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The resultant genes include the
ptsG, pykF, mqo, sdhABCD,
and
aceBA
genes that were not found in the genome of
M. succiniciproducens
,
and only in the central pathway of
E. coli
. In addition, the
pflB
and
ldhA
genes were selected for gene candidates from the literature
information [99].
Step 2: Modeling and simulation.
Next, constraints-based flux analysis
was carried out to rationally select the target genes to be manipulated in
the subsequent experiments. Initially, a genome-scale in silico
E. coli
model comprising 814 metabolites and 979 metabolic reactions was con-
structed from the publicly available information and database [75,94].
Then, a variety of in silico knockout strains were designed by fixing the
relevant fluxes at zeros among candidate genes (i.e.,
aceBA, ldhA, mqo,
ptsG
,
pykFA
,
pfl,
and
sdh
) identified in step 1. For each knockout strain
based on the in silico model, the correlation between the biomass forma-
tion and succinic acid production was examined in various mutant
strains in silico. This was achieved by maximizing the growth rate while
recursively limiting the level of succinic acid production in the flux
model, thereby identifying gene targets for succinic acid overproduction.
Finally, among all mutant strains, only the five cases (D
pflB
, D
ptsG pykFA
,
D
ptsG pfl ldhA
, D
ptsG pykF pykA pfl
, and D
ptsG pykF pykA pfl ldhA
) were
predicted as possible candidates capable of both overproducing succinic
acid and sustaining biomass formation (table 7.3 and figure 7.13).
Step 3: Gene knockout and cultivation for strain engineering.
According
to the predictions in step 2, several mutant strains were constructed
by gene knockout experiments and cultured to examine succinic acid
production (tables 7.4 and 7.5). Interestingly, anaerobic cultivation
profile of
E. coli
W3110GFA (
ptsG pykF
and
pykA
triple knockout
mutant) showed about a 3.4-fold increase in succinic acid formation
with significant reduction of other fermentation products (8.29-fold
increase in succinic acid ratio) in 24 h of anaerobic fermentation (table 7.5).
In addition,
E. coli
W3110GFA could convert 50 mmoles of glucose to
17.4 mmoles of succinic acid, while other mutants did not exceed
3 mmoles of succinic acid (data not shown). These results are con-
sistent with in silico prediction (table 7.3), and thus prove the
effectiveness of the systems biotechnological approach.
FUTURE PERSPECTIVES ON SYSTEMS BIOTECHNOLOGY
The cell is a complex system. Various types of biochemical processes
are seamlessly integrated for generating mass and energy (metabolic),
transmitting information (signaling), and regulating cellular and
metabolic behaviors (gene regulatory) through complex networks and
pathways of the molecular interactions and reactions [100]. Thus,
systemic and integrative approaches to systems biotechnology allow
us to understand its organization within a more global context of the
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