Biology Reference
In-Depth Information
gathered by different methodologies, specific data analysis
procedures were used to minimize systematic bias and to be
consistent in data processing prior to integration.
Table 18.2
summarizes the pipeline and data analysis steps performed
to handle each class of 'omic' data. The genome-scale
integrative data analysis and modeling of dynamic
processes from different 'omic' levels, obtained with the
latest techniques, will be a recurrent challenge in systems
biology
[53,172]
.
With this perspective, we submit that complex imbal-
ances and diseases should be viewed as shown in
Figure 18.3
. The genome and epigenome ('nature') define
the essential networks, homeostatic states, and initial
susceptibility to dysregulation, which will be subjected to
a sequence of environmental perturbations ('nurture').
Perturbations will result in transient deactivation of
redundant networks and activation of defense or stress
responses, until a new homeostatic state is restored.
Complex (e.g., multifactorial) and/or sustained perturba-
tions that overcome the intrinsic defenses and stress
responses may result in specific cascades of dysregulation,
which result in highly complex acute imbalances and
diseases. From here, periodic longitudinal monitoring in
proper experimental systems (e.g., chemostat steady states
subjected to carefully designed perturbations; see above)
allow the characterization of homeostatic and perturbed
networks in systems biology by comprehensive experi-
ments in model organisms (e.g., yeast)
[53,93,243]
.These
have the potential to unveil the origin, early stages, and
dynamics of progression of complex imbalances and
diseases towards early diagnosis (e.g., characterization of
relevant, multiple biomarkers, at different 'omic' levels;
[244]
) and rational, truly affordable, strategies for
prevention and therapy. At this point, it is relevant to note
that, although the essential core of eukaryotic machinery is
conserved in all eukaryotes
[16,53,56]
, yeast models of
human disease have shortcomings and there will always be
doubts about how closely the conditions in yeast recapit-
ulate conditions in differentiated human cells. However,
the large number of molecular tools and the huge potential
for high-throughput genetics and chemical screenings
position yeast as a first-line approach to tackle complex
human diseases. With careful experimental designs
towards the characterization of biomarkers and networks
dynamics, the new knowledge can be used to develop
advanced experimental strategies in higher eukaryotes
[245]
. Based on this, the facts explained in previous
sections, and its huge potential for integrative systems
biology studies
[17]
, we submit that S. cerevisiae is an
optimum model organism for comprehensive studies of
dynamics of dysregulated networks at the essential cellular
level, to enable rational strategies and applications in
biotechnology and disease.
Yeast will be invaluable in most challenges at the
forefront of systems biology, such as (a) characterization
of essential eukaryotic networks, common core to all
eukaryotes: their architecture, hierarchy, interplay, dyna-
mics, changes in topology under different conditions and
perturbations, principles of adaptability, flexibility and
robustness in eukaryotic networks
[246]
; (b) characteriza-
tion of steady states and dynamics of activation of transient
defense networks (e.g., stress, proteostasis networks), their
interaction and interplay with essential networks, dynamics
Yeast for Comprehensive Studies of
Dynamics of Dysregulated Networks:
Towards Rational Strategies and Applications
in Biotechnology and Human Disease
Advanced biotechnological processes and complex
diseases, multifactorial in nature, constitute a major
challenge, for which a rational strategy for comprehensive
dynamic studies at the cellular level is currently lacking.
Complex diseases, traditionally classified based, for
example, on basic symptoms, organ localization and/or
late phenotypes, are being progressively better character-
ized, with more insight into their primary molecular
origin. However, the common trend to reduce the problem
to a single causation (e.g., candidate 'disease gene'; 'risk
gene' or 'risk mutation'
[234
236]
), overlooking the
essential role of biological networks and interactions, their
interplay and dynamics, needs to be addressed
[7,16]
.
Until we understand diseases as altered states of human
biological networks
[237]
, with intrinsic dynamics and
interplay, not only between them (
Figures 18.1
and 18.2)
but also with our microbiome (
[238]
; Human microbiome
project;
http://commonfund.nih.gov/hmp/
), in constant
relationship with the environment, and the reality of the
recently proved human metabolic individuality
[239]
, our
vision will be incomplete. Progress is being made, and we
are beginning to consider complex diseases to be more
likely due to alterations of 'system' properties
[7]
, starting
with efforts directed towards better mapping and charac-
terization of the interactomes of different eukaryotic
organisms, with progressive incorporation of more layers
of information
[7,240]
.
Figure 18.1
, and global maps of
human binary protein
e
protein interactions (CCSB Inter-
actome Database,
http://interactome.dfci.harvard.edu/
),
provide a basis for seeking dysregulated pathways/
networks
[235,241]
, specific 'disease networks' or alter-
ation in 'networks properties'
[7,234,242]
. Many of these
studies and maps, however, need to incorporate more
information on changes of topology, and the interplay and
dynamics of networks characteristic of the specific bio-
logical problem/disease, initially at the cellular level, from
comprehensive experiments under controlled conditions
(see previous section).
e
