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From YouTube: DevoWorm 2022, May 2 Update
Description
May 2 Update. Table of Contents for special issue on "Approaches to Developmental Networks", canola seeds inside the ball microscope. Topical reviews on energetics and biophysics in the Zebrafish embryo and experimental evolution in C. elegans.
A
A
This
will
be
a
lot
of
fun,
a
lot
of
papers
to
go
through,
but
we'll
get
through
it.
So,
let's
take
a
look
at
some
of
our
things
that
were
where
first
thing
we're
going
to
talk
about
is
our
special
issue
of
cells,
and
let
me
go
to
that.
So
this
is
a
special
issue.
The
title
is
approaches
the
developmental
network
structures
and,
if
you've
been
watching
us
recently,
we've
talked
about
this.
A
A
A
A
You
know
something
about
like
how
networks
interface
with
things
like
phase
transitions
or
symmetry
breaking,
and
you
know
they
kind
of
describe
the
dynamics
of
a
network,
so
it
could
just
as
easily
be
a
dynamical
network,
but
we
don't
know
exactly
what
papers
we're
going
to
get
for
this.
So
this
again
is
going
to
be
a
shorter
section.
These
two
sections.
A
A
So
we
want
to
leave
space
for
people
who
may
want
to
apply
a
network
approach
to
their
problem,
but
they
don't
necessarily
have
that
network
approach
worked
out.
So
this
could
be
like
you
know
some
question
in
genetics
having
to
do
with
phylogenys
or
gene
networks,
gene
regulatory
networks-
it
could
be
with
with
like
lineage
trees
and
the
network
aspects
of
those
and
by
network,
we're
being
very
generous,
we're
saying
it
could
be
like
a
complex
network.
It
could
be
a
directed
graph,
but
we
want
to
give
a
you
know.
A
Susan
crawford
young
has
been
working
on
her
ball
microscope
and
she
sent
me
some
pictures
of
canola
seed
that
she
put
inside
the
ball
microscope.
So
if
you
recall,
the
ball
microscope
is,
is
a
spherical
microscope
with
cameras
mounted
at
different
angles,
along
the
edge
and
so
there's.
I
think,
10
cameras
at
this
point
and
there
are
different
angles.
A
She
did
a
paper
actually
with
her
daughter
in
2021
that
she
published
in
biosystems
and
that
paper
had
some
samples
in
it.
It
had
some
bio
some
plant,
I
think,
plant
pollen
samples.
I
don't
know
if
she
used
the
canola
seed
in
that
paper,
but
she
sent
me
some
more
results
from
canola
seeds
and
I'm
going
to
show
you
that
now.
A
So
this
is
what
the
latest
version
of
the
ball
microscope,
so
it
has
the
latest
cameras
on
it
and
everything.
This
is
a
one
millimeter
diameter
canola
seed.
So
this
is
something
that
she
lives
in
the
in
the
great
plains
of
north
america.
So
they
have
canola
seeds.
You
know
they
can
just
grab
it
from
somewhere
and
use
it
to
do
this.
So
so
the
canola
seed
is
held
in
place
with
a
drop
of
water.
You
suspend
the
canola
seed
in
water
and
you
have
the
microscope
bottom
in
stages.
A
Microscope
one
side,
bottom,
microscopes,
two
to
five
side
top
microscope,
six
to
nine
and
top
microscope
is
ten.
So
what
that
means
is
that
you
have
this
stage
and
it's
in
the
middle
of
this
ball
and
the
ball
has
ten
microscopes
and
they're
mounted
in
the
following
way:
the
microscope
bottom
or
the
sort
of
the
thing
it's
going
to
show
the
bottom
and
the
stage
of
the
microscope
is
microscope
one.
A
The
side
bottom
microscopes
are
coming
in
from
the
bottom
sort
of
at
an
angle,
so
you're
seeing
sort
of
the
lower
half
of
the
canola
seed
at
different
orientations.
That
would
be
two
three
four
and
five.
So
that's
from
four
different
angles:
four
different
viewpoints
side
top
microscope
six
to
nine.
So
this
is
again
four
microscopes
around
the
top
at
an
angle
and
then
ten
is
from
the
very
top
and
it's
it's
it's
symmetrical
to
one
from
the
bottom.
A
So
you're
getting
you
know
points
of
view
at
I
don't
know
what
the
angle
is,
but
you're
getting
evenly
distributed.
Points
of
view
on
that.
On
that
thing
now
there
aren't
things
around
the
equator
of
the
canola
seeds
as
it
were,
but
that's
okay,
because
we're
going
to
use
these
images
later
and
we're
going
to
stitch
them
together.
So
there's
going
to
be
enough
resolution
at
least
for
a
decent.
A
You
know
decent
resolution
all
across
the
canola
seed.
So
this
is
again
she's
put
the
pictures
in
in
this
document.
So
this
is
number
one.
This
is
where
you
see
it
from
the
bottom,
so
you
can
see
the
stage
and
you
can
see
the
seed
through
the
middle
here.
So
you
can
see
the
bottom,
then
you
can
see
the
sides
here
you're
seeing
from
these
different
angles.
A
This
is
the
water
here
around
it,
and
this
is
the
light
source
as
you
can
see,
and
then
this
is
from
the
top
angle,
so
angular
from
the
top.
So
you
can
see
this.
This
is
a
different
side
of
the
canola
seed
and
you
can
see
that
the
illumination
is
making
it
look
different,
but
it's
just
another
angle,
and
so
these
need
to
be
aligned.
She's,
just
sending
me
the
images-
and
this
is
something
that
you're
going
to
find.
A
We've
been
talking
in
the
meetings
about
an
axolotl
embryo
at
the
one
cell
stage-
and
this
is
very
similar.
Similar
problem
is
that
a
we
have
this
background
problem
where
we
have
to
take
the
background
out,
but
also
that
you
have
you
know
they
have
to
be
aligned
and
then
they
have
to
be.
You
know
reconciled
at
each
angle,
so
this
is
going
to
be
a
image,
processing
task
and
then
finally,
10
is
from
the
very
top
so
you're,
seeing
downward
towards
the
stage
and
the
top
very
top
of
the
seed.
A
A
A
Okay,
so
now
we're
going
to
talk
about
two
sets
of
papers
and
we're
going
to
try
to
get
through
this,
I'm
not
going
to
go
in
very
deeply
in
each
paper,
but
I'm
going
to
talk
about
them.
I've
had
these
topics
interesting
because
they
do
deal
with
some
of
the
things
we
talk
about
a
lot
in
the
group,
so
the
first
one
is
this
free
energy
of
cell
cycles
and
chemical
oscillators
and
I've
been
able
to
catch
a
number
of
talks
on
this
topic.
This
is
a
topic
that
I'm
interested
in.
A
This
is
a
topic.
We
talked
a
little
bit
about
in
the
origins
of
the
embryo
paper.
The
first
paper
that
we
did.
We've
also
been
talking
about
the
origins
of
embryos
in
evolution.
So
I
think
this
these
works
may
fill
in
some
of
the
gaps
here.
They
don't
get
directly
at
some
of
the
questions
we
were
asking
in
these,
but
this
is
something
that
is
definitely,
I
think,
an
emerging
area
and
it's
something
that's
very
interesting
in
light
of
those
two
research
aims.
A
So
there
was
a
book
75
years
ago.
I
don't
know
how
many
years
yeah,
I
guess
it
was
about
75
years
ago
now
it
was
called
what
is
life
and
it
was
by
schroedinger
a
physicist
and
rob
phillips
who's.
A
biophysicist
has
written
a
paper
here
going
thinking
back
about
this
book
at
75
years.
So
how
is
this
book
aged?
What
is
it
teaching
us
these
sorts
of
things,
so
erwin
schoeninger,
there's
a
quote
that
says
these
facts
are
the
most
are
easily
the
most
interesting
that
science
has
revealed
in
our
day.
A
So
this
is
about
the
intersection
of
biology
and
physics
and
remember
at
the
time
people
were
kind
of
exploring
this
interface,
but
there
wasn't
like
the
kind
of
biophysics
community.
There
is
now
there
is
very
little
data
in
terms
of
the
physical
parameters
of
these
things
like
the
embryo
or
other
other
systems,
and
so
this
is
something
that
you
know
you
can
speculate
on
and
write
a
book
about,
and
then
it
leads
to
other
things
and
in
this
case
it
led
to
an
entirely
new
field.
A
A
A
A
All
these
things
take
place
in
space
and
time,
but
then
you
know
they're
interested
also
and
the
things
that
take
place
within
the
spatial
boundary
of
a
living
organism,
so
the
defining
and
how
you,
depending
on
how
you
define
that
it
could
be
that
the
spatial
boundary
is.
You
know
the
edges
of
the
organism,
the
edges
of
the
cells
in
the
organism.
It
could
be
the
edges
of
the
behavior
of
the
organism.
A
It
could
be
the
re,
you
know
the
ecological
range
of
the
organism,
so
you
know
there
are
a
lot
of
things
to
consider
here,
but
this
is
basically
the
the
province
of
biophysics
through
schrodinger's
book
is
often
hailed
for
its
influence
on
some
of
the
titans
you
found
in
molecular
biology.
This
article
takes
a
different
tack.
A
A
What
is
life
is
full
of
timely
outlooks
and
approaches
to
understanding
the
mysterious
living
world.
That
includes
and
surrounds
us,
and
can
instead
be
viewed
as
a
call
to
arms
to
tackle
the
great
unanswered
challenges
in
the
study
of
living
matters.
So
we
have
this
need
to
understand
sort
of
the
biophysics,
and
you
know
going
back
into
these
old
books.
It's
often
something
that
you,
you
find
a
lot
of
things
that
sort
of
inspiration
for
new
questions.
A
I
guess-
and
so
you
know,
this
was
something
that
well,
I'm
not
going
to
get
too
much
into
this,
because
it
kind
of
detracts
from
her
main
point
here,
but
just
to
get
into
sort
of
this
idea
of
accounting
for
the
living
organism,
so
the
meaning
of
accounting
for
the
living.
So
what
what
schrodinger
proposed
really
was
this
accounting
mechanism
and
accounting
in
terms
of
physics,
so
in
terms
of
like
energy
balances
and
in
terms
of
different
types
of
forces
and
other
things,
and
certainly
something
we
talk
about
in
this
group.
A
You
know
so
this
kind
of
goes
into
some
of
these.
You
know
aspects
of
modern
science
where
you
have
all
these
genes
and
proteins
and
names,
and
we
need
to
find
a
way
to
bring
these
together.
We
also
need
to
have
this
accounting
mechanism
for
things,
usually
a
quantitative
accounting
mechanism,
and
then
that
can
actually
allow
us
to
do
some
of
these
other
theoretical
pursuits.
A
So
one
of
the
outcome
of
these
struggles
in
the
19th
century
was
the
discovery
of
empirical
formula
that
gave
a
phenomenological
mathematical
description
to
measure
wavelengths
of
various
spectral
lines.
So
this
is
in
the
physics
domain
purely
and
what
they
did
was
they
had
this
accounting
of
what
was
there
and
then
they
used
equations
to
figure
out
the
relationships.
What
was
there
and
to
get
the
equations,
of
course,
is
one
thing
you
have
to
figure
out
the
relationships,
but
you
also
need
numbers
and
you
need
numbers.
A
You
need
to
know
what
the
range
you
know
the
ranges
of
values
are.
You
need
to
know
what
the
parameters
look
like,
and
we
can
do
this
a
simulation
now
back
then
they
didn't
have
computers
or
computational
power,
but
to
do
the
kinds
of
computation
you
might
need
to
say
simulate
this
equation,
but
you
did
have
to
have
numbers,
and
so
that's
that's
kind
of
where
we're
going
with
some
of
this
other
stuff
here.
So
the
next
article
is
the
free
energy
cost
of
accurate
biochemical
oscillations.
A
Reads:
oscillation
is
an
important
cellular
process
that
regulates
timing
of
different
vital
life
cycles.
However,
in
the
noisy,
cellular
environment
oscillations
can
be
highly
inaccurate
due
to
phase
fluctuations,
it
remains
poorly
understood
how
biochemical
circuits
suppress
phase
fluctuations
and
what
is
incurred.
What
is
the
incurred?
Thermodynamic
cost
here?
We
study
three
different
types
of
biochemical
oscillations
representing
basic
oscillation
motifs,
shared
by
all
known
systems
of
all
the
systems
studied.
A
We
find
that
oscillation
is
driven
by
multiple
irreversible
cycles
that
hydrolyze
the
fuel
molecule
such
as
atp.
A
number
of
phase.
Coherent
periods
is
proportional
to
the
free
energy
consumed
per
period.
Experimental
evidence
in
support
of
this
general
relationship
and
testable
predictions
are
also
presented.
A
A
There
are
different
ways
that
you
can
represent
the
chemical
reactions
and
things
that
matter
in
the
system.
So
I
don't
know
if
they
show
any
pictures
of
these
motifs,
but
this
is
so.
They
go
through
a
lot
of
different
biochemical
oscillations
here
and
they
talk
about
how
this
is.
They
don't
really
have
any
well
they
actually
they
do
at
the
end
here.
So
here's
some
of
the
motifs
this
this
one
here,
I
think,
is
a
let's
see
if
they
have
it
down
here.
A
Then
there's
this
one
here
called
the
repressor
later.
That's
where
you
have
three
things
going
on:
they're,
all
repressing
each
other.
So
it's
a
circular
system
of
repression
and
it's
you
know
it's
shutting
everything
down.
We
don't
know.
What's
coming
into
a
b
and
c,
it
could
be
that
there's
free
energy
coming
into
each
of
them,
but
the
interaction
of
b
and
c
and
c
and
a
and
a
and
b
are
all
repressive
in
nature.
A
So
it
shuts
down
it
inhibits
b,
no
matter
what's
coming
into
it
from
somewhere
else
and
then
c
is
inhibited
by
b
in
interactions
with
b
and
then
a
is
inhibiting
b.
So
this
is
what
they,
I
guess.
It's
like
a
festival
of
repression
or
something
repressed
later
and
then
third
is
the
substrate
depletion
motif.
So
this
is
where
you
have
s
things
coming
into
s
and
something's,
coming
into
p
and
they're,
both
excitatory
or
they're,
both
active
activatory,
I
guess,
and
then
p
goes
back.
A
A
A
If
we
go
to
this
paper
here,
which
is
measuring,
then
now
we
shift
embryonic
development
and
we
see
this
is
actually
rob
phillips
and
peter
foster
and
emmanuel
manuel
razo,
mejia
and
he's.
I
think
these
well
rob
phillips
is
at
caltech
and
I
think
they're
also
at
mit.
Some
of
these
people
were
at
mit
and
so
the
abstract
here.
This
is
one
of
these
papers
that
previews
another
paper.
So
we're
going
to
talk
about
the
other
paper,
but
this
is
the
one
that
kind
of
previews
it.
A
A
When
you
measure
the
heat
dissipation
of
the
system,
you
can
see
a
measurable
periodicity
which
makes
sense,
because,
if
cell
division
is
coordinated,
it's
going
to
occur
in
waves,
and
so
one
of
the
reasons
why
we
care
about
this
is
because
it
serves
as
a
proxy
for
looking
at
how
much
work
is
done,
biochemically
in
cell
division,
so
the
paper
we
just
saw
was
that
there
are
these
biochemical
reactions
going
on
and
they're
these
networks
that
are
occurring
and
these
networks
are
driving
cell
division.
So
cell
cycle,
for
example,
is
cyclical.
A
A
You
have
protein
synthesis
and
dna
replication,
and
these
things
are
all
occurring
at
certain
time
scales
and
then
this
is
their
heat
dissipation
rate
graph
versus
time.
So
this
is
time
in
the
embryo
on
seconds.
This
is
the
heat
dissipation.
It's
nano
joules
per
second,
I
believe-
and
it's
going
upward
over
developmental
time-
there's
a
little
lag
here,
but
then
it
goes
upward,
and
so
you
can
see
that
there's
each
of
these
things
that
are
going
on
incurs
a
free
energy
cost.
A
A
So
this
is
the
paper
and
developmental
cell
heat
oscillations
driven
by
the
embryonic
cell
cycle
reveal
the
energetic
cost
of
signaling
and
it's
by
jonathan,
rita,
rodenfels,
carla
new
new
gui
nugebauer
and
jonathan
howard,
and
so
this
is
where
they're
measuring
the
energetic
costs
of
her.
We
see
zebrafish
development
using
isothermal,
calometry,
so
they're.
Looking
it's
a
an
optical
method
where
they're
able
to
visualize
the
thermal
thermal
values
here
and
caliometry
is
basically
how
many
calories
or
kilocalories
are
being
burnt.
A
This
is
calories
are
actually
a
standard
measure
of
energy
consumption,
so
this
is
something
that
is
it's
an
older
method,
but
you
know
this
is
something
that
you
can
see.
So
you
have
these
different.
You
have
these
different
things.
These
molecular
things
that
they're
measuring
and
then
they
have
this
heat
flow
graph,
where
they're
showing
heat
flow
over
time
and
then
they're,
looking
at
the
micromolars
of
atp
atps
in
the
system
and
they're
able
to
look
at
this
they're
able
to
generate
this
graph.
A
So
this
is
one
of
these
graphs
with
two
y
axes.
The
heat
flow
in
nano
joules
per
second
is
equivalent
to
a
similar
scaling
of
micro
molars
of
atps
per
second.
So
this
is
this
just
kind
of
shows
you
over
the
cell
cycle.
What's
going
on
here,
there's
mitotic
entry,
mitotic
exit,
so
they're
able
to
identify
these
different
phases
of
the
cell
cycle,
so
yeah
they're
able
to
do
this.
A
A
So
you
know
there's
this
noisy
aspect
of
it
and
we
talked
about
noise
in
the
first
paper
as
a
sort
of
a
thing
that's
inherent
or
intrinsic
to
biochemistry
and
something
that
maybe
we
don't
think
enough
about
modeling
developmental
systems.
But
noise
is
ubiquitous
in
these
type
of
systems,
and
you
know,
modeling
measuring
noise
is
sort
of.
You
can
take
a
time
series
and
you
can
take
noise
components
out
using
something
like
like
a
a
frequency
domain
analysis
of
some
type.
A
A
A
A
You
bring
it
down
into
you,
recursively
sample
it
into
smaller
and
smaller
windows,
so
you
get
like
from
a
large
number
of
periods
down
like
64
periods.
So
you
do
this
like
multiples
of
two.
I
believe-
and
you
get
this
sort
of
power
at
different
scales
of
the
time
series,
so
you
average
it
at
different
scales
and
where
you
transform
it
at
different
scales,
and
you
end
up
with
these
different
what
they
call
wavelets
over
time.
So
for
each
of
these
you
have
different
periods.
A
The
sort
of
these
oscillation
cycles
using
wavelets,
and
so
this
is
a
nice
study
that
can
really
get
down
and
dirty
and
measure
a
lot
of
things
here,
get
some
good
numbers
on
them.
So
this
is
all
great
work
and
there's
a
final
paper
in
this
group
that
I
want
to
talk
about,
and
this
is
also
from
jonathan
rodenfels
and
some
other
colleagues.
Jonathan
howards
on
this
carly
nougabauer
is
also
on
this
again.
A
So
in
this
paper
they
actually
talk
about
the
increasing
plasma
membrane,
and
so
this
is
something
in
embryos
where
you
have
the
size
of
the
membrane
as
it's
increasing
and
it
contributes
to
the
energetic
cost
of
embryogenesis.
So
the
abstract
reads:
how
do
early
embryos
allocate
the
resources
stored
in
the
sperm
and
egg?
A
Recently
we
established
isothermal
telemetry
to
measure
heat
dissipation
by
living,
zebrafish
embryos
and
estimate
the
energetic,
specific
developmental
events-
and
this
was
largely
in
cell
division
during
the
reductive
cleavage
divisions.
The
rate
of
heat
dissipation
increases
from
around
60
nanojoules
per
second
at
the
two
cell
stage
to
around
90
nanojoules
per
second,
and
so
this
is
an
increase
at
the
1024
cell
stage.
So
this
is
like
you
know,
early
in
in
development,
from
two
cell
1024
and
there's
this
rate
of
heat
dissipation
that
increases,
and
so
why
is
that?
A
It's
not
necessarily
due
to
cell
division,
because
we
know
that
cell
division
is
probably
you
know
in
this
stage.
We
know
it's
more
coordinated,
so
we
don't
know
exactly
why
it's
being
driven
it's
and
it's
not
irregular.
It's
just
increases
here
we
ask
which
cellular
processes
drive
this
increasing,
energetic
cost.
We
present
evidence
that
cost
is
due
to
the
increase
in
the
total
surface
area
of
all
the
cells
in
the
embryo.
A
So,
as
the
embryo
expands
in
terms
of
the
number
of
cells
it
can
expand
in
size
as
well,
a
single
egg
is
going
to
be
much
smaller
than
the
embryo
ends.
Up
being
so
you
know
this
is
this
is
different
different
organisms
by
the
way,
but
the
embryo
volume
volume
is
going
to
you
know
change
size
and
depends
on
the
on
the
phase
of
the
embryo.
So,
first
embryo
volume
stays
constant
during
the
cleavage
stage,
indicating
that
the
increase
is
not
due
to
growth,
so
they
actually
rule
out
growth
as
a
factor
here.
A
This
suggests
some
aspect
of
cell
proliferation
contributes
to
these
costs,
so
this
is
actually
cell
proliferation
and
it's,
I
guess,
related
to
cell
division.
Third,
the
heat
increases
in
proportion
to
the
total
cell
surface
area,
rather
than
the
total
cell
number.
So
this
is
actually
not
due
to
actual
cell
number.
It's
that
there's
proliferation
in
their
surface
area
that
expands
and
then
this
is
so.
In
other
words,
you
have
this:
the
embryo
is
a
certain
size
and
in
this
stage
it
doesn't
increase
in
size.
A
So
this
is
what
they've,
so
that
was
a
third
and
then
fourth,
the
heat
increase
falls
within
the
range
of
the
estimated
costs
of
maintaining
and
assembling
plasma
membranes
and
associated
proteins.
Thus
the
increase
in
total
plasma
membrane
associated
with
cell
proliferation
is
likely
to
contribute
appreciably
to
total
energy
budget
to
the
embryo.
A
I
hope
you
found
that
useful.
I'm
going
to
go
through
some
papers
now
on
experimental
evolution,
and
this
is
kind
of
a
shift
from
what
I
was
talking
about
before,
but
several
years
ago
the
lab
actually
did
a
paper
on
experimental
evolution.
It's
actually
myself,
but
this
is
something
that
was
based
on
the
interests
of
the
group
here,
and
so
this
is
called
evolution
in
eggs
and
phases,
experimental
evolution
of
facility
and
reproductive
timing
in
c
elegans.
A
So
this
was
published
in
royal
society,
open
science
and
it
kind
of
goes
through
has
a
bunch
of
different.
It
has
a
wild
tape
and
a
bunch
of
different
defined
mutants
that
were
used
here
and
a
number
of
genealogies
were
created.
So
what
happened?
Was
you
had,
like
a
you
know,
a
population
of
these
different
genetic
strains,
and
then
there
was
some
selection
process
where
certain
worms
were
selected
out
and
passed
to
the
next
generation,
a
number
of
generation.
A
A
A
Then
you
passage
a
small
number
of
that
population
into
a
new
generation
and
then
they
have
offspring
and
those
are
measured.
And
then
those
are
selected
and
passed
on
and
they're
selected
at
random,
so
they're
not
selected
by
any
particular
feature
and
then
the
idea
is
to
go
through
and
look
at
the
different
strains
to
see
how
they
perform,
and
so
you
can
see
that
the
wild
type
is
probably
the
best
marker
here
where
you
can
look
at
the
performance,
and
this
was
more
or
less
a
linear
increase
over
20
generations.
A
I
think
this
plot
only
includes
20.
I
think
there
were
actually
25
total,
but
and
then
you
know
you
can
compare
the
rest
of
the
strains
to
that
wild
type.
So
these
aak
strains
were
there
was
some
energetic
aspect
to
them.
That
was,
you
know,
an
energetic
mutation,
so
the
mutation
impacted
their
ability
to
reproduce,
and
so
this
had
some
effect
on.
You
know
their
ability
to
go
through
this
experiment.
A
There's
a
little
bit
less
than
the
wild
type,
but
depending
the
double
mutant
was
actually
better
than
the
single
mutant
aak-1,
and
I
think
aak2
is
much
less
robust
in
its
fucunity,
its
populations,
average
population
size
over
20
generations,
and
then
you
had
other
mutants
like
death
16
to
f7.
These
were
alternate
mutants,
that
they
had
developmental
mutations
that
can
affect
developmental
aspects
and
they
didn't
perform
as
well
as
into
there
were
some
that
kind
of
died
off
here.
A
These
were
double
mutants
ak
and
def,
which
didn't
make
it
past
15
generations.
This
is
notable
because
they
just
couldn't
be.
You
know
you
weren't
able
to
they
weren't
able
to
increase
their
fuconity.
In
fact,
their
faculty
fell
to
zero,
as
the
experiment
went
on,
so
this
is
really
notable,
and
these
double
mutants
actually
had
both
these
this
metabolic
defect
in
this
developmental
defect
or
this
developmental
mutation.
So
this
is
something
that
was
interesting
in
terms
of
the
biology
of
these
mutants.
A
A
Where
you
get
you
have
this
population
at
generation,
one
you
select
from
that
population,
you
put
them
into
a
new
new
setting,
they
generate
their
offspring.
You
select
from
that.
You
generate
an
offspring
and
the
idea
is:
is
that
you're,
just
selecting
at
random,
so
you're
not
really
imposing
any
sort
of
artificial
selection
on
it
necessarily
but
you're?
Looking
at
what
happens
when
you
keep
repeat,
you
know
when
you
keep
passing
on
these
individuals
and
restricting
the
gene
flow
of
the
population.
So
usually
it's
like
one.
A
If
there
are
genes
or
they're
different
mutations
that
might
be
deleterious,
it
might
crash
the
population,
it
might
increase
the
population
and
so
for
these
different
mutants
there's
a
you
know,
there's
differences
there,
and
so
you
basically
get
a
lot
of
genetic
drift,
but
you
also
get
this
ability
to
evolve
because
you're
you're
not
picking
from
the
same
you're,
not
necessarily
picking
from
the
same
lineage
they're
mutations
that
are
generated.
So
it's
not
not
just
like
picking
from
the
same
well
every
time.
A
So
this
is
just
kind
of
showing
some
more
of
the
data
here,
so
yeah.
So
there's
some
caveats
to
this
approach,
but
this
is
basically
how
this
works
and
c
elegans
is
a
nice
model
for
evolution,
experimental
evolution,
because
you
can
do
a
lot
of
things
with
it
now,
there's
some
papers
on
not
just
on
experimental
evolution.
The
way
I
showed
you,
but
there
are
other
ways
you
can
do
this,
so
you
can
there's
a
paper
called
natural
genetic
variation
and
multi-generational
phenotypes
of
c
elegans.
This
is
marianne.
A
In
brief,
this
paper
discovers
that
some
c
elegans
wild
isolates
become
sterile
after
several
generations
of
high
temperature.
So
you
can
go
through
several
generations
at
high
temperature,
and
this
is
a
trait
that
can
be
not
only
can
sterilize
them,
but
it's
reversible,
so
it
corresponds
to
alterations
and
non-genetic
inheritance
phenomena.
So
you
can
actually
look
not
just
the
genetics
but
the
epigenetics,
and
in
this
in
this
type
of
model,
it's
very
simple
to
set
up.
A
But
you
can
set
up
these
conditions
and
you
can
go
out
quite
a
while,
because
the
c
elegans
generation
is
something
like
three
to
four
days.
You
can
spend
a
lot.
You
can
get
a
lot
data
on
a
lot
of
generations,
so
this
is
a
nice
paper
where
they
identify
the
in
the
wild,
isolate
my
ten.
So
I
showed
you
the
end
in
the
last
paper.
I
showed
you
an
example
of
the
n2
mutant.
This
is
a
variant
of
that
of
a
wild
type
mutant,
it's
not
into
its
my
10..
A
Now
this
can
be
actually
quite
powerful
because
you
can
actually
look
up
to
14
generations
ahead
and
ascribe
it
to
transgenerational
transmission.
So
this
is
a
paper
transgenerational
transmission
of
environmental
information
in
c
elegans-
and
this
is
using
this
is
where
you
have
so.
In
this
case,
the
environment
experienced
by
an
animal
can
sometimes
influence
gene
expression
for
one
or
a
few
subsequent
generations.
A
Here
we
report
the
observation
that
a
temperature-induced
change
in
expression
from
a
c
elegans
heterochromatic
gene
array
can
endure
for
at
least
14
generations
inherent
since
it's
primarily
in
what
they
call
cysts
with
the
locus
incurs
through
both
oocytes
and
sperm
and
is
associated
with
altered
trimethylation
of
histone
h3yz9,
which
is
this
marker
here
and
so
before.
The
onset
of
zygotic
transcription
expression.
Profiling
reveals
that
the
temperature-induced
expression
from
endogenous
repressed
repeats
also
be
inherited
for
multiple
generations.
A
They
have
these
candidate
markers
that
they
use
and
they're
looking
at
transmission,
transgenerational
transmission,
and
then
this
set
of
experiments
they
found
that
they
could
look
ahead
up
to
14
generations,
so
something
can
be
inherited
for
up
to
14
generations
for
high
temperature
memory.
So
in
this
case
you
have
high
temperature
memory.
A
A
There
you
know
there
are
also
other
opportunities
in
c
elegans
experimental
evolution,
so
this
paper
is
sort
of
advocacy
for
mainstreaming
c
elegans
in
experimental
evolution
and,
as
I
mentioned
before,
it's
a
very
powerful
tool.
Potentially
it
has
a
very
short
generation
time.
It's
very
easy
to
raise
a
large
number
of
individuals.
A
The
genetic
basis
of
things
is
pretty
well
worked
out,
so
we
know
the
genome,
for
example,
and
we
know
what
the
defined
mutations
look
like
and
it's
very
easy
to
do.
Genetic
screens
I
mean
relatively
easy,
and
so
you
have
all
these
things
going
for
it.
So
it's
a
very
nice
system
to
look
at
and
probe,
and
you
know,
aside
from
that,
you
know,
there's
a
lot.
A
A
So
you
have
all
these
things
you
can
look
at
and
they
they
have
some
an
you
know
analogy
to
some
of
the
mammalian
systems
that
people
work
on.
So
this
is
something
that
you
know
we're
really
interested
in
kind
of
using.
Maybe
it's
a
very
easy
way
to
look
at
some
of
these
issues.
There
are
also
things
like
heat,
stress
and
other
adaptive
processes
that
are
very
easy
to
kind
of
probe
and
manipulate,
so
we
can
actually
get
some
pretty
good
information
about
biological
plasticity.
A
I
see
elegance
isn't
really
known
for
its
biological
plasticity,
but
there
are
ways
to
probe
this
in
development.
So
this
is
something
that
you
know
actually
allows
you
to
do
a
lot
of
really
interesting
things.
So
the
life
cycle
is
short
and
it's
well
characterized
and
you
have
mutant
alleles
that
you
can
look
at
define
the
you
know,
define
mutations.
A
You
can
actually
create
custom
alleles
using
genome
editing,
so
you
can
use
something
like
crispr
to
create
manipulations
in
the
genome
and
then
you
can
actually
understand
not
only
that,
but
these
extra
chromosomal
transgenes
and
other
types
of
epigenetic
inheritance
using
c
elegans
and
it's
been
observed
in
c
elegans.
It's
usually
relates
to
temperature
or
food.
A
A
This
paper
evolution
and
the
wa
or
c
elegans
evolution
or
cantor
hepatitis
evolution
in
the
wild.
I
should
say
this
cantor
hepatitis
being
the
genus
kind
of
talks
about
some
of
the
natural
history
of
cantor
hepatitis.
So
we
not
only
have
cantor
hepatitis
elegans,
but
we
have
other
species
in
that
or
in
that
genus
and
they're
they're
somewhat
different
than
c
elegans.
They
can
be
different
and
you
know
understanding
that
natural
diversity
is
actually
pretty
important
for
getting
a
handle
on
what
we're
seeing
with
experimental
evolution.
A
So,
even
even
just
some
of
the
things
that
we
talk
about
in
the
group
like
lineage,
trees
and
things
like
that,
those
are
not
you
know.
Those
are
not
necessarily
typical
of
even
members
of
its
own
genus,
so
they're
worth
kind
of
understanding,
the
natural
variation
and,
of
course,
a
natural
variation
is
very
important
to
interpreting
what
you
see
in
your
experimental
evolution
experiments.
A
A
A
We
use
the
n2
strain,
for
example,
as
a
wild
type,
that's
a
strain
from
nature,
but
it's
also
been
back
crossed
quite
a
bit.
So
it's
not
like
I
at
one
point
I
actually
was
able
to
harvest
c
elegans
at
a
out
of
a
black
backyard,
mulch
pit
or
a
mulch
pile,
and
so
I
was
able
to
take
samples
out
and
get
c
elegans
out
of
there.
A
Those
probably
have
a
lot
different
genetic
makeup
than
something
in
the
lab
I
didn't
genotype
it,
but
one
of
those
n2
strains
is
going
to
look
very
different
genetically
and
then
the
the
define
newtons,
the
ones
with
specific
mutations
that
have
specific
functions.
Those
are
usually
derived
from
the
n2
string.
A
So
understanding
a
lot
of
this
natural
diversity
is
very
important
and
especially
in
the
open
worm
effort.
I
think
there's
a
lot
of
opportunity
to
think
more
deeply
about
that
natural
variation
and
how
it
might
play
a
role
in
simulating
the
worm
and
simulating
some
of
the
things
that
we
kind
of
take
for
granted
in
terms
of
this
and
knowing
every
cell
and
knowing
every
connection
in
the
in
the
connectome.
So
this
is
this
is
something
that's
quite
quite
useful
as
well.
A
So
talking
about
changes
that
occur
in
development
that
are
that
come
from
sort
of
evolutionary
change,
so
you
get
changes
in
development
or
changes
in
evolution
that
translate
into
changes
in
development
and
there's
an
interplay
there.
If
you
were
to
do
these
experimental
evolution
studies,
you
could
actually
manipulate,
perhaps
developmental
mechanisms
there's
a
lot
of
interplay
between
the
production
of
offspring
and
that
larval
period
that
larval
stage
and
there's
a
lot
of
biological
plasticity
in
that
larval
stage.
A
And
finally,
one
of
the
things
that
we
like
to
think
about
with
c
elegans
in
terms
of
medical
research
is
the
lifespan
or
life
expectancy.
So
a
lot
of
people
have
studied
c
elegans
to
understand
aging
in
humans,
but
in
general,
some
of
the
genes
involved
in
aging
are
homologous
to
some
of
the
genes
that
we
find
in
humans.
And,
of
course,
you
can
see
you
know,
with
a
life
span
of
several
days,
it's
very
easy
to
look
at
the
lifespan
over
and
over
and
over
again,
and
even
like
look
at
how
it
evolves.
A
So
this
paper
is
about
the
evolution
of
lifespan
and
c
elegans,
and
so
it
was
proposed
almost
50
years
ago.
That
aging
is
not
adaptive
and
it's
the
consequence
of
a
decline
in
the
force
of
natural
selection
with
age.
So
this
is
leading
this
led
to
a
theory
that
aging
results
from
detrimental
effects
late
in
life
of
genes
that
act
beneficially
in
early
life.
So
there's
there's
a
theory
that
people
have
kind
of
come
up
with
in
c
elegans
using
c
elegans.
A
This
group
showed
that
a
mutation
that
greatly
increases
the
lifespan
of
the
nematode
c
elegans
does
indeed
exhibit
a
fitness
cost,
as
demonstrated
during
starvation
cycles.
They
may
mimic
field
conditions,
thereby
validating
the
pleiotropy
theory
of
aging.
So
this
is
something
that
is
this
thing
that
they
proposed
50
years
ago,
they're
actually
using
c
elegans
to
sort
of
find
evidence
for
it,
and
so
in
c
elegans.
You
can
actually
starve
them
pretty
easily
in
development.
A
You
can
do
l1
starvation,
which
is
where
you
take
the
take
the
eggs
and
you
harvest
them,
and
you
place
them
in
on
a
plate.
So
you
can
synchronize
the
population
and
you
actually
don't
place
them
on
a
plate
right
away.
You
put
them
in
a
tube
in
buffer
for
about
a
day
or
two.
You
can
keep
them.
I
think
for
up
to
two
weeks,
and
this
starves,
the
eggs.
A
But
then
they
also
exhibit
changes
due
to
that
starvation
experience
and
it
affects
the
way
that
they
sort
of
mature,
and
so
you
can
do
these
starvation
cycles.
You
know
over
multiple
generations
and
you
can
actually
force
a
certain
adaptation,
so
you
can
observe,
for
example,
these
epigenetic
mutations,
or
these
epigenetic
changes
you
can
observe.
Maybe
you
can
select
for
mutations,
that
favor
starvation
environment,
so
there
are
different
things
you
can
do
these
these
starvation
experiments
are
very
well
characterized
in
the
literature,
but
combining
that
with
experimental
evolution.
A
Give
you
really
interesting
results
like
this.
That
can
actually
address
some
of
the
issues
with
in
the
aging
literature-
and
maybe
some
you
know-
maybe
some
of
the
phenotypic
plasticity
literature
as
well,
so
they
were
able
to
actually
identify
some
alleles
here,
some
genetic
alleles
that
were
involved
in
aging
and
yeah.
This
is
an
interesting
little
communication
here.
The
observed
fitness
costs
associated
with
field-like
conditions,
which
means
that
it
would
be
analogous
as
something
you'd
find
in
nature,
provides
an
example
of
a
single
gene
that
acts
early
in
life
aging
in
darwinian.
A
Fitness
is
predicted
by
the
pleiotropy
theory
of
aging,
so
that's
gliotropy
is
where
you
have
a
gene
that
has
multiple
effects.
So
you
know
this
is
something
that
people
proposed
and
they
were
able.
This
actually
goes
back
to
1957
an
old
paper
by
gc
williams
in
evolution,
so
this
is
again
something
that
was
published
in
the
year
2000.