Illustration prédire les phénotypes

Predict

Predict

Axis 2: Phenotype prediction

This axis deals with the prediction of phenotypes from the cell to the individual and population levels, their functionalities and responses to changes in stress fields (biotic and abiotic environment, management methods, practices).

One of the challenges of digital biology is to develop, compare and improve learning methods, adapting them to integrate multi-source data (omics, sensors, environment, data from participatory approaches). The modelling of biological and physiological processes to develop predictive approaches and the simulation of complex biological systems are also major challenges. In particular, the robustness of the models built in axis 1 must be tested by subjecting them to fluctuating conditions (internal or external).

This research includes predictive approaches based on modelling, data assimilation and the coupling of "data-driven" and "concept-driven" approaches, taking into account the essential dimensions of prediction, i.e. planning, quantification of uncertainties and evaluation of forecasts.

Research themes

  • Genomic prediction;
  • Take into account all transmissible factors - whether genetic or not - in predicting the potential of an individual to transmit its characteristics to its descendants;
  • The application of these methods to identify bio-markers of the state or potential of organisms (level of resistance, level of stress, etc.), or genetic targets for the optimisation of metabolic networks;
  • Predict the impacts of complex chemical and biological exposure scenarios on physiology (from the cell to the individual level);
  • The mobilisation of information regarding the state (phenotype, genome, environment) and knowledge of processes at organism (and sub-organism) levels to predict the functioning and trajectories of populations at different spatial and/or temporal scales.

Methodological challenges

  • Select the learning methods best suited to a given problem, determine their contributions and limitations for detecting complex structures or weak signals in the data;
  • Build and simulate integrative models with good predictive capabilities, allowing to distinguish the effects of confounding factors in the data;
  • Access unobservable quantities through model inversion (e.g. trait value estimation);
  • Improve the quality and robustness of predictions (with the underlying issues of planning and sampling, uncertainty assessment, taking account of changing environments).

In this folder

Fermentwin © Freepik
Gene transcription is an essential process in the adaptive response of plants to environmental constraints. The interdisciplinary scientific consortium PRECURSOR aims to investigate and better understand how this process takes place in the proximal regions of genes to ultimately improve the predictive power of selection models.
Jumeaux numériques de systèmes microbiens © katemangostar, Freepik
The Arte-mis consortium will bring together an interdisciplinary community of researchers working at the interface between the experimental and digital sciences to overcome methodological barriers to the creation of digital twins in microbial ecology.
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.
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.
A major issue in in vitro fertilisation (IVF) is the selection of the "best" embryo, i.e. the one most likely to implant in the uterus. The objective of the BovMovie2Pred consortium is to propose solutions to assist in the selection of bovine embryos in order to increase the percentage of viable births from in vitro produced embryos.
Photo Acyrthosiphon pisum
Today, agriculture faces many challenges, including to avoid the development of certain pathogens resulting from the reduction in the use of inputs with a view to sustainable agriculture as well as the effects of climate change. In this context, many questions arise in the short term about the adaptive capacities of these bio-aggressors. Will an insect pest resist the next heat wave? Or will it instead be greatly affected by rising temperatures and cease to be a threat ?
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
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
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.
Photo vache dans une étable
In plant and animal genetics, selection programmes aim to identify individuals whose performance (yield, resistance to disease or environmental stress) meets previously defined criteria. This selection requires the acquisition of data, in the field or in breeding, which can be costly or time-consuming.
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Modification date: 06 October 2023 | Publication date: 10 January 2022 | By: Com