Métaprogramme DIGIT-BIO. Crédit photo : @REZOOmarketing
Understanding
DIGIT-BIO metaprogramme
Themes
Understanding

Understanding

Axis 1 - Deciphering the functions of living matter at multiple scales: regulation and integration of biological processes

This line of research addresses the understanding of biological processes, their regulation and the way in which these processes interact or cooperate. It concerns all levels of organisation of living organisms up to the organism and the population.

The aim is to describe, understand and model biological systems and to establish causal links within and between biological scales, integrating systemic effects such as stochasticity or retroactions as determinants of system dynamics and evolution. Beyond the study of processes under standardised conditions, it will also be necessary to characterise them in a diversity of environments (biotic, abiotic, contaminant) and contexts.

These challenges call for mathematical and computer modelling of dynamic systems, digital calculation, knowledge extraction and integration of heterogeneous data to calibrate and evaluate the quality of models. The integration of data from different sources requires progress on knowledge representation methods using ontologies, controlled vocabularies and networks, as well as on spatialization methods. Learning methods are also used.

Research themes

  • Characterization of the principles of organization, functioning and evolution in genomes, cells, organs and organisms;
  • Construction of molecular networks underlying biological functions and, in particular, multi-scale or multi-species metabolic networks;
  • Establishing links between gene(s) and form(s) by integrating geometric and physical constraints and associated retroaction;
  • Investigation of mechanisms underlying the adaptive responses of organisms and populations and the spatial and temporal scales of evolution of these responses.

Methodological challenges

  • Extract and represent knowledge in forms that allow hypothesis formalization and integration into models;
  • Search for causal relationships using statistical and automatic learning methods on large-scale data;
  • Develop multi-scale modeling, inference rules for systems, and simulation studies of complex dynamic systems describing living organisms;
  • Optimize the modeling and coupling of different models;
  • Identify "meta-mechanisms" as an alternative to coupling models between successive levels of organization.

In this folder

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

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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.

Logo consortium TIMe-ID

Interdisciplinary work on integrated methodologies for temporal data in biology

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The integration of temporal data in biology is a cutting-edge field of research that currently lacks operational methods. The purpose of the TIMe-ID consortium is to build an interdisciplinary community that can progress the development of such methods.

Comparative analysis of 3D genomics data

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Thesis by Elise Jorge (GenPhyse, 2023-2025). This thesis, at the interface between statistics, molecular biology and functional genomics, seeks to develop a differential analysis method for Hi-C data to identify regions in the 3-D genome structure where modifications occur.

Photo de chenille du maïs © INRAE, Buisson Christophe

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

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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 adn

An interdisciplinary network for 3D genomics

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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

Multiscale mathematical modeling of oogenesis in fish

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Thesis by Louis Fostier (PRC, 2022-2025). This thesis, which arises from the IMMO exploratory project, addresses the understanding and quantification of the ovarian dynamics of model fish with asynchronic ovogenesis. It will construct and calibrate a mathematical model integrating the maturation dynamics (and regulation ) of gametes over fish lifetimes.

Illustration thèse confinancée

Multi-omics regulation network inference via the Gaussian copula

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Thesis by Ekaterina Tomilina (MaIAGE, 2022-2025). Systems biology is built on the analysis of complex, large and highly diverse data networks. Understanding the interdependencies between, and regulation of, the different types of omics data it measures is a genuine challenge. This thesis proposes to use copula theory to construct, test and apply a coupled statistical model of this heterogeneity.

Modelling the heritability of traits from count data

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Understanding the genetic determinism of a trait, i.e. the set of genes involved in the development and expression of this trait, is a major challenge for better understanding biological processes and supporting genetic improvement programmes.

Sclerotinia sclerotiorum sur une tige de coco

Simulating plant-pathogen interactions to better understand plant immune responses

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Understanding how plants defend themselves against pathogens is a major challenge for moving towards an agriculture that uses fewer pesticides.

Cross Methodological Insights for Multi-source Data Integration

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In biology, as in other scientific fields, the integration of multi-source data is more relevant than ever. Indeed, the data collected are increasingly complex and their volume is growing, due to the development of analytical platforms, imaging techniques, the rise of omics data, etc

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Modification date: 28 October 2024 | Publication date: 10 January 2022 | By: Com