Processes 2018, 6,38
and omics datasets. The macromolecular composition of a cell is one such area of basic knowledge.
Macromolecular composition of both prokaryotic and eukaryotic cells is governed by allocation of
resources and can shift depending on cell cycle, specific growth rate, and diel cycle (e.g., cyanobacteria
and green algae) [4–6].
Stoichiometric modeling approaches analyze steady state fluxes based on metabolic reactions
identified from an 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], typically use production of biomass as an objective function,
and macromolecular composition dictates the metabolic precursors necessary for growth. Different
weightings of macromolecular components in the biomass synthesis reaction can influence results
by shifting requirements for precursors [11]. However, the proportions of biomass components are
not specified by the genome sequence [12]. While technologies for automatic model construction
are rapidly increasing, stoichiometric coefficients for the biomass reaction are still necessary [13].
Often, coefficients for this essential reaction are borrowed from literature reported for Escherichia coli or
an organism similar in physiology or phylogeny to the organism being modeled (e.g., [14,15]). However,
these values may not be representative of the organism under study. In biotechnology applications,
a specific macromolecular component may be targeted, such as lipids extracted for biofuels [16]
or starch compounds for biochemical production. Accurate quantification of these components is
important for comparison of production potential under different conditions. Additionally, ratios of
macromolecule pools, such as protein, DNA, or RNA, from a microbial population can be correlated to
important culture properties, including specific growth rate [17].
A variety of methods for quantification of any given macromolecule can be found in the literature
(e.g., [18]). Many of these methods date back several decades, and numerous adaptations have arisen
over the years. Selecting and implementing a method with an assurance of valid and accurate results
relevant to computational biology applications can present a significant challenge, particularly when
testing new organisms. Additionally, not all reported methods have been developed for or tested on
prokaryotes, and different organisms may respond differently to treatment conditions. For example,
cell wall type may influence the efficacy of reagents or procedures, resulting in a method with varying
degrees of efficiency for different types of microorganisms. External factors, such as materials used,
can also affect the outcome of an analysis, and specific procedural details not included in publications
can hinder reproducibility. Recently, methods for determining multiple biomass components with a
single technique, e.g., gas chromatography-mass spectrometry, have been developed [19] but still rely
on adequate cell lysis techniques and standard compounds for quantification. A concise collection
of information about the variety of existing methods for each macromolecule, including advantages
and disadvantages of methods, specific procedural details, and points for potential pitfalls, is a useful
resource that is lacking from the published literature.
The current work fills this gap with objectives: (1) to review and compare existing literature
regarding methods to measure five major macromolecules (carbohydrate, DNA, lipid, protein,
and RNA); (2) to develop a select step-by-step protocol for each macromolecule and test the efficacy on
different types of bacterial samples; and (3) to demonstrate the application to 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, thermophilic acidophile). These microorganisms encompass a range
of physiological capabilities and characteristics, including photosynthesis and alicyclic fatty acids.
The impact of biomass composition on model predictions was demonstrated using essential parameters,
including biomass yield on electron donor, biomass yield on electron acceptor, biomass yield on
nitrogen, biomass degree of reduction, and growth associated maintenance energy. The results
highlight the importance of appropriate methods for the accurate determination of macromolecule
155
and omics datasets. The macromolecular composition of a cell is one such area of basic knowledge.
Macromolecular composition of both prokaryotic and eukaryotic cells is governed by allocation of
resources and can shift depending on cell cycle, specific growth rate, and diel cycle (e.g., cyanobacteria
and green algae) [4–6].
Stoichiometric modeling approaches analyze steady state fluxes based on metabolic reactions
identified from an 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], typically use production of biomass as an objective function,
and macromolecular composition dictates the metabolic precursors necessary for growth. Different
weightings of macromolecular components in the biomass synthesis reaction can influence results
by shifting requirements for precursors [11]. However, the proportions of biomass components are
not specified by the genome sequence [12]. While technologies for automatic model construction
are rapidly increasing, stoichiometric coefficients for the biomass reaction are still necessary [13].
Often, coefficients for this essential reaction are borrowed from literature reported for Escherichia coli or
an organism similar in physiology or phylogeny to the organism being modeled (e.g., [14,15]). However,
these values may not be representative of the organism under study. In biotechnology applications,
a specific macromolecular component may be targeted, such as lipids extracted for biofuels [16]
or starch compounds for biochemical production. Accurate quantification of these components is
important for comparison of production potential under different conditions. Additionally, ratios of
macromolecule pools, such as protein, DNA, or RNA, from a microbial population can be correlated to
important culture properties, including specific growth rate [17].
A variety of methods for quantification of any given macromolecule can be found in the literature
(e.g., [18]). Many of these methods date back several decades, and numerous adaptations have arisen
over the years. Selecting and implementing a method with an assurance of valid and accurate results
relevant to computational biology applications can present a significant challenge, particularly when
testing new organisms. Additionally, not all reported methods have been developed for or tested on
prokaryotes, and different organisms may respond differently to treatment conditions. For example,
cell wall type may influence the efficacy of reagents or procedures, resulting in a method with varying
degrees of efficiency for different types of microorganisms. External factors, such as materials used,
can also affect the outcome of an analysis, and specific procedural details not included in publications
can hinder reproducibility. Recently, methods for determining multiple biomass components with a
single technique, e.g., gas chromatography-mass spectrometry, have been developed [19] but still rely
on adequate cell lysis techniques and standard compounds for quantification. A concise collection
of information about the variety of existing methods for each macromolecule, including advantages
and disadvantages of methods, specific procedural details, and points for potential pitfalls, is a useful
resource that is lacking from the published literature.
The current work fills this gap with objectives: (1) to review and compare existing literature
regarding methods to measure five major macromolecules (carbohydrate, DNA, lipid, protein,
and RNA); (2) to develop a select step-by-step protocol for each macromolecule and test the efficacy on
different types of bacterial samples; and (3) to demonstrate the application to 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, thermophilic acidophile). These microorganisms encompass a range
of physiological capabilities and characteristics, including photosynthesis and alicyclic fatty acids.
The impact of biomass composition on model predictions was demonstrated using essential parameters,
including biomass yield on electron donor, biomass yield on electron acceptor, biomass yield on
nitrogen, biomass degree of reduction, and growth associated maintenance energy. The results
highlight the importance of appropriate methods for the accurate determination of macromolecule
155