DIGIT-BIO

Digital biology to understand and predict biological systems

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Photo de chenille du maïs © INRAE, Buisson Christophe
article

25 September 2024

By: Domergue Marjorie

Modeling the neural mechanisms of gravity perception in European corn borer caterpillars

How caterpillars perceive gravity is still not known. However, an evolutionary adaptation in the caterpillars of the European corn borer enables these larvae to use gravity information to move down the cob and thus avoid being killed during harvesting. The main aim of this project is to understand how caterpillars use gravity information to orient their movement.
Bandeau jumeaux numérique DIGIT-BIO @REZOOmarketing
Since its launch in March 2021, DIGIT-BIO has funded 10 interdisciplinary networks, 17 exploratory projects and 2 flagship projects in the field of digital biology. Find an overview of the actions supported by the metaprogramme.
Fermentwin © Freepik
The control of continuous fermentation during production is a major challenge for manufacturers of fermented vegetable juice drinks. With its proposed development of a digital twin that can continuously predict and control the plant fermentation process, the FermenTwin project could provide food technologies with a valuable solution.
HepatO'Twin © Julos, Freepik
The HepatO'twin project will put the concept of the digital twin to use in exploring the effects of food contaminants on the liver’s metabolism. This will allow us to advance understanding of the contribution made by diet and exposure to food contaminants to the risk of developing metabolic diseases.

Our goals

The quantitative and qualitative explosion of data in biology, combined with the development of new tools for processing and analysing these data, is revolutionising research in the life sciences. This development opens up new perspectives for better understanding the functioning of biological systems and predicting their behaviour.

The metaprogramme Digit-Bio aim to support research at the interface between computational / engineering sciences and life sciences (biology, physics, chemistry or environmental sciences), in order to:

  • Understand the functioning and predict the behaviour of biological systems
  • Anticipate the impact of stresses on these systems, reason out their management and develop levers for action.  In the medium term, the ambition is to develop a small number of projects for in silico monitoring of biological systems, based on the concept of the "digital twin".

 

Axis schématic DIGIT-BIO © INRAE
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