Nogales2012 - Genome-scale metabolic network of Synechocystis sp. PCC6803 (iJN678)

Nogales2012 - Genome-scale metabolic network
of Synechocystis sp. (iJN678)

This model is described in the article:

Detailing the optimality of
photosynthesis in cyanobacteria through systems biology
analysis.

Nogales J, Gudmundsson S, Knight EM,
Palsson BO, Thiele I.
Proc. Natl. Acad. Sci. U.S.A. 2012 Feb;
109(7): 2678-2683
Abstract:

Photosynthesis has recently gained considerable attention
for its potential role in the development of renewable energy
sources. Optimizing photosynthetic organisms for biomass or
biofuel production will therefore require a systems
understanding of photosynthetic processes. We reconstructed a
high-quality genome-scale metabolic network for Synechocystis
sp. PCC6803 that describes key photosynthetic processes in
mechanistic detail. We performed an exhaustive in silico
analysis of the reconstructed photosynthetic process under
different light and inorganic carbon (Ci) conditions as well as
under genetic perturbations. Our key results include the
following. (i) We identified two main states of the
photosynthetic apparatus: a Ci-limited state and a
light-limited state. (ii) We discovered nine alternative
electron flow pathways that assist the photosynthetic linear
electron flow in optimizing the photosynthesis performance.
(iii) A high degree of cooperativity between alternative
pathways was found to be critical for optimal autotrophic
metabolism. Although pathways with high photosynthetic yield
exist for optimizing growth under suboptimal light conditions,
pathways with low photosynthetic yield guarantee optimal growth
under excessive light or Ci limitation. (iv) Photorespiration
was found to be essential for the optimal photosynthetic
process, clarifying its role in high-light acclimation.
Finally, (v) an extremely high photosynthetic robustness drives
the optimal autotrophic metabolism at the expense of metabolic
versatility and robustness. The results and modeling approach
presented here may promote a better understanding of the
photosynthetic process. They can also guide bioengineering
projects toward optimal biofuel production in photosynthetic
organisms.

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Nogales2012 - 集胞藻（Synechocystis sp.）的全基因组规模代谢网络（genome-scale metabolic network）模型（iJN678）

本模型的相关内容发表于以下文章：

《通过系统生物学分析解析蓝细菌光合作用的最优性》

作者：Nogales J、Gudmundsson S、Knight EM、Palsson BO、Thiele I

发表于《美国国家科学院院刊》（Proc. Natl. Acad. Sci. U.S.A.），2012年2月，第109卷第7期，页码2678-2683

摘要：

光合作用近年来因在可再生能源开发中的潜在作用受到广泛关注。为优化光合生物的生物质或生物燃料生产，需对光合过程开展系统层面的解析。我们针对集胞藻PCC6803重构了高质量的全基因组规模代谢网络，从机制细节层面刻画了核心光合过程。我们针对该重构的光合过程，在不同光照、无机碳（inorganic carbon, Ci）条件及遗传扰动下开展了全面的虚拟（in silico）分析。主要研究结果如下：
（1）鉴定出光合装置的两种核心状态：无机碳限制状态与光照限制状态；
（2）发现9条替代电子流通路，可辅助光合线性电子流以优化光合性能；
（3）证实替代通路间的高度协同性对最优自养代谢运行至关重要。尽管在亚最优光照条件下存在高光合产率通路以优化生长，但低光合产率通路可在光照过剩或无机碳限制条件下保障最优生长；
（4）光呼吸（photorespiration）对最优光合过程不可或缺，阐明了其在高光适应中的作用；
（5）极高的光合鲁棒性以牺牲代谢灵活性与代谢鲁棒性为代价，驱动了最优自养代谢。

本研究的结果与建模方法可增进学界对光合过程的理解，同时可为光合生物的生物工程改造以实现最优生物燃料生产提供指导依据。

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