Measuring Cellular Biomass Composition for Computational Biology Applications
Measuring Cellular Biomass Composition for Computational Biology Applications
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DOI：
10.3390/pr6050038
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发表时间：
2018-05-01
期刊：
影响因子：
3.5
通讯作者：
Carlson, Ross P.
中科院分区：
文献类型：
--
作者：
Beck, Ashley E.;Hunt, Kristopher A.;Carlson, Ross P.
新陈代谢的计算表示在医学、环境和生物过程应用中越来越常见。细胞生长通常是计算生物学分析的重要输出，因此，生物量成分的准确测量对于相关模型预测至关重要。明显缺乏详细的大分子测量方案，包括与替代测定和方法的比较，以及将实验数据转换为计算生物学应用的生化反应的工具。本文汇编了有关五种主要细胞大分子（碳水化合物、DNA、脂质、蛋白质和 RNA）方法的简明文献综述，并为每种大分子提供了选择方法的分步方案。此外，每种方法都在三种不同的细菌物种上进行了测试，并给出了故障排除和测试新物种的建议。大分子组成测量用于构建具有适当质量控制指标的生物质合成反应，例如常见计算生物学方法的元素平衡，包括通量平衡分析和基本通量模式分析。最后，事实证明，生物量组成可以极大地影响基本模型的预测。这里量化了生物量组成对计算机预测的影响，包括电子供体的生物量产量、电子受体的生物量产量、氮的生物量产量和生物量减少程度，以及与生长相关的维持能量的计算；这些参数在参考生物质组合物和十种测试生物质组合物之间分别变化高达7%、70%、35%、12%和40%。目前的工作通过回顾有关各种常见分析测量的文献、开发详细的程序、在实验室测试方法并将结果应用于代谢模型，所有这些都在一个公开可用的资源中，进一步推动了计算生物学界的发展。
Computational representations of metabolism are increasingly common in medical, environmental, and bioprocess applications. Cellular growth is often an important output of computational biology analyses, and therefore, accurate measurement of biomass constituents is critical for relevant model predictions. There is a distinct lack of detailed macromolecular measurement protocols, including comparisons to alternative assays and methodologies, as well as tools to convert the experimental data into biochemical reactions for computational biology applications. Herein is compiled a concise literature review regarding methods for five major cellular macromolecules (carbohydrate, DNA, lipid, protein, and RNA) with a step-by-step protocol for a select method provided for each macromolecule. Additionally, each method was tested on three different bacterial species, and recommendations for troubleshooting and testing new species are given. The macromolecular composition measurements were used to construct biomass synthesis reactions with appropriate quality control metrics such as elemental balancing for common computational biology methods, including flux balance analysis and elementary flux mode analysis. Finally, it was demonstrated that biomass composition can substantially affect fundamental model predictions. The effects of biomass composition on in silico predictions were quantified here for biomass yield on electron donor, biomass yield on electron acceptor, biomass yield on nitrogen, and biomass degree of reduction, as well as the calculation of growth associated maintenance energy; these parameters varied up to 7%, 70%, 35%, 12%, and 40%, respectively, between the reference biomass composition and ten test biomass compositions. The current work furthers the computational biology community by reviewing literature regarding a variety of common analytical measurements, developing detailed procedures, testing the methods in the laboratory, and applying the results to metabolic models, all in one publicly available resource.