DIGIT-BIO metaprogramme

DIGIT-BIO

Digital biology to understand and predict biological systems

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Illustration thèse confinancée
article

03 February 2026

By: Marjorie Domergue

Estimation of wheat architectural development dynamics from high-throughput field phenotyping data using a hybrid AI-3D plant model approach

Thesis by Aurélien Besnier (2025 - 2028, UMR EMMAH). The overall aim of the thesis is to estimate wheat architectural development traits at the organ level using the Phenomobile V2 unmanned rover, the latest generation of high-throughput field phenotyping (HTP) instruments.
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Thesis by Julien Kossi Kowou (2025 - , UMR GABI). This PhD project aims to develop statistical analysis methods to explore inter-species genetic diversity.

Photo de chenille du maïs © INRAE, Buisson Christophe

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.

OBAMA © Pexels Sarai Zuno

The genetic selection of animals has been revolutionised over the past years by the advent of genomics, making it easier to select for specific essential phenotypes. Nevertheless, the task of understanding the links between observed genetic variations and phenotypic characteristics of interest remains complex. The OBAMA interdisciplinary project proposes to combine AI with genomics to improve our understanding of the influence of genetic factors on phenotypes in pigs.

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".

 

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