Please wait while the transcript is being prepared...
0:00
Hi.
My name is Uri Alon from the
Weizmann Institute in Israel.
Today I'm going to talk with you
about evolutionary trade-offs
and the geometry of
gene expression space.
0:11
The basic idea is that when you
have trade-offs between tasks,
they lead to simplicity,
a simple geometry
of the phenotypes in trait space.
0:23
Now, when we think about
evolution and evolutionary theory,
we usually think
about a simple picture
where the DNA, the genotype,
through developmental processes
gives rise to the organism's
shape, the phenotype.
And the phenotype does
something to give you fitness.
For example, the bird's
beak eats the seeds.
The better it eats
the seeds, the more
fit the organism, the more
viable babies that it makes.
0:49
And conceptually,
we can describe this
in terms of a fitness landscape.
So we think about things we
can measure about the beak.
We take a ruler, and
we measure its height
and its width and its lengths.
And each trait like that is an
axis, and so we have the trait space
describes all possible beak shapes.
And for each point in that space,
we can imagine the fitness of a bird
with that beak.
And if we can imagine
a fitness landscape,
it might have a peak
or several peaks.
And natural selection tends to
maximize fitness and bring you
to the maximum of the
fitness landscape.
1:26
But in this talk we'll talk
about a different situation.
What happens if the same beak
needs to do more than one task?
For example, if you need to
eat both the seeds and you
need to pick insects or
pollen from inside flowers.
Now, the problem is
that a single phenotype
can't be optimal at
two tasks at once.
Maybe to eat the seeds you need
something like a heavy plier.
And to pick out the
pollen you need something
like a pincers or a nose plier.
And a single phenotype can't be
two different shapes at once.
That leads to a
fundamental trade-off.
