Métaprogramme DIGIT-BIO. Crédit photo : @REZOOmarketing

News

Period
OBAMA © Pexels Sarai Zuno
article

18 March 2025

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.
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.
article

01 April 2025

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.
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.
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 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.
Illustration adn
article

11 March 2025

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
article

01 April 2025

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.
Illustration thèse confinancée
article

01 April 2025

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

01 April 2025

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

11 March 2025

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

11 March 2025

By: Com

Predicting plant phenotypes under combined stress

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

11 March 2025

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

11 March 2025

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.
article

11 March 2025

By: Com

Visualising fish oocytes using AI and 3D imaging

In the natural environment as well as in fish farming, the process of formation and maturation of female gametes (oogenesis) is essential for reproductive success.
article

11 March 2025

By: Com

Application of machine learning and deep learning to improve animal genomic selection

The development of genomic selection - and other "omics" analyses such as metagenomics, transcriptomics, metabolomics and proteomics - now makes it possible to characterise animals using thousands of measurements. This massive data is integrated into models to predict production traits with the highest possible degree of accuracy.
article

14 March 2025

By: Com

Analysing biological networks of mixed-type data with copula models

Integrative biology is based on the study of complex biological networks. Understanding the plasticity of biological interaction networks due to phenotypic, environmental or interventional variability is an important challenge in fields as diverse as genomics or human nutrition. Such studies often include comparisons between contrasting groups, including variables of various natures (continuous, counts, binary, etc.). These so-called "mixed-type" data can be difficult to analyse in a unified way. While multivariate probabilistic models provide a robust framework for inferring interrelationships among continuous variables, an analogous model for mixed-type data has yet to be defined.