Processes 2018, 6,38
Figure A4. Correlations between experimentally measured and genome-based amino acid distributions
for: (a) E. coli (NCBI reference sequence NC_000913.3; trendline y = 0.81x + 1.04 with R 2 value of 0.82);
(b) Synechococcus 7002 (NCBI reference sequence NC_010475.1; trendline y = 0.81x + 1.07 with R 2 value
of 0.85); and (c) A. acidocaldarius (NCBI reference sequence NC_013205.1; trendline y = 0.87x + 0.73 with
R 2 value of 0.87). The two most outlying amino acids are labeled.
References
1.
Feist, A.M.; Palsson, B.O. The biomass objective function. Curr. Opin. Microbiol. 2010, 13, 344–349. [CrossRef]
[PubMed]
2.
Hunt, K.A.; Jennings, R.D.; Inskeep, W.P.; Carlson, R.P. Stoichiometric modelling of assimilatory and
dissimilatory biomass utilisation in a microbial community. Environ. Microbiol. 2016, 18, 4946–4960.
[CrossRef][PubMed]
3.
Pramanik, J.; Keasling, J.D. Stoichiometric model of Escherichia coli metabolism: Incorporation of growth-rate
dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng. 1997, 56, 398–421.
[CrossRef]
4.
White, D.; Drummond, J.; Fuqua, C. The Physiology and Biochemistry of Prokaryotes, 4th ed.; Oxford University
Press: New York, NY, USA, 2012.
5.
Vrede, T.; Dobberfuhl, D.R.; Kooijman, S.; Elser, J.J. Fundamental connections among organism C: N: P
stoichiometry, macromolecular composition, and growth. Ecology 2004, 85, 1217–1229. [CrossRef]
6.
Zuniga, C.; Levering, J.; Antoniewicz, M.R.; Guarnieri, M.T.; Betenbaugh, M.J.; Zengler, K. Predicting
dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. Plant Physiol. 2017.[CrossRef]
7.
Maarleveld, T.R.; Khandelwal, R.A.; Olivier, B.G.; Teusink, B.; Bruggeman, F.J. Basic concepts and principles
of stoichiometric modeling of metabolic networks. Biotechnol. J. 2013, 8, 997–1008. [CrossRef][PubMed]
176
Figure A4. Correlations between experimentally measured and genome-based amino acid distributions
for: (a) E. coli (NCBI reference sequence NC_000913.3; trendline y = 0.81x + 1.04 with R 2 value of 0.82);
(b) Synechococcus 7002 (NCBI reference sequence NC_010475.1; trendline y = 0.81x + 1.07 with R 2 value
of 0.85); and (c) A. acidocaldarius (NCBI reference sequence NC_013205.1; trendline y = 0.87x + 0.73 with
R 2 value of 0.87). The two most outlying amino acids are labeled.
References
1.
Feist, A.M.; Palsson, B.O. The biomass objective function. Curr. Opin. Microbiol. 2010, 13, 344–349. [CrossRef]
[PubMed]
2.
Hunt, K.A.; Jennings, R.D.; Inskeep, W.P.; Carlson, R.P. Stoichiometric modelling of assimilatory and
dissimilatory biomass utilisation in a microbial community. Environ. Microbiol. 2016, 18, 4946–4960.
[CrossRef][PubMed]
3.
Pramanik, J.; Keasling, J.D. Stoichiometric model of Escherichia coli metabolism: Incorporation of growth-rate
dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng. 1997, 56, 398–421.
[CrossRef]
4.
White, D.; Drummond, J.; Fuqua, C. The Physiology and Biochemistry of Prokaryotes, 4th ed.; Oxford University
Press: New York, NY, USA, 2012.
5.
Vrede, T.; Dobberfuhl, D.R.; Kooijman, S.; Elser, J.J. Fundamental connections among organism C: N: P
stoichiometry, macromolecular composition, and growth. Ecology 2004, 85, 1217–1229. [CrossRef]
6.
Zuniga, C.; Levering, J.; Antoniewicz, M.R.; Guarnieri, M.T.; Betenbaugh, M.J.; Zengler, K. Predicting
dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. Plant Physiol. 2017.[CrossRef]
7.
Maarleveld, T.R.; Khandelwal, R.A.; Olivier, B.G.; Teusink, B.; Bruggeman, F.J. Basic concepts and principles
of stoichiometric modeling of metabolic networks. Biotechnol. J. 2013, 8, 997–1008. [CrossRef][PubMed]
176