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In-Depth Information
subcellular organization essentially conserved in all
eukaryotes, is recognized as an optimal model for the study of
the eukaryotic cell (
[16,17]
and references therein).
In many cases there are attempts to explain complex
diseases in terms of the alteration of a single component
(e.g., a 'gene'; see
[23,24]
); however, this is simplistic
[4,7,16,25]
. The application of progressively more
affordable next generation-sequencing (NGS) technologies
(e.g., exome sequencing and whole-genome sequencing
(WGS;
[26]
)) is already making an invaluable contribution
[5,27,28]
towards a more comprehensive holistic,
'systems-level' perspective (see below). First, it is
important to recognize that, despite their limitations and
the challenges ahead
[29]
, NGS and advanced post-
genomic technologies are already delivering clear exam-
ples of direct applications and improvements in human
diagnostics and the treatment of diseases with a basic
genomic component (see, for example:
[30
and the dynamic behavior of cells subjected to specific
perturbations appears essential. We submit that compre-
hensive experiments with S. cerevisiae and other model
eukaryotes have the potential to unveil basic principles of
internal organization at the cellular level
[20]
, and the
short- and long-term effects of perturbations and the
dysregulation of networks that may illuminate the origin
and sequence of events underlying complex phenotypes
and diseases. These approaches will lead to direct appli-
cations in medicine and biotechnology (e.g., the early
detection of imbalances; biomarker discovery
[51]
; see
also below).
YEAST FOR SYSTEMS UNDERSTANDING OF
EUKARYOTIC BIOLOGY AND NETWORKS
APPLICATIONS
Yeast as a Model System for Comprehensive
Systems Biology Studies
The new technologies of the post-genomic era are redis-
covering the complexity of biological systems, with thou-
sands of components (e.g., genes, transcripts, proteins,
metabolites) interacting in finely tuned dynamic biological
networks
[16,52,53]
. This is reflected in
Figure 18.1
, which
shows the flow of genetic information (from DNA to RNA
to proteins), together with the whole spectrum and exquisite
choreography of biological interactions and networks in the
eukaryotic cell: DNA
34]
). While
this is opening the way towards a new era in the charac-
terization and treatment of both Mendelian and, as yet,
uncharacterized diseases
e
38]
, not all complex
diseases will be resolved merely by the application of
more powerful sequencing approaches.
The reality is that many complex traits and diseases are
being revealed as multifactorial in nature
[4,25,39]
or
involving a combination of genomic, epigenomic, and
environmental factors (see next section).
Complex phenotypes may be more directly related to
global 'systems' properties than they are to particular
'genes' or non-coding DNA sequences (Marc Vidal in
[4]
),
with perturbations leading to changes in network interac-
tions which, if not counteracted, will lead to global
imbalances and complex diseases
[7,16]
. Essential steps
towards clarifying the whole picture will be (1) the proper
characterization and annotation of complex disease
phenotypes, and (2) the definition of the dynamics of
interactions within the networks underlying these complex
diseases, towards early diagnosis and affordable treatment.
Initiatives such as the definition of phenotype ontologies, in
the OMIM and PhenOMIM databases are beginning to set
the standards
[7,40
[35
e
DNA; DNA
RNA; DNA
protein;
e
e
e
DNA
metabolites; RNA
RNA; RNA
protein (RNP);
e
e
e
RNA
metabolites; protein
protein; protein
metabolites
e
e
e
and metabolite
metabolite interactions), with their
dynamics and interaction with the environment being
ultimately responsible for particular phenotypes. This
'ecosystem of organelles' which constitutes the eukarotic
cell
[54]
is a direct consequence of millions of years of
evolution
[19,22]
.
A schematic representation of the eukaryotic cell, its
basic architecture, the main levels of regulation at the (epi)-
genome, transcriptome, proteome and metabolome, and its
interactions with the environment (sensing natural fluctua-
tions in external conditions together with those derived from
interaction with other organisms, e.g., competition or coop-
eration) is presented in
Figure 18.2
. This intrinsically
complex system involves the integration of mechanisms and
networks in different compartments and at different levels of
regulation, subjected to distributed, multilevel control
[52,53,55]
. The integration of comprehensive data from these
levels into models with predictive and explanatory power
constitutes one of the most exciting challenges of systems
biology (
[16]
and references therein). In this context, the use
of well-defined model systems, in properly designed exper-
iments under controlled conditions, with proper data
e
44]
.
The idea that multi-scale dynamic complex systems
formed by interacting macromolecules and metabolites,
cells, organs, and organisms underlie some of the most
fundamental aspects of life was proposed by a few
visionaries half a century ago (see
[45]
and references
therein) and systems biology is materializing as one of the
major
e
47]
.
Systems biology is not so much concerned with invento-
ries of working parts but, rather, with how those parts
interact to produce units of biological organization whose
properties are much greater than the sum of their parts
[16,45
ideas
of
'post-genomic
biology'
[4,45
e
50]
. For this purpose the use of model organisms
in the elucidation of basic principles of eukaryotic biology
e
