How To Calculate Biomass

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How To Calculate Biomass

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By Ashley E. Beck Ashley E. Beck Scilit Google Scholar 1, Kristopher A. Hunt Kristopher A. Hunt Scilit Google Scholar 2 and Ross P. Carlson Ross P. Carlson Scilit Google Scholar 3, *

Received: 27 January 2018 / Revised: 6 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018

Pdf] Biomass Factors Used To Calculate Carbon Storage Of Turkish Forests

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 thus accurate measurement of biomass constituents is essential for relevant model predictions. There is a lack of detailed macromolecular measurement protocols, including comparison of alternative assays and methods, as well as tools to convert experimental data into biochemical reactions for computational biology applications. A brief literature review of methods for five major cellular macromolecules (carbohydrates, DNA, lipids, proteins and RNA) is compiled here, with a step-by-step protocol for the selection method indicated for each macromolecule. In addition, each method was tested on three different bacterial species, and recommendations were given for troubleshooting and testing new species. Macromolecular composition measurements are used to construct biomass synthesis reactions with appropriate quality control metrics such as elemental balance for general computational biology methods including flux balance analysis and fundamental flux state analysis. Finally, it is proven that biomass composition can affect the predictions of the basic model. The effect of biomass composition on in silico predictions is estimated here for biomass yield in electron donors, biomass yield in electron acceptors, biomass yield in nitrogen and biomass reduction rate, as well as calculation of maintenance energy related to growth; these parameters vary by 7%, 70%, 35%, 12% and 40% between the reference biomass composition and the ten test biomass compositions, respectively. Current work extends the computational biology community by reviewing the literature on a range of common analytical measurements, developing detailed procedures, testing methods in the laboratory, and applying the results to metabolic models, all in one publicly available resource.

The in silico study of metabolism has largely transitioned from a specialized discipline to a mainstream biological approach due to improvements in the use of software, increases in computing power, and the accumulation of omics databases. Cell growth is an important component of many of these computational biology studies [ 1 , 2 , 3 ]. Understanding the growth basis of mass and energy flux rates remains critical to the interpretation and integration of in silico metabolic models and omics datasets. The macromolecular composition of cells is an area of ​​basic knowledge. The macromolecular composition of prokaryotic and eukaryotic cells is regulated by resource allocation and can change depending on cell cycle, specific growth rate and diel cycle (e.g. cyanobacteria and green algae) [ 4 , 5 , 6 ].

The stoichiometric modeling approach analyzes steady-state fluxes based on metabolic reactions identified from the organism’s genomic potential, enzyme-coding genes identified in the genome sequence [ 7 ]. These methods can be applied to microbial communities as well as individual species [8, 9]. Optimal metabolic pathways are often assessed in terms of growth: constraint-based approaches, such as flux balance analysis [10], usually use biomass production as an objective function, and macromolecular composition dictates the metabolic precursors required for growth. The different weights of the macromolecular components in the biomass synthesis reaction can affect the results by changing the requirements for precursors [11]. However, the proportion of biomass components is not determined by the genome sequence [12]. While the technology for automatic model construction is increasing rapidly, stoichiometric coefficients for biomass reactions are still needed [13]. Often, the coefficients for these important reactions are borrowed from the reported literature for Escherichia coli or organisms similar in physiology or phylogeny to the modeled organism (e.g. [ 14 , 15 ]). However, these values ​​may not reflect the organisms studied. In biotechnological applications, specific macromolecular components can be targeted, such as extracted lipids for biofuels [16] or starch compounds for biochemical production. Accurate quantification of these components is essential to compare production potential under different conditions. In addition, the ratio of the macromolecular pool, such as protein, DNA, or RNA, in a microbial population can be associated with important cultural traits, including specific growth rates [ 17 ].

Different methods to quantify specific macromolecules can be found in the literature (e.g. [18]). Many of these methods date back decades, and many adaptations have emerged over the years. Selecting and implementing methods that ensure valid and accurate results relevant to computational biology applications can be a significant challenge, especially when testing new organisms. Additionally, not all reported methods have been developed or tested in prokaryotes, and different organisms may respond differently to treatment conditions. For example, the type of cell wall can affect the effectiveness of a reagent or procedure, leading to different levels of effectiveness for different types of microorganisms. External factors, such as the materials used, can also influence the results of the analysis, and specific procedural details not included in the publication can hinder reproducibility. Recently, a method has been developed to determine different components of biomass with a single technique, for example gas chromatography-mass spectrometry, [19] but it still depends on appropriate cell lysis techniques and standard compounds for quantification. A concise collection of information on the various methods available for each macromolecule, including advantages and disadvantages of the method, specific procedural details and pointing out potential pitfalls, is a useful resource lacking in the published literature.

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The present work fills this gap by aiming: (1) to review and compare the existing literature on how to measure the Big Five macromolecules (carbohydrates, DNA, lipids, proteins, and RNA); (2) to develop a protocol of selected steps for each macromolecule and test its effectiveness in different types of bacterial samples; and (3) to demonstrate applications in computational biology by generating biomass synthesis reactions. Three bacterial species were used as test cases in the current work: E. coli (Gram-negative, mesophilic model laboratory organism), Synechococcus sp. PCC 7002 (Gram-negative, mesophilic cyanobacterium; Synechococcus 7002 hereafter) and Alicyclobacillus acidocaldarius (Gram-positive, acidophilic thermophile). These microorganisms include various physiological capabilities and characteristics, including photosynthesis and alicyclic fatty acids. The impact of biomass composition on model predictions is shown using important parameters including biomass yield in electron donors, biomass yield in electron acceptors, biomass yield in nitrogen, biomass reduction rate and maintenance energy related to growth. These results highlight the importance of appropriate methods for accurate determination of macromolecular composition. Bringing together literature reviews along with laboratory-tested protocols with demonstrated applications to metabolic models, all in a single source, is a useful resource for the computational biology community to facilitate the transparency and reproducibility of model building.

Synechococcus 7002 was grown in A+ synthetic seawater medium (18 g/L NaCl, 0.6 g/L KCl, 1 g/L NaNO

O, 1 g/L Tris pH 8.2), supplemented with 4 mg/L vitamin B12 and 1 mL/L P1 mixed metal (34.26 g/L H)

E. coli cultures were grown at 37°C shaking at 150 rpm. Inoculum cultures were prepared in 8 ml M9 glucose + 10 g/L in disposable culture tubes, inoculated with multiple colonies isolated from agar plates streaked from a 20% glycerol frozen stock solution and grown to OD.

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<0.6 (exponential phase). Cells were then centrifuged at 4000 rpm for 10 min and resuspended in OD.

~0.05 in 50 mL fresh M9 + 1 g/L glucose in a 250 mL shake flask. Cultures were grown until OD

~0.6 (exponential phase) and then harvested for analysis (collected in a 50 ml polypropylene centrifuge tube cooled on ice and then centrifuged).

Synechococcus 7002 cultures were grown at 38°C without shaking in continuous light. Inoculum cultures were prepared in 25 ml A+ medium in undisturbed 250 ml shake flasks, inoculated with multiple colonies isolated from agar plates (transferred monthly for propagation) and grown to OD.

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