We promote here an integrative workflow that combines net-
work structural and dynamical analysis with high-throughput –omics
data and other biomedical information to gain mechanistic insights
into the causes of differentiated expression patterns from normal to
disease state. To this end, we constructed the transcription factor-
miRNA regulatory network to understand the mechanisms of meta-
static phenotype in prostate cancer.
To further substantiate the analysis, one can use the dynamical
systems theory to construct a mathematical model using suitable
modeling formalism. The dynamical analysis, for example in silico
stimulus response or perturbation analyses, of biochemical net-
works helps in understanding the functioning of a system and
provides an opportunity to formulate new hypotheses about the
effect of specific internal or external perturbations in a system. In
the systems biology approach, the iterative cycle of data-driven
modeling and model-driven experimentations refine the formu-
lated hypotheses until they are validated, which help to understand
complex mechanisms in certain biological traits.References
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