Biology Reference
In-Depth Information
FIGURE 20.1
Schematic of
Arabidopsis
embryo and seed-
ling. Embryonic stages are depicted with dotted lines indicating
the provenance of different cellular populations. Magnified
regions of the shoot and root apical meristems are shown with cell
types indicated in the root meristem and expression domains for
different genes color coded in the shoot meristem.
SYSTEMS ANALYSIS OF ARABIDOPSIS
DEVELOPMENT
Background
Because plants cannot move, their developmental strategies
necessarily differ from those of animals. Whereas animals
generally have all of their organs formed during embryo-
genesis, plant embryos can be thought of as two sets of stem
cells, one that will form the aerial tissues and the other that
will form the roots (
Figure 20.1
). These stem cell pop-
ulations or 'meristems' are the source of all post-embryonic
growth and development, and it is nearly impossible to
predict the ultimate form and structure of a plant from its
embryo. For instance, the embryo of an oak tree does not
look that different from the embryo of a corn plant. How
meristems are able to generate all of the different types of
organs in a plant is a fascinating question, which is
beginning to be addressed through systems-level analysis.
We will discuss work that addresses both root and shoot
development.
One essential component of systems analysis is the
ability to dissect gene activity in space and time. The root
of Arabidopsis has become a favored organ for such
analysis, owing primarily to the simplicity of its organi-
zation. The stem cell niche is located at the tip of the root
(
Figure 20.1
) and it produces its progeny through a series
of highly stereotyped divisions. As one moves from the
stem cell niche up the root toward the shoot, the cells of
each type become older. Thus, to a first approximation any
root cell can be specified along two axes, its place on the
radial axis identifying the type of cell and its position on
the longitudinal axis determining where along the devel-
opmental timeline it is. This simplification effectively
reduces what is normally a four-dimensional problem
(three spatial dimensions plus time) to a two-dimensional
problem. Being able to specify cell type and develop-
mental stage along two axes has greatly facilitated
approaches that treat this organ as a heterogeneous set of
closely coordinated cell types, rather than assaying the
organ in its entirety. Similar approaches are currently
being used in shoot development, despite the higher
complexity of this organ system.
A second component of systems analysis is the gener-
ation of models from data. When regulatory networks are
not linear, for example when there are feedbacks, it is no
longer trivial to understand their workings, as not only the
quality of interactions (positive or negative) but also their
strength determine the outcome. Mathematical or compu-
tational analysis is then required to calculate how regula-
tory circuits work. This approach has been increasingly
