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Systems biology is rapidly coming to limelight
from the past decade.

One of the salient features of systems biology
is the use of mathematical and computational models, which define the
complexity of biological systems with much ease. This complexity arises from
the diversity of components (genes, proteins, and their metabolites), their
interactions, and a non-linear nature of these interactions. The computational
models used in biochemical systems biology typically require iterative building
and stepwise improvements based on the comparison with experiments.

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Such models have the ability to predict the
behavior of any biochemical system under different deviations, or hypothetical
conditions that may be of interest but are not feasible in experimental
settings. However, recently, mathematical models are more than just tools for
integrating observations, making detectable predictions, or for high
throughput in silico experimentation. Highly refined
mathematical models also serve as the embodiments of our current knowledge
about specific biochemical systems.

Increasingly, the focus has shifted to the
crosstalk between the genes, transcripts, proteins, and metabolites that the regulate
gene’s expression. Systems biology approaches have already proven as a
milestone to initiate a deeper understanding of diverse biochemical processes,
from individual metabolic pathways, to signaling pathways, to genome-scale
metabolic networks. Therefore, we can safely predict that systems thinking will
become even more ubiquitous in future. The role of formal mathematical and
computational models in systems approaches renders the role of bioinformatics
increasingly important for systems biology research.

The future
of systems biology also aims at developing several versatile poly-pharmacology
therapies, like many classes of drug transporters within the cellular
environment. In addition, systems biology and systems medicine tools should be
further implemented to achieve the highly ambitious goal of developing the
“virtual human”, essentially encompassing all the intricate molecular networks
and dynamic interactions on multiple omics-levels in order to render the tasks
of drug development, through multiple system deviations with lead molecules.

In addition,
such data handling should be shared more efficiently across the pharmaceutical
industry in order to allow for more rapid theranostic developments. Ultimately,
with the adoption of such novel research perspectives, systems medicine will
prove to become one of the mainstays in the way future research will be carried
out, not only for extracting further mechanistic knowledge on disease processes
but also through a faster and more effective drug development pipeline with the
integration of systems-based analysis.

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