Biology Reference
In-Depth Information
What can systems biology contribute to the under-
standing of the elaborate regulatory network that guides the
transition to flowering? First, more complete descriptions
of gene activity networks can help map out how different
pathways are connected. Recently, high-confidence targets
of the APETALA1 (AP1) transcription factor were identi-
fied by a combination of gene expression analyses after
AP1 induction and deep sequencing of AP1-bound nuclear
DNA (ChiP-Seq). From these studies a dual role emerges
for AP1, in which it acts first to downregulate repressors of
the floral transition and then as an activator for floral organ
formation genes, including its own dimerization partners
[87] . A similar dual role was extracted from target gene
analysis and chromatin-binding studies for the APETALA2
(AP2) transcription factor. AP2 targets also fall into two
broad classes, flowering time genes that repress the floral
transition, and floral identity genes that regulate regional
identity in the flower [88] . These dual roles for two
important factors in the floral transition pathway indicate
once more that subnetworks become more intimately
connected when more information becomes available. It
will be very informative to obtain target gene analyses of
each of the major transcription factors that guide the tran-
sition from vegetative to generative meristems in the shoot.
It will be important to see whether the separation into
subnetworks derived from decades of genetic analysis will
ultimately need to be replaced by a description of a linked
'network of networks'.
To determine whether large, complicated networks can
be decomposed into subnetworks or whether they need to
be considered in their fully integrated state, a description of
network structures is not sufficient. Again, modeling the
effect of network connections will be needed to substantiate
whether our current, essentially linear view on how envi-
ronmental inputs initiate flowering holds true. When
genome-wide data indicate that such inputs are intertwined,
surprising behavior may be expected when the networks are
simulated. A workable approach to tackle large-scale
information processing networks is to perform Boolean
network analysis, which simplifies quantitative inputs in
gene regulatory networks (see Chapter 10). Such an anal-
ysis has been performed for floral pattern formation
[89,90] , and it seems worthwhile to integrate similar
models with an interlinked flowering time network. At the
same time, it will be necessary to model these networks in
space, as all factors are operating in a spatial context and
their activity modifies the spatial context. An early example
of such models generates, from a hypothesis at the
molecular level, understanding at the level of whole plant
architecture [91] . Here, a simple network for the floral
transition is modeled in a spatial context, which provides
tantalizing insights into the architecture of floral branching
structures as well as in to the constraints that guide their
evolution. Ultimately, a much more fine-grained integration
will be necessary to understand, for example, how the floral
transition changes not only the identity of organs formed on
the shoot apex but also their relative positioning, which is
phyllotaxis. Finally, theoretical analysis is not only helpful
to ascend to higher levels of integration, but can also
provide key insights into basic molecular mechanisms that
underlie many of the processes captured more abstractly in
global networks. Very recently, computational modeling
has been adapted to understand how the accumulation of
cold can lead to stable repression of the FLC transcription
factor [92] . The observed two-step accumulation of
epigenetic marks at the key flowering regulator FLC was
used to inspire a model in which nucleation of repressive
marks and a bias in histone dynamics towards the repres-
sive mark were simulated on in silico loci. The model
predicted stochastic switching of individual cells to the
repressed FLC state, where the amount of switched cells
depended on the length of the cold induction, and this
stochastic behavior was experimentally validated.
CONCLUSIONS AND PERSPECTIVE
One of the goals of systems biology is to map the relation-
ships between genotype and phenotype. The gene products
encoded in the genome interact in complicated and complex
networks, and from those interactions the biological
processes that underlie phenotype are enabled. In this view,
phenotype is an emergent property of the networks whose
components are directly or indirectly defined by genotype.
An added measure of complexity is that networks respond
differentially to environmental stimuli.
Plants are tractable systems in which advances have
been made on this very big problem. Their sessile nature
allows environmental perturbations to be controlled and
monitored. Aspects of their growth and development help
to simplify the analysis of responses, particularly at the
resolution of individual cell types.
We have provided several examples in which high-
throughput expression data were used as a starting point to
probe biological processes. The results have been the iden-
tification of genes and subnetworks that regulate the process
under study. The primary focus to date has been on tran-
scriptional regulation, in large part because the tools avail-
able for genome-wide transcriptional analysis are far more
robust than for analysis of other cellular components. With
the advent of the next generation of sequencing platforms an
area that is rapidly opening is post-transcriptional regulation
by non-coding RNAs. To achieve anything like the same
depth and breadth of analysis for proteins and metabolites
will require advances in current technologies.
The next challenge is to move from the identification of
networks to an understanding of how they function in real
time. We have given a number of examples that show how
computational modeling is necessary to address this issue
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