Biology Reference
In-Depth Information
notypes [22, 23]. The CB analysis techniques, including FBA, have been instrumental
in elucidating metabolic features in a variety of organisms [20, 24, 25] and, in a few
cases thus far, they have been used for concrete biotechnology endeavors [26-29].
However, in all previous applications in which a CB approach was used to design
the production of a biochemical, the studies addressed only the production of com-
pounds that can be directly coupled to the objective function used in the underlying
FBA problem. The major reason for this is that FBA-based methods predict a zero-
valued fl ux for any reaction not directly contributing to the chosen objective. Since
the production pathways of most high-added value and bulk compounds operate in
parallel to growth-related metabolism, straightforward application of FBA to these
biocatalytic processes fails to be a useful predictor of output. Other CB analysis meth-
ods, such as extreme pathways and elementary modes analysis, are capable of analyz-
ing non-growth-related pathways in metabolism, but, due to combinatorial explosion
inherent to numerical resolution of these methods, they could not be used so far to
predict fl uxes or phenotypes at genome-scale for guiding biocatalysis efforts [30].
To address both the elucidation of the metabolic wiring in
P. putida
and the use of
P. putida
for the production of non-growth-related biochemicals, we developed and
present here a genome-scale reconstruction of the metabolic network of
Pseudomonas
putida
KT2440, the subsequent analysis of its network properties through CB model-
ing and a thorough assessment of the potential and limits of the model. The reconstruc-
tion is based on up-to-date genomic, biochemical, and physiological knowledge of the
bacterium. The model accounts for the function of 877 reactions that connect 886 me-
tabolites and builds upon a CB modeling framework [19, 20]. Only 6% of the reactions
in the network are non-gene-associated. The reconstruction process guided the refi ne-
ment of the annotation of several genes. The model was validated with continuous
culture experiments, substrate utilization assays (BIOLOG) [31],
13
C-measurement
of internal fl uxes [32], and a specifi cally generated set of mutant strains. We evalu-
ated the infl uence of biomass composition and maintenance values on the outcome of
FBA simulations, and utilized the metabolic reconstruction to predict internal reaction
fl uxes, to identify different mass-routing possibilities, and to determine necessary gene
and reaction sets for growth on minimal medium. Finally, by means of a modifi ed Opt-
Knock approach, we utilized the model to generate hypotheses for possible improve-
ments of the production by
P. putida
of PHAs, a class of compounds whose production
consumes resources that would be otherwise used for growth. This reconstruction thus
provides a modeling framework for the exploration of the metabolic capabilities of
P.
putida
, which will aid in deciphering the complex genotype-phenotype relationships
governing its metabolism and will help to broaden the applicability of
P. putida
strains
for bioremediation and biotechnology.
Highlights of the Model Reconstruction Process
We reconstructed the metabolism of
P. putida
at the genome-scale through a process
summarized in Figure 1. The reconstruction process involved: (1) an initial data col-
lection stage leading to a first pass reconstruction (iJP815
pre1
); (2) a model building
stage in which simulations were performed with iJP815
pre1
and reactions were added
until the model was able to grow
in silico
on glucose minimal medium (iJP815
pre2
);
