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questions, balancing inductive (hypothesis-generating)
approaches with hypothesis-driven experiments [71] in
order to avoid becoming 'lost in high-throughput data'
[53,72,73] ; (b) The need for proper experimental design
that minimizes the number of confounding variables and
puts in place a bioinformatic and statistical strategy from
the outset; (c) The need to acknowledge the uncertainties in
biology. Even at the cellular level there are components and
reactions still to be identified, localized, or properly
annotated (e.g., ATP-producing and -consuming reactions;
NAD(P) and NAD(P)H redox balances in different
compartments; [3,15,56] ; (d) The possibility of more than
one function per gene, RNA, protein and/or metabolite,
participating in different biological networks. As an
example, studies on the dynamics of transcriptional regu-
latory networks of S. cerevisiae have revealed large topo-
logical changes depending on environmental conditions,
with transcription factors altering their subcellular locali-
zation and interactions in response to stimuli, some of them
serving as permanent hubs but most acting transiently in
specific conditions only [74,75] ; SGD database; www.
yeastgenome.org ; BioGRID, http://thebiogrid.org/ ; [16] ;
(e) The correct use of mutants (e.g., yeast auxotrophic and
knockout mutants) and interpretation of the results obtained
with them [76
introduced by periodic sampling). Physiological studies in
batch cultures are often limited to the analysis of dual
transitions (e.g., from starvation to excess; from excess to
nutrient limitation), or to the study of cells in exponential
phase (a short interval in which all cells are growing at
a constant growth rate, the maximum specific growth rate
under the conditions tested,
m max ) in an essentially constant
environment [81] .
The main limitation of batch cultures, i.e., the difficulty
of extracting proper biological conclusions from cultures
in which growth rate and environmental conditions are
changing, can be solved by using continuous (e.g., che-
mostat) cultures in steady state, in which the specific
growth rate can be selected and fixed operationally, and the
cells are growing (and can be long maintained) in a steady
state at a constant growth rate, in a constant environment
[81
83] . This characteristic has made the chemostat one
of the preferred experimental systems for biochemical,
physiological and functional genomic studies at the
cellular level [81,82,84,85] . On the other hand, steady
states alone are not able to realistically reproduce essential
processes occurring during dynamic transitions (which are
the most common events in nature, e.g., responses to
environmental perturbations). Moreover, in a recent
evolutionary study combining batch and chemostat culti-
vation, the majority of evolutionary mutations appeared to
occur during dynamic transitions (batch culture) [86] ,
which emphasizes their importance and the need for proper
experimental designs
e
79] ; (f) The relevance of compartmentali-
zation and distribution of functions between organelles in
the light of evolution [22,54] .
e
for
comprehensive
studies of
Experimental Systems for Comprehensive
Studies of Yeast Networks Dynamics: from
Steady States to Time-Course Experiments
through Perturbations
Provided that external conditions such as pH, temperature,
or the presence of toxic compounds have a negligible
influence, most commonly encountered situations in nature
can be summarized as nutrient starvation, nutrient limita-
tion or nutrient excess. Free-living microbial eukaryotes
have evolved in order to survive environmental perturba-
tions, including those leading to changes in nutrient avail-
ability, with periods of starvation and nutrient limitation
among the most common in nature [80] .
A batch (flask) culture, most commonly used experi-
mental system, is able to reproduce common transitions in
nature (e.g., from starvation/quiescence to nutrient excess,
nutrient limitation, and a new quiescence or maintenance
state). Time-course experiments in batch, monitoring the
different growth phases (quiescence; lag phase; accelera-
tion; exponential growth; deceleration; stationary phase;
maintenance or death), have the potential to characterize
transitions in nature where the specific growth rate (
dynamic transitions.
The definition of an experimental system for compre-
hensive studies of eukaryotic network dynamics needs to
satisfactorily address the issues explained above. Thus, the
use of chemostat steady states combined with well-
designed perturbations (transient or sustained), followed by
time-course experiments and profiling (e.g., monitoring at
different molecular and 'omics' levels), has advantages as
a reference experimental system for next-generation
comprehensive systems biology (NGSB) studies. Chemo-
stat steady states combined with perturbations, monitoring
short- and long-term responses have been applied to the
elucidation of basic physiological responses and functional
genomics studies [87
91] . Their huge potential will be
clearly revealed with (a) the implementation of proper
experimental designs with clear objectives, and (b) the
integration of data from a range of 'omic' and molecular
biological analyses [17,92
e
95] see also below). At the
fundamental level, a first challenge will be the character-
ization of essential eukaryotic networks and their dynamics
in comprehensive yeast systems biology studies, towards
the elucidation of eukaryotic 'design' principles [17,53] .At
the more applied level the challenge will be the charac-
terization of early stages of dysregulation in eukaryotic
networks responsible for the onset of complex diseases
e
)is
continuously changing. However, their potential is
hampered by operational and physiological limitations
(including time-course limitations and the perturbations
m
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