UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d'Ornon, France.
UR 1115 PSH, INRA, Avignon Cedex 9, France.
Ann Bot. 2018 Jun 28;122(1):1-21. doi: 10.1093/aob/mcy057.
One of the key goals of fruit biology is to understand the factors that influence fruit growth and quality, ultimately with a view to manipulating them for improvement of fruit traits.
Primary metabolism, which is not only essential for growth but is also a major component of fruit quality, is an obvious target for improvement. However, metabolism is a moving target that undergoes marked changes throughout fruit growth and ripening.
Agricultural practice and breeding have successfully improved fruit metabolic traits, but both face the complexity of the interplay between development, metabolism and the environment. Thus, more fundamental knowledge is needed to identify further strategies for the manipulation of fruit metabolism. Nearly two decades of post-genomics approaches involving transcriptomics, proteomics and/or metabolomics have generated a lot of information about the behaviour of fruit metabolic networks. Today, the emergence of modelling tools is providing the opportunity to turn this information into a mechanistic understanding of fruits, and ultimately to design better fruits. Since high-quality data are a key requirement in modelling, a range of must-have parameters and variables is proposed.
水果生物学的主要目标之一是了解影响水果生长和品质的因素，最终旨在通过操纵这些因素来改善水果的特性。
初级代谢是生长所必需的，也是果实品质的主要组成部分，显然是改良的目标。然而，新陈代谢是一个移动的目标，在整个果实生长和成熟过程中会发生明显的变化。
农业实践和育种已经成功地改善了水果的代谢特性，但两者都面临着发育、代谢和环境之间相互作用的复杂性。因此，需要更多的基础知识来确定进一步操纵水果代谢的策略。近二十年来，涉及转录组学、蛋白质组学和/或代谢组学的后基因组学方法已经产生了大量关于水果代谢网络行为的信息。如今，模型工具的出现为将这些信息转化为对水果的机制理解提供了机会，并最终设计出更好的水果。由于建模的关键要求是高质量的数据，因此提出了一系列必需的参数和变量。
