MISIVI

Partners:

  • IHSEV
  • ADRIA Développement
  • Pôle Agronomique de l’Ouest
  • Région Bretagne

Contact:

desmeulles@enib.fr

Description:

The aim of the project is to study bacterial interactions in foods. In the food industry, the products affected by bacterial interactions are numerous: Fermented milk products (milk, sausage …), brine, raw products …However, manufacturers do not have predictive tools of development of bacterial flora, since several species are involved. This project aims to overcome this limit and enrich model that deal with many “living” foods.

Currently, our software partner uses predictive biology SYMPREVIUS, which predicts the evolution of a population of bacteria. For example, in the case of a pathogenic bacterium Listeria monocytogenes, the software provides the population growth of bacteria in a certain amount of food. Unfortunately, it is not possible to predict the evolution of many populations interacting in the same substrate. Thus, the software can not be used to model the inhibition of Listeria monocytogenes by a flora of lactic acid bacteria and strain evolution is modeled for each species separately. Indeed, the current method encounters a limit: it is impossible to simulate the evolution of colonies in competition since the models do not take into account the interactions between bacterial colonies.

ReISCOP allows us to model bacteria as autonomous systems coupled to an environment.
These bacteria can move, divide, die, interact with the environment, etc…In coming to express the good interaction laws for bacteria and the environment, we will be able observe the competition between bacteria colonies.

We computer scientists, know how to simulate. Our industrial partners and biologists know what to simulate and how to produce the missing data. For this project, we propose a web interface using a version of ReISCOP running Unity3D. This provides the opportunity to use the VR interfaces to improve handling of the 3D model. For example, in order to facilitate the visualization of 3D data, we develop a system for head tracking allows the modeler to intuitively modify the observation point of the model. All this gives us a sandbox in which it is easy to test and modify models of bacteria, and to identify what data is missing. Biologists and companies involved in this project can then imagine the in vitro experiment to complete the numerical model. Once we have succeeded in developing a prototype model, we can validate according to a conventional method to integrate in Symprevius tool.