Métaprogramme DIGIT-BIO. Crédit photo : @REZOOmarketing

News

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@Olivier FILANGI
article

29 June 2026

By: Marjorie Domergue

Contextualization of plant metabolomics data using knowledge graphs enhanced by Big Data, AI and Semantic Web technologies

The SEED project expands the Metabolomic Semantic Data Lake (MSD), an electronic infrastructure that combines the Semantic Web with Big Data technologies to enable large-scale processing of the scientific literature on metabolomics. First developed to improve access to knowledge on the links between the metabolism and human health, this infrastructure has now been adapted to include plant metabolisms. SEED will refine its use of artificial intelligence, enabling publications to be annotated automatically through the use of ontologies. The project will also expand the range of sources included in the data lake and will draw on four case studies to validate and illustrate the new associations discovered between metabolites, biomarkers and plants.
Photo d'un puceron © wirestock, Freepik
article

05 June 2026

By: Marjorie Domergue

Open digital phenotyping to accelerate the creation of aphid-resistant plant varieties

The pressures exerted on plants by insect pests such as aphids are increasing, as are the viral diseases they carry. The changing climate, reduced insecticide use and the development of resistance to those pesticides that are still authorized have all conspired to boost the life cycle and population dynamics of these pests. With the need to reduce insecticide use, new agro-ecological approaches have emerged in recent decades that use crop genetic diversity as a tool to modify aphid behaviors or performance. To be effective, such approaches require the accurate characterization of plant genetic diversity, yet there is currently no open, high-performance, standardized and affordable phenotyping system available to developers of future applications. The aim of the Gratitude consortium is to fill this gap by developing a digital plant-aphid system that can characterize aphid development and behavior.
Illustration thèse confinancée
article

09 July 2026

By: Marjorie Domergue

Large scale paired multi-omic data integration

Thesis by Julien Kossi Kowou (2025 - , UMR GABI). This PhD project aims to develop statistical analysis methods to explore inter-species genetic diversity.
article

03 February 2026

By: Marjorie Domergue

Mechanoperception of Wind as a Modulator of Plant Immunity and Plant–Microorganism Interactions

Thesis by Adèle Coppel (2025 - 2028, LIPME). The objective of this PhD project is to investigate, through modelling approaches, the role of wind in the modulation of plant immune responses and to identify molecular actors involved in the variability of resistance associated with acclimation and sensitization.
Photo de chenille du maïs © INRAE, Buisson Christophe
article

15 November 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.
Illustration thèse confinancée
article

09 July 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.
OBAMA © Pexels Sarai Zuno
article

26 June 2026

By: Marjorie Domergue

Artificial intelligence as a tool for the genetic selection of livestock

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.
Fermentwin © Freepik
article

15 November 2024

By: Com

Using digital twins to predict the evolution of food microbiota during vegetal fermentation

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.
bandeau in silicow 2.JPG
article

15 November 2024

By: Marjorie Domergue

The inSiliCow simulator: a virtual dairy farm to improve real-farm management

By applying the concept of the digital twin at the scale of a dairy farm, the inSiliCow project will develop a multi-scale simulation tool to aid on-farm decision-making with regard to farming practices for dairy cows. The inSiliCow project is a flagship ‘digital twin’ project for the Metaprogramme DIGIT-BIO.
HepatO'Twin © Julos, Freepik
article

18 June 2024

By: Marjorie Domergue

HepatO’Twin: a digital twin to investigate the effects of food contaminants on the hepatic metabolism

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.
Bandeau jumeaux numérique DIGIT-BIO @REZOOmarketing
article

25 November 2024

By: Marjorie Domergue

Overview of actions funded by DIGIT-BIO (2021-2024)

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.
article

09 July 2026

By: Marjorie Domergue

Machine learning and high-throughput epigenotyping: a new lever to improve phenotype predictions in cattle

Thesis by Alexandre Asset (BREED /MIA-PS, 2024 - 2026). Building on the work of EPINUM, this thesis proposes to investigate the most appropriate AI approaches that integrate epigenetic data into phenotype prediction models.
article

09 July 2026

By: Marjorie Domergue

Spatiotemporal modeling of signaling pathways: impact of endosomal compartmentalization and application to gonadotropin receptors.

Thesis by Chloé Weckel (PRC, 2024-2026). Following on from the IMAGO exploratory project funded by DIGIT-BIO, this thesis continues the interdisciplinary development of new formalisms to describe the spatio-temporal dynamics of cell signalling in the context of reproductive control.
Illustration adn
article

10 July 2026

By: Com

An interdisciplinary network for 3D genomics

in the nucleus of a cell, the three-dimensional conformation of the genome has a major impact on how it functions. A better understanding of the links between the 3D structure of the genome and its functioning represents a methodological challenge and requires dialogue between different disciplines
Illustration thèse confinancée
article

09 July 2026

By: Marjorie Domergue

Characterization and algorithmic modeling of root nitrogen in a heterogeneous nitrate environment.

Thesis by Cannelle Armengaud (IPSiM, 2023-2026). This thesis builds on the ALGOROOT project. It seeks to better understand and model the behavior of plant root systems when offered a choice between environments with differing nutrient availability. It will also characterize and integrate transport response dynamics into the model.
article

09 July 2026

By: Marjorie Domergue

Deep learning on graphs for morphofunctional analysis and comparison of brains

Thesis by Antoine Bourlier (PRC, 2022-2025). This thesis seeks to create new algorithms for anato-functional comparison of brain data from growing lambs using both traditional graph theory and new graph-based deep learning methods to study the differences between individuals and over time.
article

11 March 2025

By: Com

A new tool for exploring the multi-regulator and multi-scale network controlling plant architecture

To maintain the agronomic performance of plants in increasingly stressful environments, it is necessary to have a systemic vision of their adaptation mechanisms, particularly their architectural development, i.e. the initiation and development of new organs.
article

27 January 2026

By: Com

Modeling decision algorithms for root development in heterogeneous environments

To survive, plants must take up water and many nutrients from the soil. These resources are unevenly distributed and plants must explore the soil to find them. This exploration requires the extension of roots, which is a development that comes at a cost for the plant.
article

29 June 2026

By: Com

Predicting plant phenotypes under combined stresses using resource allocation models

Climate change, the scarcity of certain natural resources and the need to reduce agricultural inputs have increased the number and diversity of situations that agronomists need to understand. They need plant models with extensive predictive capability and capable of taking into account complex environmental conditions, where different constraints (stresses) come into play at the same time.
cultures d'arabidopsis
article

10 July 2026

By: Com

Predicting plant response to combined stresses (CO2 and Heat)

Plants are constantly threatened by biotic and abiotic stresses, especially in the current context of climate change. The complexity of the stress response involves different levels of biological organisation, from genomes to metabolites.
article

18 June 2026

By: Com

Predicting the response of plants exposed to chronic thermal stress

Climate change is characterised not only by variable and extreme intensities of the main climatic factors but also by an increased frequency of extreme events, such as heat waves, which are highly detrimental to field crop yields and harvest quality.
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