Biology Reference
In-Depth Information
a Global “Systems” Context:
The Complement Paradigm Shift
Dimitrios Mastellos & John D. Lambris
The Human Genome Project has pioneered scientific discovery by
eloquently demonstrating that the basic constituents of a biological
system (namely, the genome, transcriptome, and proteome) can be
precisely defined, measured, and quantified at predetermined stages
or time intervals [1]. The genome carries the entire digital (DNA) and
inheritable code of life which remains largely unaffected by external
stimuli, while the transcriptome and proteome reflect the dynamic and
complex nature of biological information, as they are subject to fine
regulatory control (e.g., transcriptome) and chemical modifications
(e.g., proteome) that together dictate the actual behavior of cells and
organisms. The deciphering of the entire human genomic sequence has
provided the essential driver for developing high-throughput screen-
ing strategies and methodologies for the measurement of the entire
RNA and protein output of a cell or organism [2]. This breakthrough
has led to the development of
proteomics
and the unraveling of the
dynamic protein profile of different tissues and organisms as it con-
stantly adapts to discreet environmental perturbations.
It should be noted that the genome analysis of various organisms
(e.g., yeast, insects, mammals) has revealed that what actually defines
the differences between species, and favors natural selection across the
evolutionary ladder, is not the gene content per se
,
but the fine inter-
play of signaling pathways and inducible gene regulatory networks
that are activated in response to various molecular signals and environ-
mental stimuli [1]. In this respect,
systems biology
has emerged as the
field that studies these regulatory networks in an integrated and com-
prehensive manner. The “systems” approach attempts to “reconstruct”
biological networks by integrating data produced on a multidiscipli-
nary platform and aims at designing “model biological systems” with
potentially new properties by exploiting the vast computing capability
of modern bioinformatics [1].
Immunological processes epitomize in many ways the inherent
complexity of biological systems. Random or stochastic signals are
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