Biology Reference
In-Depth Information
It is clear that we are still far from understanding cellular behavior
at the systems level. Nevertheless, the combination of these x-omes
profiling will suggest holistic insight into the whole-cell metabolism
and regulation, and the interaction of cells with the environment.
The benefits of having global x-omic data will be truly realized when
we can correctly integrate all these data using well-validated in silico
modeling and simulation tools. Again, it will take a while to see this
achieved. In the meantime, we can still enjoy developing improved
organisms based on these large-scale data. One can extract interesting
knowledge on local pathways from global-scale data sets, and use it
for altering metabolism toward the desired goals. It should be empha-
sized that the generation of new knowledge on the local (rather than
global) reactions and pathways to be manipulated was possible because
large-scale data on the genome, transcriptome, proteome, metabolome,
and fluxome are available. In what follows, we describe how each
x-ome's data were generated and their combinations were used for
strain improvement.
Genome Analysis
Comparative analysis of genomes is a relatively simple yet powerful
way of extracting information necessary for understanding differ-
ences in metabolism and identifying targets for strain improvement.
One can identify unnecessary genes or operons that are not beneficial
or even harmful for producing the desired biochemical products,
and those genes that need to be newly introduced, amplified, or
modified to establish new pathways or to enhance the pathway fluxes
[24,25]. The genomes can be compared among different organisms
as well as between the wild type and its mutant strains. This can
obviously be extended to comparing the control strain with the engi-
neered strains. Engineering of microorganisms based on comparative
genomics has recently been successfully demonstrated by Ohnishi
et al. [25]. The genome sequence of the lysine-overproducing
Corynebacterium
strain was compared with that of the wild-type strain
to identify genes with point mutations that might be beneficial for
the overproduction of
L
-lysine. Consequently, the point mutations in
five genes responsible for improved lysine production could be iden-
tified. Introduction of new genes and/or knockout of undesirable
genes have been frequently practiced in metabolic engineering. Having
the complete genome sequences for a number of microorganisms,
we can identify many more candidate genes to be manipulated based
on genome-scale comparison data.
Transcriptome Analysis
Development of high-density DNA microarrays is allowing us to simul-
taneously measure relative mRNA abundance in multiple samples.
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