Theoretical Limits of Growth Yields and an Analysis of Experimental Data

  • Wolfgang Babel
  • Henk W. van Verseveld
Chapter

Abstract

Substrates for heterotrophic growth are carbon and energy sources at the same time. Hence it follows that, depending on the carbon/energy ratio, growth may be carbon- or energy-limited. From a physical-energy point of view, hydrocarbons and alcohols are energy excess substrates [1–4], i.e, firstly growth obtained on these substrates should be carbon-limited and secondly, substrate carbon might be completely incorporated into the “cell molecule” without losing carbon in the form of CO2 for the purpose of energy generation (Table 1). However, during synthesis of amino acids and other cell constituents carbon gets lost as CO2 due to unavoidable decarboxylations and a 100% carbon conversion efficiency is never possible. Theoretical calculations [5] show that, almost independent of cell composition, the upper limit of carbon incorporation into the “cell molecule” amount to about 85% with glycolytic substrates and to approximately 75% with non-glycolytic, gluconeogenetic or C2 substrates (Table 2). Substrates are defined as glycolytic when a C3 compound is formed as central precursor for the synthesis of all cell components without losing carbon, and as non-glycolytic when CO2 is released along this way. According to this definition, methane and methanol belong to the first category and long-chain hydrocarbons and ethanol to the second. Hence, the experimental upper limit of growth yield for bacteria on methane and methanol is 1.35 and 0.68 g/g respectively and for yeasts on methanol 0.64 g/g (Table 1) Most of the experimentally obtained growth yields reported for both bacteria and yeasts are markedly smaller than these upper limits. Since a carbon conversion efficiency of 85% is practically never reached, even in methanol-limited cultures, and differences in “cell formula” cannot explain these discrepancies either, methane and methanol should be considered as energy-deficient. Some reported high yield values on methanol and methane [6–9] appear to be hardly realistic.
Table 1

Upper limit (100%), carbon metabolism determined (85 and 75%), experimental (exp) andcalculated (calc) growth yields of bacteria and yeasts on various substrates. Bacterial and Yeast"cellformula" are respectively C4H802N and C6H10O3N.

Bacteria

Yeasts

substrate

Y 100%

Y 85%

Y 75%

Y exp

Y calc

Y 100%

Y 85%

Y 75%

Y exp

Y calc

methane

1.59

1.35

-

0.9

0.58 1 (36.5%) 0.72 2 (45%)

     

methanol

0.8

0.68

-

0.5

0.45 1 (56.7%) 0.6 2 (75%)

0.75

0.64

-

0.4

0.41(55.2%)

ethanol

1.11

-

0.83

0.8

0.83 (75%)

1.04

-

0.78

0.77

0.78 (75%)

acetate

0.85

-

0.64

0.4

0.41 (47.7%)

0.8

-

0.6

0.35

0.39(49.2%)

glucose

0.85

0.72

-

0.5

0.52 (61.4%)

0.8

0.68

-

0.5

0.51(63.4%)

hexadec-ane

1.81

-

1.35

1.1

1.35 (75%)

1.7

-

1.27

0.9

1.27 (75%)

Y : g dry weight per g substrate. % in parentheses : carbon conversion efficiency.

1 Serine pathway-ICL+ variant, methanol -* formaldehyde + 1 ATP.

2 Hexulose phosphate pathway-FBP variant, TK, TA, TK

Table 2

Carbon-metabolism determined maximum carbon conversion efficiencies (%) of the synthesisof individual cell constituents and of yeast biomass with different elementary compositions

 

methanol via DHA pathway

glucose via EMP pathway

acetate

Polysaccharides

100

100

75

protein

84.7

84.7

77.2

lipid

67.9

67.9

98.1

RNA

81.3

81.4

81.3

DNA

89.1

83.1

83.1

|1|

84.3

84.3

76.5

|2|

83.7

83.6

77.4

|3|

86.3

86.2

n.d.

|4|

82.5

82.5

n.d.

|5|

86.2

86.3

n.d.

Macromolecular cell composition (given as % of dry weight)

 

Polysaccharides

protein

lipid

RNA

DNA

|1|

20

55

7

12

3

|2|

35

40

15

6

1

|3|

35

40

7

12

3

|4|

27

40

15

12

3

|5|

33

50

7

6

1

Keywords

Growth Yield Formate Dehydrogenase Acinetobacter Calcoaceticus Paracoccus Denitrificans Recycling Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Martinus Nijhoff Publishers, Dordrecht 1987

Authors and Affiliations

  • Wolfgang Babel
    • 1
  • Henk W. van Verseveld
    • 1
    • 2
  1. 1.Institute of BiotechnologyAcademy of Sciences of the GDRLeipzigGermany
  2. 2.Biologisch LaboratoriumVrije UniversiteitAmsterdamThe Netherlands

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