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From YouTube: DevoWorm (2023, Meeting #19): Fossil Diatoms, SAM prompting, curvatures and planes, cell lineages
Description
The fossil origins of Diatoms, Segment Anything Model and prompting with image masks and centroids, curvatures and planes in collective cell behaviors, guide to C. elegans developmental cell lineages and lineage trees (GSoC community resource). Attendees: Sushmanth Reddy Mereddy, Morgan Hough, Susan Crawford-Young, Bradly Alicea, Jiahang Li, and Richard Gordon.
B
A
A
B
A
A
C
A
A
Okay,
well,
we
can
do
this
later
in
the
meeting,
so
so
the
first
thing
I
want
to
talk
about.
Was
we
have
a
couple
of
I?
Guess
we'll
get
right
into
papers
and
and
things
that
we
have
been
waiting
on?
I
don't
have
any
features
today,
but
let
me
share
my
screen
and
so
a
number
of
interesting
things
here.
A
The
first
is
this
collection
of
papers,
we've
been
getting
on
curvatures
and
planes
and
embryos,
and
then
there's
this
paper
on
uncertainties
of
the
fossil
record
for
diatoms,
so
in
a
dick
is
interested
in
diatoms
and
sort
of
early
life.
So
that
was
something
that
well
I
mean
you
can
watch
it
later.
A
A
So
the
first
paper
here
is
uncertainty,
certainties
surrounding
the
oldest
fossil
record
of
diatoms.
This
is
the
the
kind
of
organisms
we
work
with
the
ones
you've
seen
before
of
all
different
shapes.
The
cells
are
all
different
shapes.
They
can
be
single
cell
organisms,
they
can
be
multicellular
organisms
and
the
you've
seen
the
the
colonies
of
single
cell
organisms
that
move
like
an
accordion.
A
Those
are
all
diatoms
and
so
there's
a
lot
of
diversity
there
and
the
question
is,
is
what
are
their
evolutionary
origins,
and
so
this
is
a
nice
paper
on
sort
of
getting
at
the
origins
of
diatoms
in
the
tree
of
life
and
where
we
find
the
earliest
fossil
record
of
them.
Now
they're
made
of
the
silicate,
the
cell
walls
are
made
of
silica
So.
In
theory,
they
should
be
easy
to
preserve.
So
that's
good
that
we
have
those
around,
but
we
don't.
A
You
know
we
it
still
unclear
as
to
where
they
fit
into
the
fossil
record.
So
molecular
clocks
estimate
that
diaton
microalgae,
one
of
the
world's
foremost
primary
producers,
meaning
that
they
produce
energy
through
photosynthesis
for
secondary
producers,
which
are
organisms
that
eat
them,
and
you
know
they're
able
to
produce
energy.
So
a
lot
of
plant
species
a
lot
of
algae
or
you
know
primary
producers,
that's
how
they
refer
to
them
ecologically,
and
then
they
originated
near
the
Triassic
Jurassic
boundary,
which
was
about
200
million
years
ago.
A
So
this
was
in
the
time
of
the
dinosaurs
and
and
close
to
the
sort
of
a
first
large
dinosaurs,
I,
guess
yeah,
which
is
also
close
in
age
to
the
earliest,
generally
accepted
diatom
fossils
from
the
genus
Pikes
of
Pike
City,
coola
I.
Guess
that's
how
you
say
it
during
an
extension:
extensive
search
for
Jurassic
diatoms
from
25
sites
worldwide,
three
sites
yielded
microfossils,
initially
recognized
as
diatoms.
A
That's
one
of
the
problems
you'll
have
again
and
again
in
the
fossil
record,
with
things
like
trees
and
other
other
types
of
parts
of
organisms,
you
have
to
get
it
into
the
context
of
the
organ
whole
organism
or
in
the
context
of
some
sort
of
ecological
thing.
So
you
know
it's
exceedingly
rare,
for
example,
to
find
a
full
skeleton
of
say,
like
an
ancestral,
hominid
or
a
a
dinosaur,
but
they
can
be
found.
A
A
Although
these
fossils
resemble
some
extent
radio-centric
diatoms
and
have
character
states,
which
means
that
they
have
characteristics
that
may
have
been
similar
to
those
of
ancestral
diatoms,
we
describe
numerous
sources
of
uncertainty
regarding
the
reliability
of
these
records.
We
conclude
that
the
lower
Jurassic
fossils
were
most
likely
concern.
Is
nano
fossils,
whereas
the
middle
of
Jurassic
species
have
been
reassigned
to
the
lower
Cretaceous
and
is
likely
a
testate
amoeba
diatom.
A
So
this
widens
the
gap
between
the
time
of
origin
of
the
oldest
and
the
oldest
abundant
fossil
diatom
record
to
75
million
years.
So,
basically,
those
earlier
finds
they
couldn't
be
sure
that
they
were
diatoms.
They
think
there
maybe
have
been
some
sort
of
amoeba,
and
so
now
they
they
still
are
sort
of
left.
What
the
mystery
as
to
the
earliest
diatom
versus
the
first
abundant
fossil
diatom
record,
which
is
needed
to
75
million
years.
So
this
just
talks
about
how
it's
difficult
to
find
and
just
you
know,
to
discover
and
validate
microfossils.
A
So
microfossils
are
these.
If
you
recall
the
your
average
diatom
is
maybe
on
the
order
of
100
microns,
which
is
a
very
small
amount,
a
very
small
size
and
usually
have
to
identify
it
very
carefully
under
a
microscope.
You
have
to
sort
of
search
pieces
of
of
rock
to
find
these.
So
it's
not,
you
know
easy
to
do,
but
and
you
can
confuse
it
for
other
types
of
organisms
at
the
time.
So
it's
quite
a
it's
quite
a
game,
so
they
talk
about
diatoms
and
they're.
A
You
know
they're
found
all
they
were
found
all
over
the
world
and
they
continue
to
be
found
all
over
the
world,
and
you
know
they.
They
know
kind
of
sort
of
the
first
abundant
fossil
record
they've
been
searching
different
candidate
sites
for
even
earlier
finds,
but
you
know
they're
not
quite
sure
that
they
found
the
earliest
diatoms,
but
the
you
know
the
search
continues.
I
guess
so.
A
This
is
what
a
diatom.
This
is
a
electron
microscopy
image
of
an
assemblage.
What
it
looks
like
this
is
a
species
of
diatom
here
this
is
from
a
piece
of
rock
that
they've
gotten
and
they're
scat
doing
the
microscopy
on
it,
and
they
show
these
little.
They
must
look
like
Chambers
or
holes
and
that's
sort
of
the
remains
of
the
diatoms.
A
So
and
then
this
is
a
close-up
of
that,
where
they're
able
Elemental
mapping
of
oxygen
and
carbon
and
a
pluralism
specimen.
So
this
is
where
they're
looking
at
the
cell
wall
and
they're
looking
at
sort
of
fixation
of
different
oxygen
and
carbon
molecules
going
through
the
cell
wall,
so
this
is
really
small.
This
is
five
microns
this
line,
so
that's
several
of
these
different
pores,
and
so
you
can
see
how
small
these
are,
and
so
this
is
a
very,
very
micro,
microfossil.
A
This
preservation
of
this
is
actually
quite
good
for
the
size
of
a
feature.
So
this
is
the
kind
of
feature
that
you
find
in
diatoms
if
you're
looking
close
up
on
the
cell
wall-
and
this
is
certainly
you
know-
it's
not
something
that
you
know
there's
some
ways
you
can
tell
different
species
from
one
another.
So
it's
not
that
they
don't
have
the
resolution,
it's
just
that
it.
Sometimes
there
aren't
the
kind
of
characters
to
differentiate
them.
A
Nevertheless,
this
is
more
electron
microscopy
of
diatoms,
recovered
from
shirts
or
rocks
from
this
other
site.
So
these
are
examples
here.
This
is
10
microns.
So
this
is
a
little
bit
wider
shot
of
this
specimen
and
you
can
see
again
the
the
different
samples
here
and
so
they're
I
think
they're.
All
at
this.
This
is
the
10
Micron
scale
for
all
of
these
images.
So
you
can
see
that
there's
you
know
they're
able
to
identify
certain
aspects
of
the
cell
wall
and
some
of
the
pores.
A
Let's
see
this
one
here
is
a
compilation
of
specimens
used
to
validate
lower
Jurassic
fossils.
So
now
they
have
to
validate
those
fossils.
They
have
to
look
at
other
candidate
fossils.
They
have
to
know
what
it
is,
so
they
have
to
compare
it
with
other
things
to
make
sure
it's
similar
different,
and
so
they've
done
this
with
a
lot
of
these
different
fossils
and
drawings
of
fossils.
A
A
A
C
C
A
Is
the
I
think
the
timeline
of
their
collected
samples?
So
this
is
this:
is
a
depth
in
centimeters,
so
they're
going
down
in
in
depth
in
this
at
the
site.
They
have
these
different
samples.
They
come
from
these
different
places.
This
is
the
number
of
years
ago
that
these
samples
were
gotten
from
so
from
181
million
years
to
182.7
million
years
going
down
into
the
assemblage,
and
so
the
positions
of
the
collected
samples
going
down.
This
is
the
placidonia
Shale.
So
this
is
actually
their
position
in
that
Shale.
A
So
this
is
a
compilation
of
specimenses
to
identify,
lower
Cretaceous
fossils.
So
again,
they're
doing
this
comparative
work.
They
have
these
very
things
from
you
know
from
the
very
early
19th
century
on
to
like
1902.
So
you
know
they
didn't,
have
electron
microscopes
back
then
so
they
use
drawings
of
things.
A
Sometimes
the
drawings
are
very
precise,
and
so
you
know
you
can
pick
up
features
from
those
drawings,
but
they're
not
you
know,
electron
microscopy,
so
we
have
to
sort
of
use
these
as
guides,
but
they're
not
they're
imprecise
because
they're
hand
drawings.
So
this
is
an
example
of
some
of
the
specimens
used
to
identify
these.
A
A
A
And
you
know
there
are
a
lot
of
challenges
with
different
types
of
assemblages
of
fossils,
and
you
know
identifying
I
mean
they
know
how
to
date
things,
but
they
can't.
You
know,
there's
this
comparison
between
the
thing.
They
think
it
is
and
then
other
types
of
things
that
are
around
at
the
time
and
that's
the
biggest
challenge,
I.
Think
because
you
have
things
that
look
very
similar,
and
so
you
know
it's
going
to
be
hard
to
really
definitively
say
this
was
part
of
the
species
or
not.
A
It
really
does
depend
on
these
characters
on
the
phenotype
of
the
organism.
So
in
the
image
you
have
to
identify
features
that
are
unique
to
that
species,
and
sometimes
that's
hard
to
do
because
you
have
the
preservation,
isn't
perfect.
You
have
things
that
look
very
similar
and
you
know
there
are
a
lot
of
things
that
we
you
know
it's
hard
to
do.
So
that's
that's
one
of
the
big
challenges
here
and
so
that's
they
put
a
whole
section
in
here,
because
I
think
they're
not
quite
sure
what
they
have
it.
A
You
know
this
is
despite
this
being
well.
This
is
in
scientific
reports.
I
was
going
to
say
as
a
nature
paper,
but
it's
not
necessarily
the
nature,
so
they
they
kind
of
go
through.
Some
of
the
one
of
the
other
challenges,
too,
is
that
you
can
have
these
reassessments
of
the
fossil
record.
In
other
words,
you
can
you
know.
A
Well,
people
go
back
and
and
correct
things
that
have
been
found
previously,
so
the
report
early
reports
that
were
made
that
have
to
be
updated
and
that
updates
the
taxonomy
of
of
these
different
species
groupings-
and
you
know
even
sometimes
things
are
misidentified
because
for
various
reasons
people
have
different
Criterion
for
classifying
species,
things
into
species,
and
so
they're
all
debates
about
that.
So
so,
as
described
above,
the
third
purported
diatom
fossil
described
by
rothblatt's
pianoata,
has
all
the
features
of
a
testate
amoeba
reclassification
of
rothplatz's.
A
I'll
get
that
right
at
some
point,
whether
it
is
a
diatom
Cal
service,
nanophosphor,
testate
amoeba.
So
there
are
all
these
these
three
possibilities
for
what
they
might
be
and
they
don't
really
know
yet,
or
they
still
don't
know
and
so
Pixar
to
cool
Love
Remains,
one
of
the
most
confusing
names
in
the
diatom
literature
to
Foy
dissect
and
clarify
the
convoluted
taxonomic
and
nomenclature
history.
A
It
appears
that
one
inconsistent
usage
in
overall
poor
documentation
to
lack
of
literature
citations
to
earlier
works
and
through
a
series
of
nomenclatural
changes
involving
key
species
and
four
changes
in
our
understanding
of
exactly
which
phylogenetic
lineage.
This
species
is
thought
to
represent,
and
that
compounds
our
problems.
So
that's
that's
what
yeah?
That's
what
I'll
talk
about
for
that
paper?
C
D
Now,
before
starting
a
segmentation
of
cells
or
membrane,
I
was
able
to
I
mean
actually
segment
anything
model
from
Facebook
hasn't
released,
any
code
or
library
to
fine
tune
the
model.
So
it's
a
bit
kind
of
hard,
so
I
somehow
managed
to
fine
tune
the
model
another
data
set
for
for
tumor
detection,
I
just
implemented
it
I
have
seen
whether
I
couldn't
we
have.
We
need
three
things
actually
for
segmentation
for
using
Sam
model.
Those
all
are.
Let
me.
A
D
D
Their
data
sets
to
test
out.
Actually
we
need
three
things
to
understand:
to
give
input
for
the
Sam
Actually,
because
three
things
are
a
ground.
Truth
mask
is
needed
round.
Truth.
Sorry,
a
fluorescence
image
like
this
type
of
real
image,
is
needed
and
a
masked
image
for
the
ground.
Truth
of
this
is
needed
and
a
bonding.
D
Instrument-
okay,
here
you
can
save
it.
I
think
we
need
three
things
to
give
a
prompt
to
an
image
if
a
valid
image
segmentation
marks
or
a
bonding
box
or
this
one
is
needed
in
their
code
also,
they
clearly
mentioned
actually
about
these
three
things.
So
my
idea
of
a
project
right
now,
I
have
this
data,
says
cell
tracking
challenge
data
set
to
implement
segmentation.
Here
we
have
the
fluorescence
image,
which
is
the
microscopy
image
and
the
ground
growth
of
segmentation
is
also
there.
With
that
we
have
some.
Can
you
see
here?
A
D
D
Okay,
this
Library.
Actually
there
is
a
trick
best
for
integrating
Sam
anything
model
as
encoder
they
are
using.
There
are
two
things
here:
encoders
and
decoders.
Encoders
generally,
we
give
an
image
and
convert
into
an
era.
The
numpy
error
is
converted
into
which
we
can
understand
here
they
are
using
bi
division,
transformer
for
as
an
encoder,
but
for
decoder
they
are
using
as
unit.
This
is
another
implementation,
and
this
is
working
fine,
so
Mike.
What
he
told
me
is
to
use
this
library
right
and
see
by
this.
D
I
mean
the
segment
the
pair
tumor
is
like
that.
Only
for
our
problem.
We
have
the
problems
right.
We
we
can
give
this
problem.
This
method
and
and
general
method
is
freezing
the
last
layer
of
the
network
and
then
giving
it
as
there
are
actually
three
methods
to
implement,
and
then
he
told
to
compare
three
models
in
his
same
data
set
then
extract
the
rates
file
and
compare
the
model
accuracy.
D
That's
a
bit
I
couldn't
explain
in
a
clear
way
and
write
out
a
Blog
about
these
things
and
then
send
it
to
you-
and
this
was
these-
are
three
implementations.
I
have
so
many
doubts,
but
my
already
solved
them
already.
Don't
like
use
this
method
used
that
you
can
easily
solve
them
all
right
now.
I
just
need
to
implement
what
all
the
ideas
he
given
to
me
then
this
week,
I
will
work
and
work
on
these
methods
and
I
will
try
to
implement
them.
Okay
last
was
completely
confused
about
the
segment
editing
model.
D
And
I've
seen
other
resources.
Also
in
a
coding
period
begins.
I
will
start
coding
up,
but
actually
there
are
my
exams
from
May
28
to
June
13th,
whether
I
could
manage
both
of
them
or
not.
If
I
couldn't
manage
I
will
let
you
in
the
slack
or
somewhere
okay
for
one
week,
I
will
work
on
my
exams
and
I
will
come
back
and
I'll
start
quoting
recording.
Okay,.
A
A
Is
I
can't
remember
the
length
of
the
project
I
think
it's
a
shorter
project.
So
it's
you
know
20
hours
a
week
anyways.
So
it's
not
full
time.
So
you
know.
If
you
have
to
push
things
to
another
week,
you
know
you'll
still
be
on
track.
Probably
a
few.
You
know
it's
not
like
you
have
to
do
it
yeah.
This
is
350
hours
project
right,
that's
not
sure.
Okay,
it
isn't
well,
that's
fine.
I
mean
you
know.
We
can
even
extend
it
at
the
end
if
you
need
to,
but
you
know.
D
D
D
D
A
And
so
yeah
we've
done
a
little
bit
of
that
in
the
past,
like
with
with
masks,
and
that
and
it's
you
know
it's
a
little
chat,
I
mean
I.
Guess
it
wouldn't
be
that
challenging.
So
you
do
you
plan
to
make
these
from,
like
other,
like
just
segmenting
the
data
getting
like
well
I,
guess
you
have
fluorescent
images
in
some
of
the
data
sets.
So
that's
that
already
provides
you
with.
A
A
Yeah
it
should.
It
should
be
enough
to
create
a
mask
like
from
some
of
the
data
sets
because
I
mean
it
does
basic
segmenting
it'll,
give
you
like
a
map
I,
guess
it
will
give
you
an
image
set
of
coordinates
and
it'll.
Give
you
basically
the
places
where
it
thinks
the
centroids
are,
and
then
you
know
that
that
would
be
enough
for
a
mask
yeah
yeah.
So
that's
good.
D
I
was
I
need
to
discuss
with
my
neck.
Actually,
the
cell
lineage
population
model
was
done.
We
are
updating
whole
devoland,
so
I
don't
have
any
knowledge
regarding
linear
cell
lineage
analysis
or
some
kind
of
stuff.
So
I
scheduled
a
meet
with
him
this
weekend
or
coming
weekend
or
middle
of
the
week.
I
will
discuss
with
him.
I,
don't
know
whether
it
is
a
classification
type
of
problem
or
what
kind
of
it
is
trying
to
read
his
code.
Understand
it,
but
it's
like
fit
I
need
to
learn
some
other
more
things.
D
D
A
All
right,
yeah,
that
sounds
good
they'll,
be
good
to
draw
from
that
expertise.
Yeah
I
mean
the
cell
lineage
in
C
elegans.
Of
course,
we
know
what
cells
are
part
of
each
cell.
You
know
they
have
their
own
identity
in
their
part
of
the
cell,
lineage
and
other
organisms.
Oftentimes
you'll
have
a
case
where
the
cells
will
sort
of
form
lineage
trees
based
on
their
starting
point.
In
other
words,
you
have
a
lot
of
cells
that
can
be
any
cell
in
the
lineage
tree
and
so
like
in
mouse.
A
For
example,
miles
embryos
they've
tried
to
construct
the
lineage
tree,
but
you
know
you
have
cells
that
can
be
like
a
certain
cell
on
the
lineage
tree,
and
you
know
it's
not
necessarily
traceable
to
anyone's
cell
in
a
position.
So
there's
this
sort
of
interchangeability
of
the
the
stem
cells
that
become
those
cells,
so
in
C
elegans.
Are
we
don't
have
that
problem
where
you
know?
A
Basically,
the
cells
will
divide
from
like
a
two
cell
State
you'll
have
the
a
b
and
the
P1
cell,
then
you'll
get
divisions
that
are
in
certain
parts
of
the
embryo,
and
it's
always
that
cell.
So
we
have
like
it's
almost.
It
is
almost
like
a
classification
problem
in
that
you
know
you
can
find
a
cell
in
a
certain
position
in
the
embryo,
and
you
kind
of
know
that
that's
that
cell,
you
can
also
Trace
from
like
a
two
cell,
State
and
Trace
out
where
they
should
be
because
they're
always
usually
in
the
same
place.
C
C
A
Be
different
yeah
they
can't
be
different
and
especially
awake
in
post
hatch
or
post-embryonic
development.
You
get
like
you,
even
you
even
get
like
this
sort
of
regulative
development
in
some
cells
where
it
produces
like
in
in
the
same
cells,
for
example
in
other
places,
but
that's
limited
and
but
mostly
the
the
bilateral
pairs
they
they
divide
and
they
come
out
on
like
the
right
and
the
left
side
and
you
get
like
basically
very
similar
they're
very
similar.
They
may
do
things
if.
B
B
C
A
C
D
A
D
A
So
yeah
I
can
send
change
some
references
on
that,
but
basically
that
nomenclature
is,
you
know,
part
of
development
where
you
have
these
two
cells
diverge
initially
a
b
and
P1,
and
then
a
b
will
divide
into
APA
and
AP
yeah
app
or
something
like
that.
Apl
and
APR
I.
Think
and
the
the
point
is:
is
that
there's
this
orientation?
It's
basically
a
nomenclature
that
gets
tacked
onto
a
b
as
the
cell
divides.
A
A
D
A
So
this
is
the
other
thing
I
want
to
talk
about.
Was
we
have
a
bunch
of
papers
on
curvatures
and
planes
and
embryos
curvatures
and
planes?
I
mean
this
geometry
of
the
cell,
so
we've
been
talking
about
like
curvatures,
and
we've
been
talking
about
like
some
of
the
roller
curvatures
play
and
morphogenesis
and
some
of
the
physics
of
the
cell
and
then
the
planes
are
like
these
planes
that
are
flat
surfaces.
A
A
A
Is
the
paper
Collective
curvature,
sensing
and
fluidity
in
three-dimensional
multicellular
system?
So
this
is
a
nature
physics
paper,
and
so
this
is
the.
This
is
what
I
was
reading
from
here.
The
abstract
so
requires
this
sort
of
curved
interface
or
substrate,
with
the
radii
of
curvature
comparable
to
many
cell
lengths.
So
this
is
where
you
know
it's.
You
know
multiple
cells,
migrating
they're
on
the
surface,
that's
curved
in
there
all
sort
of
on
the
surface
at
different
locations.
A
So
the
radius
is,
you
know,
pretty
large
compared
to
a
single
cell,
using
both
artificial
geometries
and
along
alveolar
spheres,
which
are
these
little
alveoli,
which
are
the
little
air
sacs
in
the
lungs
and
they
I
guess.
These
are
little,
maybe
organoids
that
are
based
on
these
little
parts
of
the
lung
derived
from
Human
induced
pluripotent
stem
cells.
So,
yes,
these
are
inducible
stem
cells,
which
are
these
cells
that
we
reprogram
from
say
like
some
Source
cell,
it
becomes
a
stem
cell
and
then
we
can
differentiate
it
in
different
ways.
A
Here
we
show
that
cell
sends
multicellular
scale
curvature,
that
it
plays
a
role
in
regulating
Collective
cell
migration
as
the
curvature
of
a
monolayer
increases.
So
as
we
increase
the
curvature
of
this
interface
cells
reduce
their
collectivity
and
the
multicellular
flow
field
becomes
more
dynamic.
A
So
this
is
where
you
know,
you're
changing
the
curvature
of
this
monolayer
and
it
affects
the
behavior
of
the
cells.
Furthermore,
hexagonally
shaped
cells
tend
to
aggregate
in
solid-like
clusters
surrounded
by
non-hexagonal
cells.
It
has
act
as
a
background
fluid.
So
these
are
you
know
where.
If
you
have
hexagonally
shaped
cells,
they
can
form
clusters.
You
can
get
other
cells
surrounding
them
that
can
act
as
a
background,
fluid
to
move
them
or
to
move
around
them.
A
We
propose
that
cells
naturally
form
hexagonally,
organize
clusters
to
minimize
free
energy
and
the
size
of
these
clusters
is
limited
by
a
bending
energy
penalty.
So
they
do
this
sort
of
energetic
analysis
of
migration
and
they,
you
know,
basically
assume
a
minimal
energy
for
these
hexagonally
hexagonal
clusters.
We
observe
that
cluster
sized
linearly
as
the
sphere
radius
increases.
A
A
A
So,
for
example,
epithelial
cells
growing
on
flat,
substrates
approach,
a
jamming
transition
during
maturation,
exhibiting
an
increasing
velocity
correlation
length,
decreasing
cell
speed
and
decreasing
cell
aspect
ratios.
So
that's
as
these
epithelial
cells
flow
on
a
two-dimensional
surface,
they
can
have
this
jamming
phase
transition
where
they
at
a
certain
density,
stop
flowing,
and
you
you
can
sort
of
measure
out
those
things,
in
contrast,
most
biological
structures
such
as
alveoli,
bronchi
and
intestines.
A
So
these
are
parts
of
the
lung
and
then
parts
of
the
intestine,
where
the
surface
is
extremely
curved,
are
naturally
curved
in
three
dimensions.
Even
for
inert
materials,
it
is
known
that
curvature
fundamentally
changes
basic
processes
such
as
crystallization,
yet
it
remains
unclear
to
what
extent
the
cellular
reflective
response
is
to
curvature
itself.
So
what
does
it
mean
that
the.
D
A
A
So,
let's
see
if
there
are
any
pictures
in
this,
so
this
is
a
curvature
here.
This
is
a
concave
curvature
or
convex
curvature,
and
then
this
is
the
radius
of
that
curvature.
So
you
can
see
that
you
have
this
this
cup
or
this
sort
of
Dome
that
comes
up
from
a
a
substrate.
Maybe
a
two-dimensional
substrate
that
you
know
the
surface
has
two
dimensional.
This
is
three-dimensional
because
it
has
this
curvature.
A
You
can
see
that
the
these
are
cells
along
this
curvature.
These
are
the
nuclei,
the
cells.
This
is
a
a
fluorescent
image,
so
you
see
the
green
nuclei
here
and
they're
on
this
curvature,
so
you
can
see
that
they're
sort
of
distributed
in
different
ways
around
the
curvature
it's
not
evenly
distributed.
You
see
these
little
clusters
and
these
are
the
ones
that
they're
referring
to
I
think
with
respect
to
the
hexagonal
versus
other
cells.
A
In
the
background,
these
are
mdck
cells,
there's
grown
on
different
curvatures
and
this
kind
of
shows
their
Collective
behaviors
in
the
Aggregates
they're,
not
really
analyzing
it
in
depth
here,
but
this
in
this
case
they're
doing
some
modeling
they're.
Looking
at
this
two-dimensional
surface
and
they're,
looking
at
the
spherical
surface
and
they're
actually
able
to
you,
know,
change
the
radio
radius
of
this
curvature
and
they're
able
to
see
some
Divergence
here
and
then
there's
a
distribution
of
divergences.
A
A
And
so
then
you
have
this.
You
know
additional
cases
here
where
you
can
show
the
curvature
decreasing
and
look
at
some
of
the
effects
of
that.
So
this
is
this
alveolosphere,
which
is
derived
from
Human
induced
stem
cells.
This
these
this
model
to
confirm.
So
this
is
the
I
think
the
model
here
that
they're
using
and
they've
stained
it
for
different
things
in
the
cell
and
they
show
this
extra
cellular
matrix
and
then
the
curvature
here
and
they're
showing
increases
and
decreases
in
curvature.
A
Then
they
look
at
the
2D
Young's
modulus
of
the
cell
pack,
which
factors
into
this
calculation
by
minimizing
the
Delta
G.
We
obtain
a
critical
pack
size
which
is
linearly
proportional
to
R.
So
they're
able
to
do
these,
you
know
kind
of
calculate
out
a
sort
of
a
scaling
almost
and
so
this
figure
they
have
curve.
Eternities
bending
energy
causes
higher
Dynamics
in
the
multicellular
flow
field.
A
What
those
look
like
so
you
see
in
the
alveolospheres
you
get
a
similar
result
to
the
simulation,
and
this
just
like
shows
different
conditions
and
the
allele
will
Spear
and
the
simulation.
So
this
is
a
case
where
there's
High
shear
in
this
part
of
the
cell
and
this
cluster,
and
you
see
that
there's
sources
and
sinks
so
when
they
what
they
mean
by
sources
and
sinks,
let's
see
if
they
talk
about
it
in
the
legend
I
think
they
really
talk
about
it
too
much.
A
But
basically
you
have
these
different
forces
acting
on
the
structure,
and
you
know,
depending
on
the
curvature
and
depending
on
the
different
clusters
that
are
in
here,
you
know
they
deal
with
them
differently.
So
yeah,
that's
that's
an
interesting
paper
because
they
really
kind
of
put
a
lot
of
that
curvature
they
put
into
the
test.
They
look
at
it
in
a
biological
system
and
in
a
simulation.
C
A
C
B
A
A
A
A
Well,
anyways
yeah,
well,
yeah.
It
would
be
interesting
to
see
exactly
what
the
so
that
yeah.
Maybe
this,
the
other
two
papers
might
give
us
a
little
bit
more
insight
into
what
you
know
what
the
role
of
curvature
is.
So
this
paper
is
curvature
induces
active
velocity
waves
in
rotating
spherical
tissues.
This
is
a
major
Communications
paper,
so
the
abstract
of
this
is
the
multicellular
organization
of
diverse
systems,
including
embryos.
Intestines
and
tumors
relies
on
coordinated
cell
migration.
A
While
such
Collective
modes
have
been
studied
extensively
in
flat
systems,
geometry
topological
constraints
on
Collective
migration
is
largely
unknown,
so
they
discover
Collective
motor
cell
migration.
In
this
paper,
rotating
spherical
tissues
manifesting
is
a
propagating
single
wavelength
velocity
wave.
So
this
is
a
wave
that
they're
proposing
in
that
in
that
spherical
tissue
in
that
Collective
cell
movement.
So
this
wave
is
accompanied
by
an
apparently
Inc
compressible
supular
cellular
flow
pattern,
featuring
top
logical
defects,
as
dictated
by
the
spherical
topology,
using
a
minimal
active
particle
model,
so
they're
using
this
a
modeling
approach.
A
So
again
you
know
there,
here's
here's
an
example
here
of
the
nuclei
in
this
sort
of
collective
and
there's
the
spherical
pattern.
You
have
the
extracellular
Matrix
out
here.
The
cells
are
migrating
around
this
axis,
so
they're
migrating
in
a
sort
of
a
spherical
pattern,
but
it's
organized
here.
These
are
examples
of
you
know:
Collective
migration
along
an
orientation
route,
so
you
know
they're
all
kind
of
collectively
moving
around
each
other,
but
they're
going
in
this
direction
or
this
direction
or
this
direction.
A
So
you
have
this
directional
migration
and
they
kind
of
explore
things
like
angular
speed,
rotational
order,
and
then
they
look
at
the
distribution
of
this
over
there
model
versus
their
experiments.
They
have
this
rotational
order
that
varies
across,
so
it's
not
like
a
deterministic
process,
so
this
is
a
stochastic
Global
rotation
of
a
spheroid.
So
this
is
a
spheroid.
A
Here
are
different
examples
of
it.
These
are
the
nuclei.
These
are
the
individual
movements
of
the
cells
and
the
spheroid.
Then
this
is
the
movement
of
the
spheroid
over
it
as
a
collective,
so
they're
able
to
do
this
in
a
simulation
in
an
experiment,
so
they
kind
of
show
this
distributions
of
angular
speed
and
rotational
order
all
distributions
across
time
and
different
spheroids.
So
these
are
distributions.
A
This
is
I,
think
angular,
speed
and
rotational
order.
So
you
can
see
they're
a
little
bit
different,
but
that's
how
they
characterize
that.
So
this
is
this
figure
here
is
these
are
Velocity
waves
and
rotating
spheroids.
So
now
you
have
these
velocity
waves
that
go
across.
This
is
the
sphere
here
with
the
different
the.
D
A
I
said
it's
axial,
so
you
have
these
poles,
they
have
the
equator,
and
then
these
are
the
cells
within
this
sphere.
Moving
around
you
have
I
guess
this
figure,
D
or
D
and
E
are
chymographs
of
the
azimuthal
component
of
velocity
fluctuations
around
the
equator
of
one
spheroid
I
guess
this
is
higher.
This
is
lower,
the
blue
is
lower,
the
red
is
higher,
so
you
have
this
over
time.
A
You
have
the
shift
in
angle,
angular,
velocity,
I,
guess
or
angle,
and
this
variable
so
the
red
shifts
over
in
this
direction.
So
it
just
shows
that
there's
this
change
in
this
wave
that
that
goes
on
this
change,
I
guess
in
rotational
momentum
or
something
throughout
this
spheroid.
So
you
can
see
this
pattern
in
the
in
the
heat
map
here,
as
opposed
to
e,
where
you
don't
see
this,
it's
just
kind
of
constant
across
time.
A
And
then,
in
this
figure
you
have
supercellular
flow
patterns
of
the
spheroid
surface,
so
these
show
the
flow
pattern
as
sort
of
broken
down
as
the
way
that
which
they're
moving.
So
you
have
these
a
different
Arrow
different
colored
arrows.
You
have
Vortex
defects
and
Saddle
Point
deflects
and
in
these
in
the
experimental
data
and
model,
so
that
in
this
way
they
show
the
the
flows,
the
flow
fields,
and
then
these
different
defects
saddle
point
of
vortex.
A
So,
let's
see
so
a
is
the
snapshot
of
the
tangential
velocity
fluctuation
field
and
the
surface
layer
of
an
individual
spheroid.
So
that's
a
this
is
where
you
have
this.
This
flow
field,
the
flux
analysis
around
the
saddle
point
ahead
of
the
Velocity
wave
maximum.
You
have
to
go
to
the
method
section,
to
get
away
precise
definitions
for
a
lot
of
these
things.
Bar
plot
shows
the
absolute
value
of
the
average
influx,
we'll
see
along
the
Equator
and
out
flux,
mostly
towards
the
poles.
A
So
things
come
in
at
the
equator
and
move
out
towards
at
the
poles.
So
you
see
that
up
here
you
have.
This
is
the
bar
graph
they're,
referring
to
you,
have
this
influx
and
out
flux,
and
then
this
is
what
it
looks
like
in
the
sphere.
So
you
also
have
a
back
side
and
front
side
of
the
average
velocity
fluctuation
field.
So
that's
c
and
d
That's
up
here,
so
you
can
see
the
influx
and
out
flux,
and
then
this
is
F.
A
So
then,
this
shows
more
of
the
same.
This
kind
of
shows
velocity
waves
in
a
model
of
active
part
for
active
particles
on
a
sphere,
so
these
are
going
to
show
these
waves
and
evidence
of
the
waves.
This
shows
a
noise
amplitude
versus
alignment
strength.
This
shows
that
Collective
rotations
are
one
part
of
this
phase
diagram.
You
have
a
region,
that's
a
region
of
disorder
or
region
of
quiescence,
not
the
collective
rotations.
It's
usually
at
this
negative
alignment
strength
there,
I
guess
for
values,
10
to
the
negative
2
10
to
the
negative
1.
A
A
The
point
is:
is
that
there's
this
difference
between
Collective
rotations
disorder
and
quiescence,
which
means
that
the
cells
are
not
moving?
And
so
it's
it's
sort
of
this
order
at
the
edge
of
chaos,
idea
shown
that
and
yeah.
So
this
is
just
showing
more
of
the
same
I.
Think
and
then
the
discussion
they
just
kind
of
say
that
they've
discovered
this
Collective
mode
of
migration.
A
Wave
like
instabilities
are
a
key
feature
of
collective
Behavior.
The
wavelength
phenomena,
multicellular
systems
include
mechanical
mechanochemical
waves
and
spreading
up
with
the
oil
monolayers.
More
broadly,
sound
waves
have
been
theoretically
predicted
and
experimentally,
observed
and
active
matter
flux
and
flat
surfaces,
so
in
active
matter
which
doesn't
need
to
be
biological.
It
can
be
just
like
particles,
and
things
like
that.
You
get
these
kind
of
flocks
on
flat
surfaces
on
curved
surfaces
you
get.
Maybe
this
flocking
Behavior
with
a
much
more
Dynamic
behave
set
of
behaviors.
A
Hydrodynamic
theories
predict
such
sound
waves
to
also
manifest
on
spherical
surfaces
where
hydrodynamic
instability
causes
velocity
waves
to
propagate
in
Phase,
with
density
waves
along
the
Equator
of
the
rotating
flock.
So
this
is
something
they
find
more
generally
in.
You
know
in
spherical
systems
that
have
hydrodynamics,
and
so
you'll
see
this
sort
of
these
instabilities
and
these
waves
operating
there
as
well,
and
so
finally,
as
an
alternative,
sound
waves,
the
active
velocity
wave
may
be
related
to
kinematic
waves,
which
are
waves
of
motion.
A
A
In
a
cylinder,
you
have
the
spherical,
this
closed
sphere,
the
truncated
sphere
of
the
cylinder-
and
these
are
the
signatures
over
time,
and
you
can
see
that
the
sphere
and
the
truncated
sphere
show
you
know
more
sort
of
dynamic
Behavior
than
the
cylinder
where
it's
just
in
this
one
orientation
and
they're
able
to
migrate
around
freely,
but
not
on
the
spherical
surface,
which
it
provides.
You
know
this
sort
of
curvature.
A
C
C
And
that
might
be
related.
Another
one
are
is
Convergence
extension
movements
during
role
play
formation,
embryos,
I,
don't
know
if
anyone's
looked
at
the
curvature
effects
of
that
and
there
might
be
some
yeah
and
the
third
one
which
I
bring
up
is
the
flocks
on
the
flux
have
a
very
interesting
feature.
If
you
watch
movies
of
murders
of
birds,
okay,
yeah,
they
have
a
sharp
edge
too.
A
A
A
So
the
last
paper
is
redundant
mechanisms
and
division
plane
positioning.
So
this
one
isn't
like
the
other
twos
kind
of
talks
about
this
sort
of
plane
positioning
and
this
isn't
a
plant
cell
division.
So
this
is
a
little
bit
different
systems
set
of
systems
here,
so
redundancy
and
plant
cell
division
contributes
to
the
maintenance
of
proper
division,
plane
orientation.
A
Every
highlight
three
types
of
redundancy,
so
there's
temporal
redundancy
or
a
correction
of
earlier
defects.
It
results
in
proper
final
positioning,
there's
genetic
redundancy,
which
is
the
functional
compensation
by
homologous
genes
and
then
synthetic
redundancy
or
redundancy
within
or
between
Pathways
that
contribute
to
proper
division,
plane,
orientation
and
so
understanding
the
types
of
redundant
mechanisms
involved,
provides
insight
into
current
models
of
division,
plane
orientation
and
they
don't
talk
about
spheres
on
this,
but
they
talk
about
the
different
plane
orientations.
A
A
So
we
talked
about
c
elegans
c
elegans
develops
along
this
anterior
posterior
plane
the
anterior
end
being
like
the
a
b
lineage,
the
posterior
and
being
is
P1
lineage
and
then
there's
differentiation
along
that
those
different
lineages,
but
they
all
proceed
along
a
plane
which
is
the
AP
plane
and
then
there's
a
second
plane
that
emerges,
which
is
the
Left
Right
plane
where
you
get
this
bilateral
symmetry
of
cells.
So
you
get
this
ability
for
symmetrical
cell
divisions.
You
get
you
know
along
the
Left
Right
plane.
A
You
get
non-symmetrical
cell
divisions
in
different
ways
too,
but
you
know
largely
you
want
to
preserve
that
plane
orientation,
but
even
the
anterior
posterior
plane.
You
have
different
things
that
emerge
so,
for
example,
in
C
elegans,
you
always
get.
You
know
the
reproductive
structures
towards
the
back
end,
the
inectome
cells
that
form
the
main
central
nervous
system
towards
the
front
end,
and
you
don't
have
just
it's
not
randomly
placed.
A
So
this
is
the
importance
of
these
planes
that
they
provide
this
sort
of
template
for
things
that
they
end
up
in
the
generally
the
right
place,
so
plant
cells
do
not
migrate
and
instead
control
the
location
of
new
cells
by
positioning
the
division
planes.
So
in
Plants
it's
different,
you
have
this
plane
orientation,
which
is
uniquely,
you
know
unique
in
Plants,
but
you
see
this
in
all
organisms
to
some
extent.
A
A
C
A
C
A
A
Statement,
that's
fine
yeah
yeah,
so
they
kind
of
go
through
some
of
these
aspects
here,
actually
the
yeah.
They
talk
a
little
bit
about
the
spindle
spindle
captures
the
second
chromosomes
during
metaphase
and
anaphase.
Spindle
formation
is
reviewed
in
these
places.
After
chromosome
special
separation
and
anaphase,
the
spindle
disassembles
The
Forum
plant-specific
structure.
A
This
specific
structure
also
directs
the
formation
of
the
cell
plate.
So
there
are
all
these
different
parts
of
cell
division
that
contribute
to
this.
The
formation
of
this
plane
and
the
maintenance
of
the
plane-
and
so
you
know,
you'll
see,
for
example,
in
mutants
you'll,
see
differences
but
solid,
solid
elongation
prior
to
division,
typically
favors,
a
division,
bisecting
long
axis
of
the
cell,
the
relationship
between
division,
plane,
orientation
and
cell
shape
is
discussed
in
these
references
identifying
mutants
that
only
alter
PPP,
ppb
formation
or
positioning
some.
A
What
you
discussed
below,
but
do
not
seriously
alter
interphase,
microtubule
orientation,
Clarity,
cues
or
cell
shape,
provide
exceptionally
valuable
insight.
So
this
what
this
means
is
that
there
are
a
bunch
of
things
in
cell
division,
a
bunch
of
aspects
of
that
that
are,
you
know,
sort
of
set
up
this
plane.
You
have
different
microtubule
arrays
that
get
set
up
in
response,
and
this
sets
up.
You
know
this
sort
of
plane
of
these
planes
that
we're
talking
about
in
mutants,
which
are
organisms
that
have
defined
mutations
where.
D
A
A
And
so
this
is
an
example.
Here
you
have.
This
is
the
midplane,
the
interphase,
the
prophase,
the
metaphase
anaphase
the
telophase.
So
you
see
that
the
microtubules
are
stretching
out
and
then
you
get
this
replication
of
genetic
material.
You
get
then
the
two
cells
that
form
and
then
cytokinesis
and
then
this
is
the
cortex
which
is
part
of
the
microtubule
structure
where
it
stretches
out
during
these
divisions.
So
you
see
this
orientation
reinforced.
Okay,
Bradley.
Could
you
go
back
to
the
picture?
Yeah.
C
A
Okay,
so
this
is
this
is
a
map
of
temporal
redundancy,
so
you
see
some
redundancy
with
respect
to
time
and
they
kind
of
show
examples
of
correctly
oriented
cell
divisions
and
when
mitotic
structures
deviate
from
the
correctly
oriented
position.
So
you
get
these
mechanisms
that
kind
of
reinforce
the
normal,
the
normalcy
of
it.
You
get
these
opportunities
or
instances
of
incorrect
orientations,
but
they
can
be
corrected
by
this
redundancy
to
make
sure
that
they
fit
into
this
correct
orientation.
A
And
then
this
is
protein
recruitment.
So
you
get
these
aspects
of
protein
localization
that
gets
reinforced
by
this
redundancy
later
recruit,
and
then
you
get
later
recruitment
to
the
same
location.
It
has
a
different
effect,
so
yeah.
So
this
is
different
from
the
curvature
aspect,
but
this
is
how
we
form
these
planes,
which
you
know,
if
you
think
about
the
differences
between
planes
and
curvatures.
C
A
C
A
A
Yeah,
so
that
actually,
you
know
goes
back
to
the
other
papers
where
we
were
talking
about
these
hexagonal
Cells
versus
the
other
cells
and
they
have
different
behaviors,
so
yeah
I
think
that's
it
for
this
paper.
I
know,
if
there's
any
more
to
say
about
it.
It's
just
that.
You
know
this
is
a
nice
contrast
between
these
two
things
and
I.
Think
they're
both
important
for
some
of
the
topics
we've
been
talking
about
about
physics
and
the
physics
of
differentiation,
and
so
a
few
more
thoughts
and
comments
on
the
curvature
papers.
A
A
So
you
might
have
like
a
hemisphere
sticking
out
of
a
a
surface
here,
a
two-dimensional
surface
or
sort
of
the
inverse
situation
where
you
have
something
that
goes
down
into
that
surface.
So
that
was
the
hemisphere
and
the
idea
being
that
the
hemisphere
won't
have
a
constant
curvature,
because
at
some
point
it
will
sort
of
stop
at
the
two-dimensional
surface.
A
Sometimes
if
you
have
something
like
a
tumor
or
like
an
alveoli,
you
know
they're
kind
of
embedded
in
strata
of
extracellular,
Matrix
or
other
tissues,
and
so
there
they
form
that
sphere,
but
they're
impacted
by
its
surroundings.
The
other
thing
is,
is
that
sometimes
so
balls
or
things
that
are
packed
into
balls,
don't
have
a
constant
curvature.
A
A
You
can
break
it
off
into
pieces
like
this,
maybe
one
or
two
cells
in
each
of
these
divisions
and
that's
what
it
looks
like
so
there's
no
constant
curvature.
If
you
go
to
the
surface
of
this,
it's
actually
quite
rougher
of
a
chair,
and
so
we
can't
assume
a
constant
parameter
for
something
like
this.
A
The
second
being
is
left
right,
Axis
and
sometimes
there's
an
anatomical
imperative
for
a
dorsal
ventral
axis
and
so
a
lot
of
times
some
of
the
things
we've
done
with
embryos,
embryo
networks,
lineage
networks,
things
like
that.
We
often
have
these
three
dimensions.
So
in
our
some
of
our
literature,
some
of
our
papers,
we
have
like
a
five
five
Tuple
or
a
five-dimensional
equation,
which
is
basically
x
y
z,
which
is
the
XYZ
coordinates
time
and
this
factor
which
can
be
angle.
C
D
A
Have
different
different
aspects
of
morphogenesis
different
aspects
of
the
phenotype
and
so
forth,
but
these
three
are
related
to
spatial
location.
This
is
time
this
is
usually
angle
or
some
sort
of
usually
division
angle.
So
when
two,
when
a
cell
divides,
it
divides
at
a
certain
angle,
so
we
can
characterize
that
we
can
also
characterize
angle
in
terms
of
these
three
dimensions,
so
these
three
dimensions
form
a
volume
instead
of
just
like
three
independent
dimensions,
and
we
can
give
more
information
about
it
here.
A
So
this
is
the
way
we
kind
of
think
about
it
in
our
group-
and
this
relates
to
these
three
different
planes
that
you
see
here
and
then
alternate
relates
to
the
amount
of
curvature
that
you'll
find
three-dimensional
volume
we
can
also.
You
know
we
can
characterize
things
like
roughness
in
fractal
analysis
oftentimes.
They
have
a
parameter
for
roughness,
it's
a
fractal
scaling
factor,
and
that
will
tell
you
kind
of
this.
How
rough
the
surfaces,
if
you've
ever
heard
of
this
anecdote
about
measuring
the
length
of
a
coastline.
A
You
know
at
one
scale
the
coastline
appears
smooth,
but
when
you
get
to
smaller
and
smaller
scales,
it
appears
more
rough
and
you'll
see
this
with
the
spherical
packings
as
well.
If
you're
interested
in
sort
of
the
organismal
level,
you
might
consider
it
to
be
a
smooth
sphere,
a
smooth
spherical
packing
of
the
smooth
surface.
If
you're,
considering
this
in
terms
of
cell
single
cells,
you'll
find
that
there's
a
rough
surface,
so
it
depends
on
the
spatial
scale
that
you're
operating
at
and
the
way
you
might
want
to
model
this.
A
B
B
C
A
C
A
Inquired
about
some
of
the
resources
we
have
for
lineage
trees
and
I'll
be
posting
this
in
the
slack
independently
of
this.
But
I
wanted
to
go
over
these
in
the
meeting
just
to
give
us
some
baseline
for
how
we
deal
with
C
elegans
lineage
trees
and
determining
CL
against
lineages.
So,
as
I
said
in
the
meeting
see
all
again's
lineage
trees
are
what
they
call
the
germanistic.
A
Now
there
are
exceptions
with
respect
to
mute,
defined
mutants,
but
otherwise
the
scheme
always
is
replicated
in
every
CL
against.
So
we
can
use
this
when
analyzing
microscopy
images,
we
can
identify
cells,
there's
join
a
certain
spatial
location
and
they
also
often
have
later
in
development
attributes
of
early
function.
So,
for
example,
you'll
have
cells
that
will
become
connectome
cells
or
you'll
have
cells
that
will
become
germ
cells
and
they'll,
be
located
in
a
certain
place
in
a
certain
sub
lineage,
and
then
they
will
eventually
have
a
specific
function.
A
So
the
First
Resource
I
want
to
show
is
wormweb.
So
this
is
something
that
was
created
by
nikelbotland.
This
is
a
resource.
That's
been
around
for
quite
a
while
at
least
10
years,
probably
about
10
years.
Maybe
so.
This
is
an
example
of
a
CL
against
lineage
tree
P0
was
the
fertilized
gamete
and
then
a
b
and
P1
are
the
two
cells
that
divide
from
that.
So
this,
the
Mother
cell
is
P0
a
b.
Is
this
anterior
cell
P1
is
the
posterior
so
and
then,
from
a
b
you
have
ABA
and
ABP.
A
A
Now
these
cells
migrate
around
a
little
bit,
so
they
don't
actually
end
up
in
the
places
that
they
sort
of
originate
in
the
embryo.
So
sometimes
you'll
have
a
b
cells
that
contribute
to
muscle
that
end
up
going
to
the
posterior
end
of
the
organism,
but
start
out
in
this
anterior
pool
of
the
embryo.
So.
A
A
Then
we
have
for
ABP,
which
is
a
b
posterior.
We
have
abpl,
which
is
posterior
left
and
then
abpr,
which
is
posterior
right
so
at
that
second
division,
they're
dividing
left
to
right.
So
you
can
see
this
forms
a
nested
tree.
You
have
nesting,
I
can
open
up.
Abal
and
I
can
see
how
this
proceeds.
You
have
another
anterior
division
after
that
you
have
another
anterior
posterior
division.
After
that
you
have
a
third
anterior,
posterior,
Division,
and
so
on.
A
So
they
just
keep
expanding
in
that
way
and
their
initial
position
is
the
anterior
posterior,
but,
as
I
said,
these
things
can
migrate
after
they
divide.
The
point,
though,
here
is
that
they
divide
you
know,
so
you
can
trace
it
from
a
single
cell
down
a
number
of
divisions
and
you
can
roughly
locate
it.
It
doesn't
give
you
the
exact
positional
coordinates,
but
its
origin
is
in
that
part
of
the
embryo.
A
You
know
100
minutes,
200,
minutes,
300
minutes
all
of
development
within
the
egg
happens
in
about
600
minutes.
So
it's
actually
a
pretty
quick
process
relative
to
say,
like
a
million
embryos
or
some
other
type
of
embryo
that
we
might
be
more
used
to
so
this
is
where
we
get.
So
these
are
what
they
call
developmental
cells
and
they're,
not
stem
cells
they're.
A
They
have
a
deterministic
fate,
but
they're
still
developmental
cells
in
that
they're
not
differentiated
into
their
final
form,
so
they
they're
able
to
divide
and
produce
daughter
cells
and
then
at
some
point
you
get
the
differentiated
cells.
So
ainl
is
a
type
of
cell
here
you
can
see
at
the
selfie
map.
Here
we
have
it's
a
neuron
or
glia-like
cell
ainl,
it's
part
of
the
connectome
and
it
differentiates
at
about
350
minutes.
A
A
A
A
And
then
there's
some
differentiation
once
it
becomes
a
neuron
in
terms
of
like
the
things
that
are
going
on
inside,
like
the
different
types
of
proteins,
it's
it's
producing
and
so
forth,
but
it's
largely
this
type
of
cell.
It's
not
going
to
change
its
feet
now.
This
is
all
a
b
cells,
a
b
cells
form
different
things
like
different
types
of
tissues
like
cuticle
and
and
muscle
and
neuron
neuronal
cells
and
other.
D
A
Of
cells,
there
is
the
other
lineage,
so
A
B
is
the
lineage
that
produces
a
lot
of
the
somatic
cells.
A
lot
of
the
different
organs
T1
is
more
specialized.
P1
is
a
lineage
that
contributes
directly
to
the
germline.
So
you
see
that
the
germline
is,
of
course,
where
the
gametes
are
produced
and
C
elegans
is
most
most
of
the
time
a
hermaphrodite.
A
A
Within
this
P1
lineage,
though
you
have
other
specialized
lineages,
you
have
EMS,
which
I
think
it
contributes
to
the
gut,
and
you
know
Ms
and
C
and
D.
These
are
all
very
specialized
for
different
parts
of
the
organism,
different
tissues,
and
so
this
is
another.
The
second
lineage.
This
is
the
posterior
lineage.
So
this
is
all
all
these
divisions
happen,
posteriorly
and
for
Ms,
for
C
and
for
d,
which
kind
of
derived
from
P1.
Ultimately,
they
have
the
same
nomenclature
situation
as
a
b,
so
you
have
Al,
which
is
from
E.
A
You
have
an
anterior
Division
and
a
leftward
division
and
then
an
anterior
and
rightward
division.
So
that's
how
that
works
and
it's
the
same
nomenclature
as
a
b
and
eventually
these
cells
will
also
lead
down
to
differentiated
cells,
so
e
is
divides
until
you
get
to
intestinal
cells,
so
these
are
intestinal
cells
that
now,
in
this
case,
you
have
intestinal
2D,
which
is
terminal.
That
means
it
differentiates
into
a
into
the
cell,
and
then
it
stops
dividing
in
this
case,
intestinal
5r
actually
divides
into
5ra
and
5rp.
A
D
A
They
note
sometimes
you
get
a
precursor
of
some
type
and
then
you
get
the
final
cell,
and
so
this
is
again
you
see
this
again
with
intestinal
cells
and
some
of
these
other
specialized
cells
in
this
region.
So
that's
our
our
lineage
tree.
That's
a
map.
It's
an
interactive
map,
it's
at
wormweb.org
cell
lineage,
so
you
can
look
up
any
cell
and
get
information
about
it,
information
about
when
it
divides
when
it.
You
know
what
its
lineage
name
is
and
then
there's
a
Google
Scholar
length
for
things.
That
cite
this.
A
So
you
know
people
have
worked
on
the
lineage
tree.
They've
identified
specific
cells.
There's,
like
you,
know,
a
biology
literature
on
this
people
really
obsessed
with
drilling
down
to
very
tiny
details
in
the
biology
literature.
So
you
may
want
to
check
that
out.
So
there
are
another
a
couple.
Other
resources
I
wanted
to
go
over
the
first
one.
Is
this
worm
Atlas?
So
the
thing
about
the
great
thing
about
the
c
elegans
community
is
that
it
has
a
lot
of
common
Community
Resources.
The
first
of
these
is
worm
Atlas.
A
Well,
the
first
one
was
the
the
worm
this
one
here,
the
wormweb.org,
but
the
second
is
worm
Atlas,
and
this
is
a
database
that
gives
us
information
about
anatomy
of
C
elegans,
but
also
weather
nematodes.
This
article
is
on
neuronal,
Cell
lineage
is
in
the
hematode
and
so
in
the
nematode
C
elegans,
and
so
this
was
by
John
solston.
Now
John
solston
was
scientists
who
won
the
Nobel
Prize
for
discovering
this
lineage
tree,
and
he
was
the
one
who
actually
worked
it
out
by
hand.
A
So
he
sat
by
a
microscope
back
in
the
70s
he
sat
by
a
microscope.
He
drew
all
these
images
of
C
elegans.
He
worked
out
the
lineage
tree
and
then
he
drew
out
the
lineage
tree.
So
all
that
nomenclature,
some
John
Solstice
work,
and
he
did
this
all
by
hand.
This
is
a
very.
This
is
a
very
important
set
of
things
to
to
get
some
background
on.
A
So
this
basically
talks
about
that.
One
of
the
attractive
properties
of
C
elegans
is
that
we
can
trace
this
someoneage
tree
and
we
can
understand
how
cells
sort
of
form
and
differentiate
and
development
and
C
elegans
is
a
very
good
model
system
for
this.
So
it
kind
of
talks
about
the
nervous
system
in
particular.
A
So
there
are
other
things
in
between
they're
motor
neurons
and
the
ventral
nerve
cord,
which
goes
up
and
down
the
organism,
and
that
also
contributes
to
sending
signals
to
the
muscle
for
the
worm
to
move.
Now,
all
these
cells
have
to
divide
from
somewhere,
and
so
they
Divide
from
these
developmental
cells-
and
we
have
a
this-
is
a
breakdown
of
the
cell
lineage.
So,
like
I
said
a
b
features,
mostly
the
hypodermal
cells,
the
neurons,
the
muscle
Ms,
has
a
lot
of
things
with
Ms
does
have
some
neurons,
some
choliomyocytes
muscles
and
glands
eat.
A
The
subline
energy
which
we
saw
is
intestinal,
C
has
hypodermis
and
neurons
muscle,
D
has
muscle,
and
then
p
p
is
the
germline.
So
you
can
see
that
muscle
pops
up
in
a
number
of
sublineages
and
it's
just
because
it's
all
over
the
worm,
it's
a
predominant
thing
and
it
connects
to
the
the
connective
nervous
system,
connectome,
and
so
you
know
we're
interested
in
looking
at
how
that
nervous
system
originates,
how
it
hooks
up
to
the
muscle
how
all
those
things
form.
So
this
is
the
salston
tree.
A
It
kind
of
shows
how
we
map
this
out.
You
get
these
repeated
sub
lineages
you
get
these
post-embryonic
lineages,
which
we
haven't
talked
about
yet,
but
are
cells
that
will
divide
after
the
worm
is
hatched
and
is
actually
crawling
around
and
it's
in
its
larval,
State
and
so
you'll
get
different
types
of
cells
that
are
born
then,
and
actually
those
kind
of
cells
you
can
trace
out
the
salmon
age.
A
You
get
a
you
know,
you
get
a
full,
fully
functional
Worm,
but
you
do
get
some
post
embryonic
development
yeah.
It's
not
gonna
sell
some
interactions.
They
talk
about
these
different,
so
many
images
which
can
be
analogous
and
yeah.
So
this
is
worth
going
over
if
you're
at
all
interested.
In
how
you
know
this
was
sort
of
developed
this
lineage
Tree
in
how
these
cells
function.
So
we
have
the
atlas
from
wormweb.
We
have
this
article
from
where
I'm
Atlas.
This
is
information
about
John
solston.
A
This
is
the
Nobel
lecture
that
he
gave
so
in
2002.
He
won
the
Nobel
Prize.
He
talks
about.
You
know
the
model
organism
CL
again
so
C
elegans
is
a
model.
Organism
originated
with
the
work
of
Sydney
Brenner,
that
was
in
the
1960s,
and
so
they
were
looking
for
a
nice
model
organism
for
medical
research.
Is
it
worth
some
sort
of
Workhorse?
A
A
You
know
we
don't
want
a
lot
of
have
a
lot
of
complexity,
but
at
the
same
time
we
want
to
have
those
organs
that
are
distinct
and
so
C
elegans
became
the
main
candidate
for
that
so
Sydney
Brenner
worked
out
some
of
the
first.
You
know
you
have
to
culture
these.
You
can
find
C
elegans
in
the
soils
about
anywhere
on
Earth,
but
you
have
to
culture
them
in
a
laboratory
and
that's
a
whole
different
set
of
challenges.
So
Sydney
Brenner
worked
out
how
to
culture
these
things
in
the
lab.
A
Thing
you
can
do
with
them,
is
you
can
induce
mutations
and
have
what
they
call
the
fine
mutants
and
it's
rather
easy
to
make
to
find
mutants
in
C
elegans
as
opposed
to
another
species,
but
defined
moons
are
basically
mutations
that
are
stable
across
Generations.
So
you
can
always
you
know
later
on.
When
we
had
genomic
data,
we
could
localize
these
mutations
to
specific
points
in
the
genome
to
specific
genes
and
then
have
a
library
of
these.
So
we
can
actually,
like
you
know,
do
experiments
on
specific
mutations
and
see
what
the
effect
is.
A
D
A
Sequencing
genes
and
getting
you
know,
information
about
some
of
the
you
know
some
of
the
structure
of
the
genome,
so
we
have
a
pretty
at
this
point.
We
have
a
very
good
handle
on
C
elegans
genomics,
so
you
can
easily
map
out
some
of
these
things.
Some
of
these
mutations
to
the
genome.
You
can
also
understand
you
know
which
genes
are
controlling,
which
behaviors
and
see
elegans,
it's
actually
easy
to
do
and
other
organisms
it's
less
easy
to
do,
especially
with
respect
to
movement
behaviors.
A
A
Now
he
talks
about,
then
how
he
got
into
development
and
some
of
the
reasons
for
that
and
how
he
worked
out.
So
he
was
drawing
these
neuroblast
divisions
and
you
know
he's
this
is
the
level
of
what
he
was
doing.
He
was
drawing
things
out
by
hand,
and
so
you
know
this
is
all
very
hard
work
and,
if
you're
interested
in
this
history,
but
also
some
context,
this
is
a
good
thing
to
read.
This
is
from
nobelprize.org.
A
This
article
from
I
think
it's
the
journal,
genetics
and
they
talk
about
a
whole
horvitz
and
soulston
and
C
elegans
sell
lineage
mutants.
So
this
is
horvitz
and
soulston
and
they're
talking
about
horvitz
is
another
scientist.
Robert
rivets
who's
working
on
c
elegans
as
well,
and
they
talk
about
this
idea
of
someone
age
means.
A
So
you
have
these
cell
lineages
and
one
of
the
things
you
can
do
in
so
many
ages
is
you
can
knock
out
cells
during
development,
so
say:
if
you
knocked
out
this
abala
cell,
you
would
not
have
a
replacement
for
it,
so
everything
will
lower
it
in
the
cell
and
Industry
would
not
exist.
So
if
you
knocked
out
abao
and
development
you're,
just
the
bladed,
the
cell
blew
it
up.
A
You
would
not
get
a
bunch
of
neurons
and
the
consequence
of
that
would
be,
of
course,
that
you
wouldn't
have
those
neurons
and
you'd
probably
have
non-functioning
nervous
system.
Other
cells.
You
could
knock
out
and
they're
generally,
they
have
different
effects.
So
that's
a
lot
like
mutations.
You
mutate
something.
Sometimes
you
get
behavioral
deficits,
sometimes
they're
missing
cells
in
development.
You
can
also
do
this
with
getting
rid
of
the
cells
and
development,
and
so
the
point
is
is
that
you
can
really
see
what
cell
what
what
genes
are
necessary.
A
What
cells
are
necessary
and
you
can
really
do
some
interesting
studies,
so
this
is
what
they
were
doing
with
so
lineage
mutants.
It
also
shows
you
know
the
level
of
replacement
in
the
cell
lineage.
So
in
a
lot
of
the
mammalian
cell
in
ages,
the
cells,
the
stem
cells,
proliferate
and
then
are
interchangeable,
so
you
can
put
together
a
cell
lineage
tree
from
stem
cells
that
differentiate
into
different
types
of
cells.
Given
they
get
these
chemical
signals
and
they
differentiate
and
C
elegans.
It's
not
like
that.
A
A
The
combination
of
molecular
and
genetic
analysis
of
the
cell
lineage
contributed
to
our
current
understanding
of
the
mechanisms
controlling
induction
and
development.
With
of
the
vulva.
The
core
Machinery
or
program
cell
death,
which
is
epitosis,
which
we
talk
about
all
the
time
in
this
group,
the
development
of
the
muscles
and
nerves,
that
control
egg,
laying
and
developmental
timing,
which
is
a
part
of
genetics
and
the
consequent
discovery
of
what
we
call
Micro
rnas.
So
this
is
C.
A
A
This
is
an
article
by
soulston.
This
is
okay.
This
is
the
neuronal
cell.
Lineage
is
one,
and
then
we
have
this
paper
on
Cell
identification
at
someone
and
so
lineage
analysis.
This
is
by
enter
chisel
and
Claudia
messica.
This
is,
you
know,
it's
a
longer
article
on
sort
of
the
idea
of
identifying
cells
in
a
solid
Edge.
So
this
goes
through
sort
of
you
know
the
rationale:
you're,
looking
at
cell
division
patterns,
you're
looking
at
gene
expression
patterns,
and
how
do
you
identify
these
cells?
A
A
Although
the
germ
cells
don't
differentiate,
so
you
know
we
can
use
something
called
a
green
fluorescent
protein
and
if
you've
seen
these
fluorescence
images,
it's
like
a
black
background
with,
like
some
green
or
white
color,
that
that
shines,
and
this
is
what
we're,
or
sometimes
it's
red
or
even
blue.
This
is
what
we
call
fluorescent
proteins
and
they
they
use
them
to
express
them
in
cells,
and
they
may
tag
like
a
protein
of
Interest
or
an
mRNA
of
interest
and
they'll
co-express,
so
that
you
can
identify
these
things
in
an
image.
A
So
we
can
get
images
that
have
this
different,
these
different
gfp
markers
and
they
can
tell
us
about
you-
know
which
cells,
which
we
can
also
do
this
through.
You
know
positional
information,
but
that's
a
little
bit
less
precise,
and
so
you
know
these
cell
identification
markers
give
us
a
little
bit
more
Precision
in
identifying
these
different
cells,
especially
if
you're
looking
at
questions
of
like
you
know
what
is
a
cell
expressing
and
is
it
consistent
over
time?
And
you
know
maybe
what
are
precursor
cells
expressing
versus
differentiated
cells?
Now.
A
We
don't
necessarily
care
about
that,
but
this
is
something
that
can
be
useful
for
other
studies
and
so,
if
we're
creating
a
tool
say
to
under
to
analyze
cells,
that
might
be
something
we
might
build
into
it.
If
you're
answering
specific
biological
questions,
this
is
also
very
important
to
have
in
terms
of
a
capability.
A
D
A
Finally,
this
article
on
lineage
resolved
molecular
atlas
of
c
elegans
are
for
your
Genesis
at
single
cell
resolution,
so
this
is
a
molecular
Atlas
And.
This
is
actually
related
to
some
of
the
work.
That's
been
done
on
the
Epic
data
set,
so
this
is
actually
one
of
the
same.
The
same
group
or
some
of
the
same
people
involved
in
producing
the
Epic
data
set
Robert
Waterson
in
John
Murray
were
involved
in
that
data
set
and.
A
Is
the
same
group
and
with
the
Epic
data
set,
they
produced
information
about
the
different.
You
know
they
were
able
to
produce
these
movies
of
the
entire
CL
against
embryo.
That's
dividing
and
goes
the
divisions
go
up
to
the
hatch
of
the
egg,
and
one
of
the
things
I
mentioned
is
that
we
want
to
look
at
molecular
signatures
to
identify
these
cells
and
identify
when
they're
going
to
differentiate
into
differentiated
cells,
and
so
what
they're
doing
here
is
they've
built.
A
lineage
resolved
molecular
Atlas,
so
they've
built
an
atlas
that
is
resolved.
D
A
Cells
are
resolved
in
terms
of
their
position
in
the
lineage
tree
and
this
single
cell
resolution,
so
this
has
86
124
or
86
024
single
cells,
the
transcriptomes
of
these
cells,
so
C
elegans,
only
you
know,
doesn't
have
that
many
cells
by
by
far
it
has
959
cells
in
the
hermaphrodite.
But
we
use
like
a
number
of
cells
in
different
C
elegans.
We
want
to
take
a
number
of
specimens
and
average
it
so
that
we
have.
You
know
some
idea
of
you
know
the
variation.
We
don't
want
to
just
assume
that
things
are.
A
You
know
deterministic
when
maybe
they're,
not
maybe
it's
just
that
one
worm
that
you're
studying
so
they've
taken
a
large
number
of
transcriptomes
from
a
large
number
of
cells
and
then
they're
classifying
these
cells
developmentally
into
these
different
categories,
and
we
only
have
959
in
the
organism
and
I.
Think
the
great
thing
about
Clans
is
always
the
same
number
of
cells
per.
You
know
unless
you
have
a
defined
mute,
which
you
can
have
maybe
a
few
a
few
fewer
cells
and
and
males.
A
Of
course,
there
are
1024
cells,
but
the
development
within
the
egg
development
always
has
the
same
number
of
cells.
Unless
again
you're
dealing
with
a
defined
mute,
the
becoming
a
male
happens
in
the
post-embryonic
phase.
So
this
is
actually
a
classification
problem.
You
look
at
these
expression
profiles.
A
A
You
know
you
can
look
through
this
paper
to
get
a
sense
of
like
you
know
what
this,
what
this
landscape
looks
like.
You
can
get
a
sense,
look
at
the
atlas
and
see
if
it's
useful.
It
will
definitely
tell
you
more
about
the
lineage
tree
for
in
terms
of
what
we're
doing
with
image
processing.
It
may
be
less
useful,
but
it
may
also
be
you
know
something
we
can
use
as
sort
of
auxiliary
Aid
in
making
some
of
these
decisions.
A
So
I
don't
want
to
you
know:
I,
don't
want
to
bound
you
in
terms
of
your
ambition,
but
I
will
warn
you
that
this
kind
of
data
is
very
hard
to
work
with.
Unless
you
really
know
what
you're
doing
so,
I
wouldn't
spend
a
lot
of
time,
trying
to
figure
out
the
perfect
way
to
incorporate
transcription
data.
Would
just
assume
that,
like
you
know,
if
we
can
make
a
quicker
inference
from
their
their
data
from
their
Atlas,
that's
great,
but
I
wouldn't
get
too
deeply
into
this.