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Assessing inter-individual genetic variability in peach sugar metabolism through reliable parameter estimation of a kinetic model
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International audience. Integrating genetic information into dynamical models is crucial for understanding genotype variation and improving crop performance in the face of current agronomic and ecological constraints. Calibration over a large number of genotypes is a key step in building gene-to-phenotype models but remains challenging. We compared two strategies for calibrating an Ordinary Differential Equations (ODE) kinetic model simulating sugar accumulation during peach fruit development. In the first strategy, a Genotype-Based (GB) approach, the model was calibrated independently for each genotype, using either a Single-Objective Optimization (GBS) or a Multi-Objective Optimization (GBM) formulation. In the second strategy, a Population-Based (PB) approach, the model was calibrated for all genotypes simultaneously. The two strategies were applied to simulated data and to a real dataset from 106 peach genotypes. Results showed that the GB strategy allowed for a high goodness of fit for most genotypes, especially in the GBS formulation. However, the estimated parameters suffered from a lack of practical identifiability as independent repetitions of the estimation algorithm did not always converge to the same value for most genotypes. The PB calibration strategy overcame this issue showing a good identifiability of the population parameter values, a goodness of fit comparable to the one obtained with the GB strategy, and a good characterization of parameter variations within the progeny, which is key to assess the inter-individual genetic variability. This study demonstrates the value of the PB calibration strategy in capturing interindividual genetic variability, a critical step toward developing robust gene-to-phenotype models.
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