Our solutions

Digital cell with a metabolic network allows simulating flow of matter and energy towards physiological traits, like biomass growth, high value compounds production, etc… under environmental constrains.

We reconstruct a high-quality large scale or genome-scale metabolic network of your organism. Each component of the network is manually curated and the model includes state-of-the-art bibliography. We run validation steps with available experimental datasets.

We reconstruct a high-quality large scale or genome-scale regulatory network of your organism.

Each component of the network is manually curated and the model includes state-of-the-art bibliography. We run validation steps with available experimental datasets.

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We developed an artificial intelligence algorithm (that we call the tracker) to design synthetic organisms.

Our algorithm takes into account environmental clues, the robustness of the solution and trade-offs with other biological functions.

The strength of this approach is the combination of our high-quality curated database with the tracker that allows us to identify optimal solutions.

As compared to classical synthetic microorganism production, our virtual screen approach allows you to :

• Simulate millions of synthetic organisms.
• Select a limited number of synthetic organisms to validate experimentally.
• Generate patentable synthetic solutions.

You can easily save time and money and keep a competitive edge over your competitors.

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Prediction of crops complex traits is a main challenge during seed breeding processes.

Our solution @iMEAN mixes systems biology, an approach taking into account causal biological mechanisms driving complex traits, with a genomic selection model.

Depending of your objectives, our algorithm can optimise yield, pathogen resistance, robustness to environmental variations.

We design synthetic microbiota (mixture of microbes to seed into soil or leaves) considering crop traits to be optimised and environnemental clues. Divers traits can be optimised like pathogens control efficiency, plant health and yield.

The revolution of Omics data production is leading biology in the big data era. Our technology allows making your data useful.

The analysis consists on integrating your Omics datasets into digital organisms. Combining Omics data and systems biology allows to reveal Omics to phenotype causality and  identify key drivers of the phenotype in your Omics data set.

Our algorithm identifies molecular networks through a broad range of datasets, like omics data.

Many behaviors of living organisms rise at the network scale. Such so call emergent properties are complex to understand.

Our digital organisms technology allows to reveal molecular mechanisms driving emergent properties.

Among them, we performed

• trade-off (resources allocation, crosstalk) analysis between broad set of biological functions and identify key components controlling or responsible for the trade-off.
• robustness analysis of your organism when facing internal (mutations, chemicals) perturbations or environmental perturbations (changes in environmental conditions).