Department of Plant Pathology, Physiology, and Weed Science, 308 Latham Hall, Virginia Tech, Blacksburg, VA, USA.
Plant Sci. 2012 Aug;191-192:53-70. doi: 10.1016/j.plantsci.2012.04.010. Epub 2012 May 3.
As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.
随着各种类型的高通量数据的生成变得更加容易和快速，人们期望至少在主要的原核和真核模式系统中，我们现在应该对生物学、生物化学和生理学有一个全面的系统级理解。尽管有大量可用的数据，但从全局角度来看，我们只能看到分子水平上发生的事情。主要原因是细胞的复杂性很高，而我们识别几乎无限数量的内部和外部条件下所有（或至少是重要的）成分及其相互作用的能力有限。可以使用基因组规模模型 (GEM) 对代谢进行数学建模。GEM 是从可用基因组注释中得出的计算机代谢通量模型。这些模型根据内部和外部信号的影响，预测给定定义的代谢网络的通量值组合。GEM 已经成功地用于细菌代谢建模超过十年。然而，直到 2009 年，才生成了第一个拟南芥悬浮细胞培养的 GEM。在植物中进行基因组规模建模（“GEMing”）带来了新的挑战，主要是由于存在以下因素导致的缺失成分和植物细胞的复杂性：（i）光合作用；（ii）区室化；（iii）各种细胞和组织类型；以及（iv）对环境和发育线索以及病原体、昆虫和竞争杂草的多样化代谢反应。本文批判性地讨论了现有植物 GEM 的优势，同时确定了未来改进的关键目标。植物 GEM 往往能够准确预测中心碳代谢选定方面的定性变化，而次要代谢则主要被忽视，主要是因为缺失（未知）基因和代谢物。因此，这些模型适用于探索在有利条件下生长的植物中的代谢，但不适用于必须应对复杂生态系统中环境变化的田间生长的植物。AraGEM 是第一个描述光合作用和光呼吸植物细胞（拟南芥）的 GEM。我们展示了在当前（有限）植物代谢知识的基础上使用 AraGEM，并通过一系列计算机模拟揭示了 AraGEM 的出人意料的稳健性。这些模拟的主要重点是评估：（i）网络连通性；（ii）CO₂ 和光子摄取率对细胞生长速率和单个生物量成分产生的影响；以及（iii）内部 pH 变化对植物中心碳代谢的稳定性。
