聚珠球菌突变体pcc7002的生长:多参数影响及生长速率预测

N. F. M. Azmin, Atikah Mohamed Sharikh, Ummi S. Halmi Shari, A. S. Azmi
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引用次数: 0

摘要

了解综合变量对蓝藻生长速度的相关影响是开发蓝藻作为生产生物燃料的生物机制的基础。蓝藻(蓝绿藻)是一种光养微生物,具有吸引人的好处,其中包括将二氧化碳直接转化为一系列有价值的产品,如碳基生物燃料。蓝藻物种的一个模型是聚藻蓝藻球菌sp. pcc7002。本文描述了为研究这些变量对聚球菌pcc7002生长的综合影响而建立的模型。研究的变量包括培养基温度、光照强度、NaNO3浓度和NPK浓度。该数据来自一项实验室规模的研究,在该研究中,聚珠球菌pcc7002进行了诱变程序。假设这些变量的某种组合在决定聚珠球菌sp. 7002的生长速率中起关键作用。生长速率是通过测量四个响应变量,碳水化合物浓度,二氧化碳吸收百分比,细胞干重(CDW)和光密度(OD)来确定的。开发了一个多元PCA模型,揭示了变量之间的潜在关系。该模型得到了令人满意的结果。PCA模型清晰地描述了各变量之间的显著相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Growth of Mutant Synechococcus SP. PCC 7002: Effects of Multi-Parameters and Prediction of Growth Rate
Understanding of the correlative effects of combined variables on the growth rate of the cyanobacteria is fundamental to the exploitation of cyanobacteria as a biological mechanism to produce biofuels. Cyanobacteria (blue-green algae) are phototrophic microorganisms that offers attractive benefits, among which is a direct conversion of CO2 to a range of valuable products such as carbon-based biofuels. One model of cyanobacteria species is the cyanobacterium Synechococcus sp. PCC 7002. This paper describes the model developed to investigate the combined impacts of the variables on the growth of the Synechococcus sp. PCC 7002. The variables understudy include the temperature of the media, light intensity, the concentration of NaNO3, and the concentration of the NPK. The data is obtained from a lab scale study in which the Synechococcus sp. PCC 7002 underwent mutagenesis procedures. It is hypotheses that certain combination of the variables plays a key role in determining the growth rate of Synechococcus sp. 7002. The growth rate is determined through the measurement of four response variables, carbohydrate concentration, percentage of CO2 uptake, cell dry weight (CDW), and optical density (OD). A multivariate PCA model was developed which unearths the underlying relationship between the variables. Promising results were yield from the proposed model. Distinctive correlations between the variables were clearly described by the PCA model.
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