A quantitative description of light-limited cyanobacterial growth using flux balance analysis.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2024-08-05 eCollection Date: 2024-08-01 DOI:10.1371/journal.pcbi.1012280
Rune Höper, Daria Komkova, Tomáš Zavřel, Ralf Steuer
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Abstract

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

利用通量平衡分析定量描述光照受限蓝藻的生长。
光养蓝藻的新陈代谢是全球生物地球化学循环不可分割的一部分,蓝藻将大气中的二氧化碳同化为有机碳的能力在可持续生物技术中具有多方面的潜在应用。为了阐明蓝藻代谢和生长的特性,基因组尺度代谢网络的计算重建发挥着越来越重要的作用。在此,我们介绍了蓝藻 Synechocystis sp. PCC 6803 代谢网络的最新重建情况,并利用通量平衡分析(FBA)对其进行了定量评估。为了克服传统通量平衡分析法的局限性,并允许整合实验分析,我们在通量平衡分析法的框架内开发了一种描述光吸收和光利用的新方法。我们的方法将光抑制和可变量子产率纳入了基于约束的光受限光营养生长描述中。我们的研究表明,由此产生的模型能够预测蓝藻生长的定量特性,包括光合作用氧进化以及生长和细胞维持所需的 ATP/NADPH 比率。我们的方法保留了 FBA 在计算和概念上的简易性,并可随时应用于其他光营养微生物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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