processes
Article
Measuring Cellular Biomass Composition for
Computational Biology Applications
Ashley E. Beck 1 , Kristopher A. Hunt 2 and Ross P. Carlson 3, *
1
Microbiology and Immunology, Center for Biofilm Engineering, Montana State University,
Bozeman, MT 59717, USA; ashley.beck@montana.edu
2
Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA;
hunt0362@uw.edu
3
Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University,
Bozeman, MT 59717, USA
* Correspondence: rossc@montana.edu; Tel.: +1-406-994-3631
Received: 27 January 2018; Accepted: 17 April 2018; Published: 24 April 2018
Abstract: 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.
Keywords: biomass reaction; computational biology; macromolecular composition; metabolic
model; methods
1. Introduction
The in silico study of metabolism has largely transitioned from a specialty discipline to a
mainstream biological approach due to improvements in software usability, increases in computational
power, and the accumulation of omics databases. Cellular growth is an essential component of many of
these computational biology studies [1–3]. Understanding the foundation of growth from the level of
mass and energy fluxes remains critical for interpretation and integration of in silico metabolic models
Processes 2018, 6, 38; doi:10.3390/pr6050038
www.mdpi.com/journal/processes
154
Article
Measuring Cellular Biomass Composition for
Computational Biology Applications
Ashley E. Beck 1 , Kristopher A. Hunt 2 and Ross P. Carlson 3, *
1
Microbiology and Immunology, Center for Biofilm Engineering, Montana State University,
Bozeman, MT 59717, USA; ashley.beck@montana.edu
2
Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA;
hunt0362@uw.edu
3
Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University,
Bozeman, MT 59717, USA
* Correspondence: rossc@montana.edu; Tel.: +1-406-994-3631
Received: 27 January 2018; Accepted: 17 April 2018; Published: 24 April 2018
Abstract: 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.
Keywords: biomass reaction; computational biology; macromolecular composition; metabolic
model; methods
1. Introduction
The in silico study of metabolism has largely transitioned from a specialty discipline to a
mainstream biological approach due to improvements in software usability, increases in computational
power, and the accumulation of omics databases. Cellular growth is an essential component of many of
these computational biology studies [1–3]. Understanding the foundation of growth from the level of
mass and energy fluxes remains critical for interpretation and integration of in silico metabolic models
Processes 2018, 6, 38; doi:10.3390/pr6050038
www.mdpi.com/journal/processes
154