Biology Reference
In-Depth Information
The
in silico
growth yield is infl uenced not only by the structure of the metabolic
network, but also by other factors including biomass composition and the growth-as-
sociated and non-growth-associated energy maintenance factors (GAM and NGAM),
the values of which represent energy costs to the cell of “living” and “growing”, re-
spectively [22]. Therefore, since both the biomass composition and the GAM/NGAM
values were taken from the
E. coli
model [22, 33] due to a lack of organism-specifi c
experimental information, we evaluated the infl uence of these factors on the predicted
growth yield.
First, we analyzed the effects of changes in the ratios of biomass components on
the iJP815 growth yield. These analyses indicated that varying any single biomass
constituent by 20% up or down has a less than 1% effect on the growth yield of
P.
putida
. These results are consistent with results of a previous study on the sensitivity
of growth yield to biomass composition [40]. Although it is still possible that some
components of
P. putida
biomass are not present in
E. coli
or
vice versa
, we conclude
that the use of
E. coli
biomass composition in the
P. putida
model is a justifi ed assump-
tion for the purpose of our application and is probably not a great contributor to the
error in our predictions of growth yield.
Subsequently, the effects of changes in the GAM on the
in silico
growth yield were
tested. It was found that if GAM was of the same order of magnitude as the value used
in the
E. coli
model (13 mmolATPg
DW
−1
), its infl uence is negligible, as increasing or
decreasing it twofold alters the growth yield by merely 5%. A higher GAM value in
P. putida
than in
E. coli
could contribute to the discrepancy between the experimental
measurements and
in silico
predictions, but it could not be the only factor unless the
E.
coli
and
P. putida
values differ more than twofold, which is unlikely.
Finally, we assessed the effects of changes in the value of NGAM on
in silico
growth yield. The NGAM growth dependency is infl uenced by the rate of carbon
source supply, and thus indirectly by the growth rate. If the carbon intake fl ux is low
(as in the case of the experiments mentioned above, with a dilution rate of 0.05 h
−1
),
the fraction of energy utilized for maintenance purposes is high and therefore so is
the infl uence of the NGAM value on growth yield. Under such low-carbon intake
fl ux conditions, a twofold increase of the NGAM value can decrease the growth
yield by about 30%. This indicates that the main cause for the discrepancy between
in vivo
and
in silico
growth yields is that the NGAM value is likely to be higher in
P.
putida
than in
E. coli
. Increasing the NGAM value from 7.6 of 12 (mmol
AT P
g
DW
−1
h
−1
)
would reduce the
in silico
growth yield and lead to a better match with experimental
values. Consequently this NGAM value was used in subsequent FBA and FVA [41]
simulations.
For a high infl ux of carbon source the infl uence of NGAM on the growth yield is
low and the infl uence of the NGAM and GAM values on growth yield are comparable.
It should be noted that, while FBA predicts the optimal growth yield, few cellular
systems operate at full effi ciency. Bacteria tend to “waste” or redirect energy if it is
abundant [42], leading to a lower-than-optimal
in vivo
growth yield. It is also worth
mentioning that maintenance values may depend on the carbon source used [43] and
on environmental conditions [44-46].
