Guiding and optimizing the screening process




Genetic engineering approaches require a deep understanding of the genomes of the species of interest. DEINOVE’s Bioinformatics platform studies the genetic material with three specific aims:

  1. integrate new bacterial strains,
  2. mine for genes involved in the production of a given compound and
  3. understand the metabolic pathways at play that will need to be modified to optimize compound production.


Harvesting new bacterial strains: data-driven bioprospection

The bioinformatics platform brings rational insight to Deinove’s bioprospection effort. Environmental samples collected in nature are systematically analyzed in the lab by metabarcoding or metagenomics sequencing. These technics allow to characterize and quantify the microbiome present on the sampling points for the subsequent use of targeted microbiology methods.

Genome mining

High-resolution genome sequencing is performed on the most promising bacterial strains using second and third generation sequencing technologies. Subsequently, these high-quality genomic sequences can be processed with genome mining algorithms able to determine the gene cluster involved in the biosynthesis of a specific molecule of interest. The provision of these information can greatly accelerate the determination of the structure of the active molecule.

Metabolic modelling

Prior to introducing a new synthetic pathway into a chassis strain, the genome sequence of the strain is studied in order to construct a model of the strain metabolism. These models can be used to evaluate via computer simulation the production yield and thus economic performance that can be expected for a target molecule. They are also very important to design synthetic pathways that will perform optimally in the context of the chassis strain metabolism.

Integrative biology

Deinove is building its own integrative biology platform dedicated to antimicrobial natural products to increase its knowledge by recovering and studying public knowledge. This software aims to process available knowledge in the antimicrobial field (from the antibiotic producing strains to the molecules) that is compiled into in an organized database. Not only the availability of these accurate data is very useful for researchers, but it also feeds artificial intelligence tools that take these into account at various levels in the R&D process.


Key features



Support at different stages of the R&D process

Helps to speed up and streamline the R&D steps