Biology Reference
In-Depth Information
attractive for biocatalysis. To date, strains of
P. putida
have been employed to pro-
duce phenol, cinnamic acid, cis-cis-muconate, p-hydroxybenzoate, p-cuomarate, and
myxochromide [6-12]. Furthermore, enzymes from
P. putida
have been employed in
a variety of other biocatalytic processes, including the resolution of D/L-phenylglyc-
inamide into D-phenylglycinamide and L-phenylglycine, production of non-proteino-
genic L-amino acids, and biochemical oxidation of methylated heteroaromatic com-
pounds for formation of heteroaromatic monocarboxylic acids [13]. However, most
Pseudomonas-based applications are still in infancy largely due to a lack of knowledge
of the genotype-phenotype relationships in these bacteria under conditions relevant
for industrial and environmental endeavors. In an effort towards the generation of
critical knowledge, the genomes of several members of the
Pseudomonads
have been
or are currently being sequenced [http://www.genomesonline.org, http://www.pseudo-
monas.com], and a series of studies are underway to elucidate specifi c aspects of their
genomic programs, physiology and behavior under various stresses (e.g., http://www.
psysmo.org, http://www.probactys.org, http://www.kluyvercentre.nl).
The sequencing of
P. putida
strain KT2440, a workhorse of
P. putida
research
worldwide and a microorganism Generally Recognized as Safe (GRAS certifi ed) [1,
14], provided means to investigate the metabolic potential of the
P. putida
species,
and opened avenues for the development of new biotechnological applications [2, 14-
16]. Whole genome analysis revealed, among other features, a wealth of genetic de-
terminants that play a role in biocatalysis, such as those for the hyper-production of
polymers (such as PHAs [17, 18]) and industrially relevant enzymes, the production
of epoxides, substituted catechols, enantiopure alcohols, and heterocyclic compounds
[13, 15]. However, despite the clear breakthrough in our understanding of
P. putida
through this sequencing effort, the relationship between the genotype and the pheno-
type cannot be predicted simply from cataloguing and assigning gene functions to the
genes found in the genome, and considerable work is still needed before the genome
can be translated into a fully functioning metabolic model of value for predicting cell
phenotypes [2, 14].
The CB modeling is currently the only approach that enables the modeling of an
organism's metabolic and transport network at genome-scale [19]. A genome-wide
CB model consists of a stoichiometric reconstruction of all reactions known to act in
the metabolism of the organism, along with an accompanying set of constraints on the
fl uxes of each reaction in the system [19, 20]. A major advantage of this approach is
that the model does not require knowledge on the kinetics of the reactions. These mod-
els defi ne the organism's global metabolic space, network structural properties, and
fl ux distribution potential, and provide a framework with which to navigate through
the metabolic wiring of the cell [19-21].
Through various analysis techniques, CB models can help predict cellular phe-
notypes given particular environmental conditions. The FBA is one such technique,
which relies on the optimization for an objective fl ux while enforcing mass balance in
all modeled reactions to achieve a set of fl uxes consistent with a maximal output of the
objective function. When a biomass sink is chosen as the objective in FBA, the output
can be correlated with growth, and the model fl uxes become predictive of growth phe-
