Biology Reference
In-Depth Information
task of systems biotechnology is to comprehensively collect the global
cellular information and omics data at different scales ranging from
DNA and RNA to proteins, metabolites, and fluxes, and to combine
such data in order to generate predictive computational models of
the biological system. In this context, bioinformatic data analysis and
interpretations of x-omic data needs to be improved as more and more
data are collected. Each x-ome alone is not enough for understanding
cellular physiology and regulatory mechanisms due to the existence of
an information gap among the x-omes and the behavioral phenotype
[22]. As mentioned earlier, the transcriptome cannot explain transla-
tion, posttranslational regulations, and protein-protein interactions.
Furthermore, the amount of protein is not always proportional to the
activity of the protein, which in turn is not always proportional to
the metabolic flux. Thus, it is indeed required to carry out combined
analysis of all x-omes to better understand the cellular physiology
and metabolism at the systems level. By doing so, strategies for meta-
bolic and cellular engineering of organisms at the true whole-cell level
can be developed.
In silico modeling and simulation of the complex biological system
is invaluable to organize and integrate the available metabolic knowl-
edge and to design the right experiments. Most importantly, the
number of real wet experiments can be minimized by carrying out
in silico experiments using the computational model. In silico pertur-
bation of the metabolic system can provide crucial information on
cellular behavior under varying genetic and environmental perturba-
tions, thereby suggesting a multitude of strategies for the development
of efficient biotechnology processes. The current predicting power of
biological simulation, however, is limited by insufficient knowledge
of global regulation and kinetic information, and thus in silico design-
based process development is still far from perfection. Will we ever be
able to have a whole-cell model that truly resembles the real cell in
every aspect? We will reserve the answer, hopefully a positive one,
to the near future since much effort is being devoted to whole-cell mod-
eling and simulation, thus giving optimistic expectation to develop a
system that allows more accurate simulation of metabolic and regula-
tory behaviors [41]. In the meantime, we can enjoy the great advantage
of having large-scale in silico models and global-scale omics data for
strain improvement as described in this chapter.
Systemic integration of heterogeneous sectors is an ongoing trend
in every field. In industrial biotechnology, this trend is also being
driven by combining omics together with in silico modeling
and simulation. Although much remains to be done to integrate all
these systems of biotechnological components, the future seems to
be bright toward the goal of whole-cell level understanding and
engineering.
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