network proves to be quite sophisticated.
The right “motif,” or pattern of connections among a few proteins, will behave
like a switch, buzzer, sniffer or blinker.
“You can understand complicated
protein interaction networks in terms
of these little motifs that are hooked
together, like you can construct an electronic circuit,” Tyson says. “There’s been
a lot of progress here recently.”
Combining even a few such motifs in
a small subnetwork can generate useful
behaviors. For example, the tumor-sup-pressor gene p53 is controlled by just a
few motifs that combine to make p53
behave much like a ticking time bomb.
Genes carry the codes for specific proteins, and the amount of a protein that
a gene makes at any time is defined as
the gene’s activity. After DNA damage
occurs in a cell, p53 springs to life, and
its activity — and thus the concentration
of the p53 protein it encodes — begins
to oscillate slowly from more to less
and back again, like a throbbing warning light. The number of oscillations
depends on how bad the damage is. If
the DNA hasn’t been fixed by the time
the oscillations stop, p53 activity begins
to ramp up slowly and steadily until it
reaches a point that triggers the cell’s
suicide machinery.
In a network diagram, p53 is connected with two other genes, MDM2
and ATM, in a motif that could be called
a damper: a negative feedback loop. The
classic example of this kind of feedback
loop is a thermostat. If temperatures get
too high, the air conditioning switches
on and cools things down, and when
things get too cool, the air conditioning
switches off to let the room warm back
up. So the temperature oscillates around
some desired point. For genes in a network, a similar motif occurs when one
gene activates another gene, which in
turns inhibits the first gene.
Negative feedback loops with MDM2
and ATM can explain p53’s oscillation
behavior after DNA damage, Galit Lahav
of the systems biology department at
Harvard Medical School in Boston and
his colleagues reported in the May 9
issue of Molecular Cell.
The gene network that regulates a developing sea urchin embryo is well understood. Researchers can predict how changes to parts of the network would affect the entire embryo. For example, when the TBR gene is left alone, a skeletal structure can form (below). But turn off TBR
and the change propagates in a way that leaves the skeleton unformed (bottom image).
Cues from other genes
ALX1
ETS1
TBR
TEL
ERG
HEX
TGIF
dri
FoxB
FOXO
Effects on other embryo
development genes
As time runs out, a second part of
the network — a positive feedback loop
between p53 and the genes P TEN, PIP3
and Akt — overcomes this first motif.
Positive feedback loops could be called
amplifiers: They cause an increase to
keep increasing. Genes in this second
motif cut off the damper by blocking
the connection between MDM2 and
p53, Tomasz Lipniacki of the Institute
of Fundamental Technological Research
in Warsaw, Poland, and his colleagues
reported in the Sept. 21 Journal of Theoretical Biology. This change switches off
the thermostat, so the positive feedback
loop can drive up p53 activity until it triggers cell suicide.
Zoom out
Somewhat larger net works containing
a dozen or more motifs can control even
more complex processes, such as that
by which insect embryos develop body
segments. Different species develop
their segmented body plans in different
ways, but the genes involved in this segmentation are roughly the same across
most insect species. Koichi Fujimoto
and his colleagues at the University of
Tokyo wondered whether three different
development plans could be coded not in
the genes themselves, but in the way the
genes are connected in the network.
Fujimoto’s team used a computer to
“evolve” hundreds of simulated gene
networks, mutating each network
repeatedly until it produced one of the
three development plans. Looking at
the resulting networks, the researchers found that each of the development
plans corresponded to one of three network characteristics: Many feed-forward
loops, at least one negative feedback loop
or interwoven loops of both types.
While the work was based on networks
simulated in a computer, comparison
with the known gene networks of the
fruit fly and the red flour beetle showed
that those insects had the same network
patterns predicted by the simulations,
MODIFIED FROM PAOLA OLIVERI ET AL., PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES,
V.105, N. 16, COPYRIGH T 2008, NATIONAL ACADEM Y OF SCIENCES
