Several studies have been conducted to understand the dynamic of primary metabolisms in fruit by translating them into mathematics models. An ODE kinetic model of sugar metabolism has been developed by Desnoues et al. (2018) to simulate the accumulation of different sugars during peach fruit development. Two major drawbacks of this model are (a) the number of parameters to calibrate and (b) its integration time that can be long due to non-linearity and time-dependent input functions. Together, these issues hamper the use of the model for a large panel of genotypes, for which few data are available. In this paper, we present a model reduction scheme that explicitly addresses the specificity of genetic studies in that: (i) it yields a reduced model that is adapted to the whole expected genetic diversity (ii) it maintains network structure and variable identity, in order to facilitate biological interpretation. The proposed approach is based on the combination and the systematic evaluation of different reduction methods. Thus, we combined multivariate sensitivity analysis, structural simplification and timescale-based approaches to simplify the number and the structure of ordinary differential equations of the model. The original and reduced models were compared based on three criteria, namely the corrected Aikake Information Criterion (AICC), the calibration time and the expected error of the reduced model over a progeny of virtual genotypes. The resulting reduced model not only reproduces the predictions of the original one but presents many advantages including a reduced number of parameters to be estimated and shorter calibration time, opening new promising perspectives for genetic studies and virtual breeding. The validity of the reduced model was further evaluated by calibration on 30 additional genotypes of an inter-specific peach progeny for which few data were available.

中文翻译：

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减少桃子糖代谢的模型以捕捉基因型之间的不同模式。

通过将其转化为数学模型，已经进行了一些研究来了解水果中主要代谢的动态。Desnoues等人已经开发了糖代谢的ODE动力学模型。（2018）模拟桃果实发育过程中不同糖的积累。该模型的两个主要缺点是（a）校准参数的数量和（b）由于非线性和与时间有关的输入函数，其积分时间可能会很长。总之，这些问题阻碍了该模型对大量基因型的使用，而该基因型的可用数据很少。在本文中，我们提出了一种模型简化方案，该方案明确解决了遗传研究的特殊性：（i）产生适合整个预期遗传多样性的简化模型（ii）维持网络结构和可变身份，以便于生物学解释。所提出的方法基于不同还原方法的组合和系统评估。因此，我们结合了多元敏感性分析，结构简化和基于时标的方法，以简化模型常微分方程的数量和结构。基于三个标准对原始模型和简化模型进行了比较，即校正后的Aikake信息准则（AICC），校准时间和虚拟基因型后代的简化模型的预期误差。最终的简化模型不仅可以再现原始模型的预测，而且还具有许多优势，包括减少了估计参数的数量和缩短了校准时间，为遗传研究和虚拟育种开辟了新的前景。通过对种间桃子后代的30种其他基因型进行校准，进一步评估了简化模型的有效性，但该数据很少。