry and molecular biology techniques.
- Ensure Food Integrity: by leveraging molecular biology tools to determine food authentication, focusing on the detection of specific molecular signatures. Moreover, we determine the geographical and botanical origin of edible plants (e.g. aromatic, medicinal and condiment plants) utilized in the several industries. These analyses are crucial to ensure authenticity, detect frauds and/or food safety hazards.
Related Technologies
Phenotypic techniques
Phenotypic techniques
Conventional microbiology, spectroscopy (vis, FTIR), fluorimetry, microscopy (light, phase contrast, fluorescence, confocal, SEM), flow cytometry, chromatography (GC, HPLC, UPLC), and spectrometry coupled techniques (GC-MS, LC-MS).
Molecular Biology Techniques
Molecular Biology Techniques
For DNA and RNA characterization, we utilize targeted approaches (DNA and cDNA amplification, PCR-based, Sanger sequencing of specific targets (e.g., 16S rRNA gene, DNA barcodes)) and untargeted approaches (electrophoresis, NGS - metagenomics, deep shotgun metagenomics, multilocus metagenomics, WGS, transcriptomics (RNA-Seq)). As well as molecular cloning, generation of marker and markerless mutants, and protein expression.
Bioinformatics
Bioinformatics
Analytical pipelines for genomic, transcriptomic, proteomic and metabolic data sets.
