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From YouTube: DevoWorm #14: New Directions in Networks, One-cell Embryo Physics, and Jamming Phase Transitions
Description
Google Summer of Code update, New Directions in Developmental (Embryo) Networks and journal special issue, one-cell embryo physics and connections to networks, tensegrity networks, instances of jamming transitions (phase transitions) in embryogenesis. Attendees: Susan Crawford-Young, Karan Lohaan, Jiahang Li, Gopinath Balaruguman, Richard Gordon, and Bradly Alicea
C
B
B
D
I
did
have
some
updates
related
to
that
approximation
function.
You
know
because
I
was
trying
to
use
transformations
of
those
xy
coordinates.
So
I
it's
still
like.
I
didn't
make
any
progress
in
terms
of
you
know,
making
the
model
better,
but
I'm
trying
out
different
techniques.
So
that's
that's
one
thing
otherwise,
yeah!
That's.
That
was
the
only
thing
you
know
I'm
trying
out
playing
there.
D
E
And
I
was
actually
trying
to
work
on
the
gnns
for
biomedical
embedding
and
actually
like
we,
I
I
was
co-authored
in
a
paper
in
which
we
tried
to
improve
the
expressive
power
of
the
graph
neural
networks
and
it's
actually
published
in
this
year's
iclr
conference.
If
you
know
it
like,
I
was
actually
thinking
of
using
that
concept
to
come
up
with
more
expressive,
embeddings
for
our
biomedical
analysis,
developmental
bio,
biological
analysis,
so
I've
been
working
on
that
and
and
the
draft
too
so
hopefully,
maybe
tonight
it
should
be.
C
C
D
Especially,
the
thing
is
the
images
that
I'm
currently
dealing
with
that
are,
I
think,
like
currently
the
best
I
think
stage
four
adds
a
lot
legs,
because
the
outlines
are
you
know
like
whenever
I'm
trying
to
specifically
you
know,
get
a
very
clear
outline
of
that
particular
embryo.
So
so
far
the
data
set
that's
performing
the
best
right
now
is
the
stage
four
axolotl
egg
data
set.
So
if
I
could.
D
C
C
D
The
thing
yeah
that
would
really
help
in
you
know
getting
an
approximately
if
I
have
the
3d
generated
model
through
my
method,
and
I
have
the
video
data
as
well,
which
can
generate
a
very
close
approximation
like
let's
say
my
model
gives
an
80,
accurate,
3d
model
and
we
have
another
way
of
you
know
getting
that
99
percent,
so
it's
it'll
be
good
for
comparing
like
I'll,
be
able
to
compare
that
the
axolotl
model
that
I'm
generating
is
at
least
that
wasn't.
You
know
accurate
something
along
those
lines.
D
C
C
B
Good
luck
and
if
you
have
questions
please
let
me
know
you
know
anytime,
just
put
in
the
put
a
question
in
the
slack
or
whatever
send
me
your
drafts
and
I'll
recommend.
You
know
what
the
flow
should
look
like,
and
I
think
I've
mentioned
it
enough
in
the
meetings.
Everyone
should
know
what
that
should
look
like.
So
I'm
looking
forward
to
reading
it-
and
I
know
that
I
pinned
some
things
in
the
slack
and
I
wanted
to.
B
Maybe
I
don't
remember
if
I
did
this
last
week,
but
clarify
some
of
the
details
of
the
scheduling.
So
it's
a
12
week,
it's
nominally
12
weeks
long.
You
can
extend
it
out
a
little
bit
if,
for
this
part,
the
projects
that
you're
applying
to
in
diva
worm
the
it's
it's
12
weeks,
half
time.
So
it
would
be
like
you
know,
12
weeks,
20
hours
or
something
like
that,
and
if
you
want
to
spread
out
your
time
you
can
so
you
can,
you
know,
spread
it
out
across
more
weeks
of
the
year.
B
You
can
extend
it
into
I
guess,
september
october
november,
but
I
don't
know
what
the
details
of
that
are
it'd
be
best
if
we
just
kept
it
to
12
weeks,
because
it's
easier
to
work,
you
know
to
do
the
interactive
part
so
and
then-
and
so
that's
that's
what
you're
planning
for
when
you,
when
you
write
your
schedule
up.
Okay,
you
want
to
make
sure
that
it
doesn't
too
much
in
one
week.
B
You
know
you
don't
propose
that
you
do
a
bunch
of
stuff
in
one
week
that
you
can't
complete
in
80
hours,
so
you
want
to
spread
it
out.
You
want
to
find
you
know,
figure
out
the
right
amount
of
work
that
you
can
do
in
that
time
period.
So
I'm
just
letting
people
know
that,
because
that's
often
a
sticking
point
throughout
you
know
every
year
it
seems
to
be
kind
of
a
sticking
point,
because
you
know
if
you
bite
off
more
than
you
can
chew.
B
It's
always
tough
to
you
know,
try
to
figure
out
what
to
do.
If
you
can't,
you
know
fulfill
what
you've,
what
you're
trying
to
do.
So
you
know
you
want
to
have
that
executable
file
at
the
end.
So
to
get
to
that
point,
you
really
have
to
plan
out
exactly
how
this
is
going
to
work
and
how
to
out
your
time.
So,
and
you
know
again,
I
can
work
with
you
on
that.
If
you're
having
trouble
hello
dick,
how
are
you.
B
B
C
F
C
F
F
C
F
C
C
F
C
F
C
Yeah,
well,
you
could
look
at
their
vertices
and
the
shape
of
the
cells
and
yeah.
We
have
the
literature
on
this,
so
I
guess
we
could
do
it
easily
yeah.
I
have
some
literature
on
on
vertex
models
actually
kind
of
working
with
it.
With
my
simulations,
oh
okay,
good
yeah.
I've
got
a
symmetric
model,
I'm
going
to
use,
and
then
I
thought
well,
why
not
just
elongate
the
sides
and
and
make
it
to
what
they
would
call
liquid
and
then
see
the
difference
in
the
behavior.
F
C
B
B
The
things
you
were
just
talking
about
so
this
is
some
of
the
things
let's
see.
This
is
something
that
I
think
dick
sent
me
on.
I
think
these
these
are
cell
neighbors
bibliographies.
So
this
is
something
that
dick
generated-
and
this
has
a
number
of
different-
let's
see
how
many
references
there
are
a
lot
of
references
here,
but
these
are
references
for
the
cell
neighbors
literature,
so
this
has
a
lot
of
relevance
to
some
of
the
network
stuff
that
we've
been
talking
about.
B
So
there's
this
paper
on
cell
competition,
how
to
eliminate
your
neighbors
there's
partial
coupling
of
the
cell
cycles
of
neighboring
imaginal
cell
disk
cells.
So
these
are
all
things
that
involve
like
neighboring
cells.
I
think
most
a
lot
of
them
are
in
embryos.
Some
of
them
are
in
collectives
of
cells.
B
B
B
F
F
A
B
F
B
B
B
B
B
So,
like
there's,
been
a
lot
of
work
on
cell
neighbors,
you
know
cell
cell
coupling,
but
thinking
about
a
new
network
allows
us
to
open
new
avenues
to
this.
So
then
they're,
you
know
more
theoretical
and
and
quantitative
papers
like
this
paper,
average
topological
properties
of
successive
neighbors
of
cells
and
random
networks.
This
is
something
out
of
the
condensed
matter
literature.
So
this
is
pretty
much
like.
B
You
know
more
of
a
typical
type
of
network
paper
where
they're
looking
at
the
statistical
physics
of
the
network,
topology
so-
and
I
don't
have
a
nice
primer
on
that
right
now.
But
basically
you
know
you
take
a
network
and
you
can
calculate
these
statistics
these
so
not
really
they're,
they're
sort
of
you
know
summary
statistics
for
the
network
and
they're
different
measures
that
you
can
use
and
they
have
different
criterion,
and
so
that's
what
they
call
sort
of
the
statistical
physics
of
networks,
and
so
we
can
do
something.
B
But
you
know
beyond
that:
it's
like
people
haven't
really
looked
at
any
of
the
you
know
these
kind
of
network.
You
know
these
kind
of
networks
that
we're
talking
about
in
development
and
in
in
other
domains
of
of
biology,
especially
where
cells
are
behaving
collectively
and
doing
things
it.
You
know
in
a
dynamic
sense,
so
so
yeah.
We
have
a
a
huge
bibliography
on
this.
B
B
B
This
concept,
maybe
a
little
bit
differently
than
what
dick
is
thinking
about,
but
I
I've
I
remember
this
paper
from
a
couple
years
back,
so
this
is
from
2019
liquid
brain,
solid
brains
and
in
this
case
there's
some
complexity,
people
they
work
with
the
santa
fe
institute
and
there's
an
institute
in
barcelona
as
well
that
focuses
on
biological
complexity
and
so,
in
their
case
they
talk
about
liquid
brains
and
solid
brains
and
they're.
Talking
about
the
network,
that's
formed
by
the
neurons
in
the
brain.
B
Some
of
these
networks
are
describable
as
static
sets
of
neurons
linked
in
an
adaptive
web
of
connections.
So
they
have
this.
You
know
we
think
of
a
connectome,
for
example,
is
having
this
static
topology,
it's
you
know.
The
nodes
are
in
a
certain
place
and
you
draw
the
connections
and
then
that's
a
network,
and
then
we
measure
that
network,
and
then
you
know
there
you
could
have
another
network
and
that's
also
static.
B
B
F
Okay,
the
other
thing
is:
if
you
look
at
tensegrity
networks
applied
to
the
concept
of
cytoskeleton,
they
are
usually
equivalent
to
what
we
call
solid
grains
here,
known
as
the
connections.
On
the
same,
however,
real
cytoskeleton
contains
motor
molecules
and
things
attach
and
detach
which
we
put
in
the
category
of
liquid
grains.
B
C
B
F
C
F
But
if
they,
if
the,
if
the
elements
or
the,
what
do
you
call
them
the
bars
connecting
them
if
they,
if
they
have,
if
they're
attached
to
a
motor
molecule,
so
there's
motion
along
all
the
bars,
the
1070
network
itself
can
change
in
a
continuous
fashion
and
there's
an
open
question
whether
that's
stable
against
collapse
or
not,
and
therefore
is
the
tensegrity
network
of
even
a
single
cell
working
yeah.
What
do.
F
F
Maybe
that's
it,
maybe
maybe
the
maybe
the
acting
gets
tensed.
If
it's
getting
shorter,
for
example,
you
know
when
it
gets
shorter
or
or
longer,
but
you
know
it
doesn't
feed
away,
but
you
know
what
exactly
what's
going
on?
I
don't
think
he's
understood
at
all
yeah
well
trying
to
find
you
know
if
you're
looking
online
yeah.
My
reading
of
ink
papers
is
because
he
has
not
gotten
beyond
the
static
network
approach
to
10
seconds.
F
C
B
F
F
F
C
B
So
that's
that's
great
yeah,
so
that
this
paper
yeah
they
talk
about
solid
networks,
well,
they're,
well-defined
and
physically
persistent
architecture.
Other
systems
are
formed
by
sets
of
agents
that
exchange
store
and
process
information,
but
without
persistent
connections
or
move
relative
to
each
other
in
physical
space.
B
B
I
imagine
yeah,
I
mean
that
would
be
another.
I
think
property.
If
you
look
at
ant
colonies,
I
mean
that's
kind
of
what
ant
colonies.
Do
I
mean
they
build
yeah?
You
build
networks
of
ants
and
they're,
basically,
a
laying
down
pheromone,
so
they're
following
that
pheromone
and
that's
information
and
yeah.
B
So
yeah
that
would
be
so
they're.
Very
it's
a
very
broad
category,
and
so
it's
basically
where
you
have
things
that
are
flowing,
there's
something
that's
you
know,
they're
constantly
being
modified,
and
then,
in
this
paper
they
talk
about
it's
a
very
general
approach
to
this.
They
ask
the
question:
what
are
the
key
differences
between
solid
and
liquid
brains?
B
They
talk
about
the
cognitive
potential
of
these
structures,
but
that
doesn't
mean
that
they
can't
just
simply,
you
know,
process
information
instead
of
you
know
going
the
full
does
it
have
a
cognitive
potential,
but
in
any
case
there
was
a
special
issue
on
this
in
philosophical
transactions
back
in
2019,
so
they
had
a.
This.
Is
the
theme
of
this
issue?
B
F
B
Yeah,
I
mean
they
kind
of
know,
but
maybe
not
like
they've,
been
able
to
isolate,
maybe
specific
memories
they've
been
able
to
like
reverse
a
memory
trace
of
in
like
rats
of
some
some
stimulus
but
like
they
know
that
it's
also
distributed.
So
they
don't
exactly
know
like
how
that's
all
put
together.
So
it's
kind
of
like.
B
Yeah
and
then
so
yeah
this
this
kind
of
goes
through.
You
know
some
of
these,
like
they
talk
about
slime
molds
as
examples
of
these
liquid
networks,
and
they
talk
about
different
other
other
examples,
and
then
they
get
into
this.
You
know
they.
I
talked
a
couple
weeks
ago
now
about
this,
a
neural
or
basal
cognition,
where
you
have
you
know
cognitive
things
that
aren't
really
in
brains,
so
it
could
be
like
in
a
collective
of
cells
or
it
could
be
like
in
some
sort
of
like
you
know,
organismal
context.
B
So
I
think
this
is
a
nice
picture
because
it
kind
of
shows
the
diversity
of
what
they're
talking
about
here.
So
they
they
talk
about
these
as
cognitive
networks.
These
are
all
liquid
brains
as
they
define
them
or
liquid
networks,
and
these
are
all
basically
you
know
they.
You
know
they
don't
go.
I
don't
know
if
they
would
go
as
far
as
to
say
that
they
all
have
cognitive
agency,
but
they
have
this.
B
You
know
like
behavior
and
then
this
robot
collective,
where
they're
all
they
have
brains
and
they're
communicating
in
an
anal
or
I
guess
this
is
an
ant
colony
and
in
anaero
context
you
have
slime
molds,
you
have
k,
is
an
immune
network
and
then
l
is
a
microbiome
community.
So
you
don't
necessarily
have
to
have
a
central
nervous
system
guiding
this
the
formation
in
these
networks.
B
B
B
So
that's
that's
the
that
sort
of
solid
liquid
brain
distinction.
This
paper
is
actually
oh
rally:
instability
in
the
inverted
one
cell,
amphibian
embryo.
I
think
I
brought
this
in
to
talk
more
about
the
axolotl.
We
talked
about
it
being
a
one
cell
embryo,
and
this
is
a
this-
is
dick's
paper
on
rally
instabilities.
F
B
F
A
circular
drum
has
vibrations
on
it
and
the
vibrations
can
take
on
different
modes
and
basically,
what
we're
saying
here
is
that
different
modes
can
grow
faster
than
others,
and
if
you
turn
an
axon
of
embryo
upside
down,
you're
effectively
creating
these
vessel
functions
and
they
may
determine
whether
or
not
the
upside
down
embryo
develops,
and,
as
I
recall
for
some
of
them,
you
will
get
dripping
of
the
cytoplasm
down
one
side
rather
than
the
middle,
and
it
goes
down
the
side.
You
can
get
a
normal
embryo.
It
goes
down.
B
B
F
B
Yeah,
so
these
are
calm
flow
simulations.
So
that's
a
plat
simulation
platform,
for,
I
guess
the
physics
yeah,
and
then
this
shows
the
simulations.
The
hyperbolic
cosine
fit
the
solid
lines.
So
these
symbols
are
the
simulations.
The
lines
are
the
fit
function
that
they're
fitting
it
to
from
top
to
bottom
respectively,
and
they
kind
of
talk
about
the
interface
and
the
expansion
of
the
interface
here
and
then
this
is
over
time
and
then,
let's
see
if
they
have
anything
else
here,
they're
the
oscillation
of
mode.
So
you
have
this.
B
These
modes
oscillate,
and
these
are
velocity
fields
in
the
middle
vertical
slice
of
the
sphere,
with
the
heavy
fluid
on
the
bottom.
This
figure
shows
that
the
numerical
values
estimated
for
the
effective
mass
and
table
two
are
reasonable,
so
they're
building
these
using
these
physical
parameters
and
they're
fitting
it
to
this
model.
F
For
those
yeah,
I
should
explain
for
those
who
are
not
familiar
with
the
eggs.
They
are
bottom
heavy.
In
fact,
that's
the
property
that
susan
is
using
with
the
flipping
the
former
flipping
maker.
So
if
you
turn
it
upside
down,
the
egg
will
write
itself,
but
if
you
hold
it
upside
down,
the
denser
fluid
that
was
on
the
bottom
is
now
on
top
and
and
it
has
to
slow
down.
F
You
can
see
in
one
case
the
flow
is
around,
that's
one
on
the
left
and
then
the
right.
The
flow
is
central
and
we're,
assuming
that
if
the
flow
goes
around,
it
can
align
the
microtubules
on
the
inner
surface
and
that
somehow
these
normal
developments,
but
because
down
the
middle,
the
microtubules
are
aligned
in
opposite
directions,
so
they
cancel
each
other
sets
and
development
stops.
B
B
B
Of
this
velocity
field
yeah,
so
that's
that's
a
another,
that's
sort
of
focused
on
axolotl
embryos
bringing
in
you
know
this
isn't
really
deeply
connected
to
networks,
but
this
gives
an
idea
of
you
know
how
this
physical
modeling
happens
in
these
one
cell
states
that
he's
one
cell
embryos
and
thinking
about
how
that
could
be
turned
into
a
network,
or
maybe
how
you
know.
Some
of
these
other
methods
could
be
used
in
conjunction
with
these.
B
So
what's
a
phase
transition,
so
this
paper
kind
of
gets
into
what
that
is,
and
then
they
talk
about
what
that
looks
like
so.
Biological
systems
display
a
rich
phenomenology
of
states.
So
those
you
know
those
one
cell
eggs
are,
you
know
they
can
even
within
that
which
you
might
call
a
state.
They
can
exhibit
a
lot
of
different
modes
of
physics
in
a
of
you
know,
net
different
pathways
that
the
egg
will
go
down
later.
B
B
These
phases
result
from
the
interactions
between
the
microscopic
constituent
components,
the
cells
that
manifest
in
macroscopic
properties
such
as
fluidity,
rigidity
and
resistance
to
changes
in
shape
and
volume.
So
the
cells
when
they
act
collectively
they
form
these
structures,
these
collective
structures,
and
they
have
these
physical
properties
and
those
physical
properties.
Then
we
can
model
that,
with
a
network
we
can
use
physical
modeling.
We
can
do
other
things
like
that.
B
So
phase
transitions
are
these
rapid
changes
in
the
state
so,
like
you
know,
you
may
have
a
very
gradual
change
in
the
state.
But
what
tends
to
happen?
Is
you
have
these
rapid
transitions
in
state
where
some
transition
from
one
type
of
matter
to
another
or
one
type
of
network
to
another?
You
know,
phase
transitions
typically
tend
to
be
very
like
have
a
threshold
value.
So,
like
you
know
it's
what
phase
transitions
aren't
generally
very
gradual.
B
Sort
of
a
critical
point
they
get,
you
know
they
make
their
transition.
So
there's
a
an
associated
phase
transition
physics
to
this.
They
have
in
physics
these
first
order
phase
transitions
which
are
changes
from
one
state
to
another.
So
this
is
something
we
can
use
to
characterize.
Some
of
these
large
scale
changes
in
the
in
the
embryo,
additionally,
collectively
moving
confluent
cells,
which
means
that
they're
packed
together
can
also
lead
to
kinematic
phase
transitions
similar
to
multi-particle
systems,
where
the
particles
can
interact
and
show
sub-populations
characterized
by
specific
velocities.
B
They
can
interact
between
one
another
using
say
signaling
molecules
or
they
can
create
like
extracellular
matrix.
There
are
all
these
things
that
biological
systems
do
that
are
very
unique
to
this
type
of
of
jamming
transition,
and
so
this
kind
of
goes
through
some
of
the
emerging
approaches
that
they're
using
to
detect
some
of
these
yeah.
F
C
C
B
B
C
B
Yeah,
so
the
rest
of
this
paper
just
kind
of
goes
through
some
of
these
examples.
So
they
have
some
nice
pictures
here
where
you
have
some
good
examples
of
the
different
types
of
material
states
in
embryos.
So
you
have
from
gases
liquids
to
solids,
so
you
have
what
they
call
gas,
which
is
where
the
cells
are
the
gas-like
movement
of
mesenchymal
cells
and
chick
embryos
where
they're
moving
around
freely
and
then
liquid
means
liquid-like
movement
during
convergent
extension.
B
So
that's
convergent
extension,
and
this
is
an
an
example
from
drosophila
here
it's
hard
to
tell
what
the
organism
is.
This
is
in
drosophila
epithelia,
but
the
idea
is
that
the
cells
are
tighter
together
and
they
move
around.
And
then
you
have
these
solid
matrices,
which
are
these
cells
that
are
sort
of
in
place
and
they
have
this
extracellular
matrix
joining
them.
And
that's
where
you
find
that
a
lot
in
plant
tissues,
bones
and
crustacean
shells.
B
So
you
can
see
that
there
are
these
different
states
of
matter
that
can
form
through
these
different
phase
transitions
just
with
having
cells
that
are
aligned
in
certain
ways
and
and
put
into
a
larger
context,
and
so
they
show
examples
of
that
and
then,
let's
see,
then
they
show
kind
of
this
analogy
between
okay.
So
this
is
the
jamming
transition.
I
was
telling
you
about,
and
this
gives
you
a
little
bit
better
idea
of
what
this
looks
like
so
a
jamming
transition
in
biological
materials.
You
have
this
in
a
physical
system.
B
When
you
have
a
jammed
jam
system,
you
have
three
parameters,
you
have
temperature
density
and
stress,
and
then
this
is
a
space
where
you
have
jammed
particles.
So
this
is
like
a
glass
where
the
particles
are
jammed
together
and
they
form
a
solid
now.
In
other
cases,
you
have
unjammed
particles
outside
of
that
volume,
and
that
could
be
you
know
like
water
could
be
sand.
It
could
be
things
that
aren't
like
solid
masses.
B
So
there's
this
phase
transition
at
this
boundary
and
you
can
see
that,
like
the
boundary
conditions,
are
such
that
they
could
be
a
combination
of
temperature,
stress
and
density.
As
this
change
you
get
this
face,
you
cross
this
boundary,
you
get
this
phase
transition
and
you
get
this
change
in
the
in
the
material
property.
So
that's
the
physical
system,
where
you
have
a
jamming
transition
in
a
biological
system.
It's
a
little
bit
different.
You
get
these
sets
of
cells
that
jam
together.
B
So
you
have
jammed
cells
here
which
form
these
solid
masses
and
then
unjammed
cells,
which
are
doing
all
sorts
of
things.
They're
moving
they're
migrating,
maybe
they're
very
far
apart
and
they're
migrating,
maybe
they're
migrating
together,
but
then
there's
this
jam
state
where
they
form
some
sort
of
structure
could
be
a
tissue.
It
could
be
like
a
hard
layer.
B
B
So
this
is
the
mesodermal
mesodermal
progenitor
zone
and
pz,
and
then
that's
fluid-like,
and
you
have
this
jamming
transition
over
time
where
you
get
or
over
space,
even
where
you
get
the
solid-like
psm,
which
is
the
pre
pre-semitic
mesoderm.
So
the
these
are
pre-somites
basically
and
it's
it's
mesoderm,
but
it's
a
different
type
of
mesoderm.
B
B
The
point
here
being
that
acto
meios
meiosin
accumulation
fluidizes
tissue,
so
you
can
have
an
accumulation
of
different
things
in
the
extracellular
matrix
things
that
join
the
cells
together
whatever
and
that
can
change
the
state
of
that
collective
of
cells.
That's
the
lesson
here
to
be
learned,
so
you're
going
to
say
something.
B
C
F
C
B
B
So
I
think
that's
there
yeah
there's
much
more
on
this
paper,
but
it's,
it's
kind
of
you
know,
there's
a
lot
here,
it's
a
review.
So
if
you're
interested,
I
can
post
the
paper
and
you
can
read
it
and
you
can
follow
up
on
it
this
this
kind
of
shows
some
of
the
measurements
of
of
this
sort
of
these
sort
of
phase
transitions,
and
they
actually
have.
You
know
some
parallels
to
networks.
B
So
you
have
these.
You
know
where
you,
you
basically
use
a
segmentation
procedure
to
detect
different.
You
know,
particles
and
a
flow
or
cells
in
a
flow,
and
then
you
use
cross
correlation
to
figure
out
their
relationship,
and
then
you
can
find
cell
tracks.
So
when
they
move
across
the
frame
you
know
from
frame
to
frame
what
direction
are
they
moving
in?
B
You
know
what
is
that
and
how
do
they
behave
collectively,
so
that
tells
us
something
we
can
actually
detect
the
moment
when
things
jam
up
or
you
know,
if
there's
going
to
be
a
propensity
for
jamming
in
a
certain
tissue.
So
you
basically
take
these.
You
know,
I
guess
they're
cell
nuclei.
They
could
be
other
markers
and
cell,
you
segment,
those
you
create
a
frame
of
dots.
You
do
a
cross-correlational
analysis
on
this
by
their
position,
and
then
you
assemble
these
cell
tracks.
B
B
So
this
is
again
making
this
connection
between
the
physical
system
and
the
biological
system.
So
you
have
this
fluid
dasawa
jamming
transition.
Again
you
have
something
that's
flowing
and
then,
as
the
number
of
particles
increases,
it
slows
down
and
it
jams
into
a
solid,
and
so
that's
a
major
transition
here.
B
So
there's
this
imperative
to
have
both
of
these
properties-
and
this
is
something
you
see
in
like
glass
blowing
where
glasses,
you
know,
they're
trying
to
shape
it
into
a
different.
You
know
they
want
the
properties
of
glass,
but
they
want
to
shape
it
into
some.
You
know
ornate
structure
like
a
you,
know,
a
sculpture
of
some
type,
so
you
want
both
of
these
properties
and
so
disordered.
B
Soft
materials
such
as
foams,
emulsions
and
colloidal,
suspensions,
so
foams,
we
know
what
those
are
emulsions
are
where
things
are
emulsified
and
then
colloidal
suspensions
are
where
you
have
particles
in
a
liquid
switch
from
a
fluid
like
to
solid-like
behaviors
at
a
jamming
transition.
So
this
is
that
critical
point
where
things
jammed
together.
B
B
They
just
have
this
lassie
dynamics.
I
guess
means
it's:
jamming
and
jamming
in
cultured
epithelial
monolayers
behaviors,
recently
predicted,
theoretically
and
proposed
influence,
asthma,
pathobiology
and
tumor
progression.
So
they've
again,
they've
observed
this
jamming's
selective
jamming
in
epithelial,
monolayers
and
they've,
linked
it
to
different
phenotypes.
B
B
So
these
two
again,
these
two
areas
that
we
talked
about.
They
were
able
to
measure
this.
We
uncover
an
anterior
posterior
and
cadherin
dependent
gradient.
So
this
is
a
gradient.
That's
located
in
in
anatomical
space,
that's
specific
to
anatomical
space
and
yield
stress
that
provides
increasing
mechanical
integrity
consistent
with
the
tissue
transitioning
from
a
wetter
to
a
drier,
foam-like
architecture.
B
So
they're
actually
able
to
observe
this
as
a
foam-like
architecture
and
show
that
there's
this
transition
between
two
states,
and
so
they
show
that
cell
scale
stresses
fluctuate
rapidly
within
about
one
minute,
enabling
cell
rearrangements
and
effectively
melting
the
tissue
at
the
growing
end.
So
I
know
people
might
be
familiar
with
like
what
they
call
annealing
or
simulated
annealing,
and
this
is
maybe
a
similar
process.
B
Where
there's
this
annealing,
where
you
you,
don't
really
heat
or
cool
anything,
it's
just
like
it
changes
state
based
on
you
know
whether
something
needs
to
be
changed
so
like
in
annealing.
You
have
where
you
take
a
piece
of
metal,
you
heat
it
up
and
then
you
can
bang
it
into
a
certain
shape,
and
then
you
cool
it.
So
it
stays
in
that
shape
and
that's
kind
of
what's
happening
here
when
they
say
melting
in
quotes,
that's
kind
of
what
they
mean.
B
Persistent
stresses
at
supracellular
skills,
rather
than
cell
scale,
stresses
meaning
it
happens
across
multiple
cells
at
once,
guide
this
morphogenetic
flow
in
fluid
like
tissue
regions,
and
then
they
just
kind
of
talk
about
the
spatiotemporal
control
fluid
like
and
solid-like
tissue
states.
Now
this
represents
a
generic
physical
mechanism
for
embryonic
morphogenesis.
B
So
we
have
some
images
here
where
they
show
some
of
these
anatomical
locations.
This
is
the
pre-semitic
mesoderm
here
this
is
the
mesoderm
progenitor
zone.
So
you
can
see
that
this
is
happening,
as
this
shape
is
starting
to
form.
Things
are
unfolding
across
the
anatomy.
You
get
these
different
zones
and
there
are
different
things
happening
in
them.
So
you
can
see
here
this.
This
is
a
frontal
plane
and
a
sagittal
plane
which
are
just
different
slices
through
the
embryo,
and
you
can
see
that
this
is
the
area
they're
talking
about
this
is
the
anterior
end.
B
This
is
the
posterior
end,
so
you
can
see
that
there's
this
spatial
distinction
between
things
and
that
they
unfold,
and
so
that's
there
are
other
figures
here.
I
guess
this
is.
This
is
another
example
of
the
diagram
that
I
showed.
I
think
it's
the
same
group.
They
have
a
similar
diagram,
but
they
show
this
diagram
of
active
fluctuations,
supracellular
stresses
and
volume
fraction
again
these
three
parameters:
they
have
this
jammed
zone
and
they
show
these
two
anatomical
areas
within
this
jam
zone.
B
So
they
show
this
this
mpz
region,
which
is
here
in
the
psm
region,
which
is
here
and
then
they
show
that
you
know
there's
the
unjammed
region
out
here.
So
it's
within
the
jammed
region
and
they
just
show
like
what
the
you
know:
what
they're
relative
to
these
three
parameters,
so
the
mpz
region
shows
more
active
fluctuations,
but
it's
still
in
a
jammed
state.
B
The
I
guess
the
psm
by
contrast,
shows
more
supracellular
stresses,
but
it's
still
in
that
jam
state.
So
you
can
see
that
how
that
works,
and
then
I
think
that's
I
want
to
talk
about
with
this
paper.
So
I
think
that's
yeah.
It's
some
food
for
thought
today
and
I
wanted
to
go
over
those
because
I
know
we've
been
talking
about
cell
physics
and
I
think
we've
had
a
pretty
robust
discussion
on
this
stuff.
So.
B
B
Yeah,
but
I
mean
you
know
thinking
about
this
in
terms
of
computational
models
and
networks,
I
think
that
might
shed
some
new
light
on
it,
because
you
know
a
lot
of
the
studies
have
been
really
about
like
parameterizing
it,
and
you
know,
people
haven't
talked
about
other
types
of
sort
of
theoretical
models
or
computational
models.
That
would
be
it.
You
know,
find
some
interesting
things
out.
Aside
from
what
we
already
know.
B
D
B
Okay,
that
sounds
good.
Thank
you.
Yeah
and
again
the
proposals
are
due
in
the
19th,
so
we
have
another
about
a
little
over
a
week
to
work
on
that
and
get
a
good
proposal,
and
I've
posted
a
lot
of
things
in
the
slack
channel.
I've
pinned
them
so
they're
they're
easy
to
access
and
they
kind
of
go
over
some
of
the
things
that
you
know
need
to
be
there.
B
So
again,
the
most
important
thing,
I
think,
is
the
proposal
schedule
and
so
laying
out
what
you're
going
to
do
in
a
schedule
is
going
to
be
the
best.
It's
going
to
be
a
good
way
to
to
sort
of
focus
your
project
and
make
sure
you
can
deliver
the
deliverable
on
time.
So
again
you
know
I
I
say
we
just
stick
to
the
12-week
schedule
for
these
projects
and
then
and
then,
of
course,
we
will
have
a
community
period
too.
B
The
community
period,
as
I
usually
have
people
interact
with
people
in
other
channels
in
the
open
arm
slack,
and
I
also
like
to
have
people
I
always
like
to
do
some
discussions
on
some
of
the
sort
of
resources
surrounding
open
source
software
development.
So
there
are
a
bunch
of
resources
that
I've
collected
over
the
years
on
this,
and
so
you
know
talking
about
that.
More
broadly
is
always
useful.
B
B
So
if
you're
looking
for
data
to
include
in
your
proposal
or
work
through
something
using
a
data
set,
we
do
have
data
there
and
we
also
have
the
axolotl
data,
which
isn't
really
so
much
in
that
devo
zoo.
But
we
do
have
things
that
you
can
use.
We
can
get
you
the
data
so
in
for
the
proposals
I
just
want
to
have
like
some
small
proof
of
concept.
B
B
So
if
you
have
a
screenshot
or
like
a
some,
you
know,
even
if
it's
just
code,
I
guess
you
could
show
the
code
but
it'd
be
more
powerful
if
you
showed
like
a
screenshot
of
it
running,
and
so
that's
that's
what
we're
looking
for
that.
I
think
that's
really
something
that
would
you
know
say
to
me
or
the
people
evaluating
these
proposals
that
you
know
you're
really
ready
to
go,
and
you
can
do
this
in
the
amount
of
time
that
you
propose.
B
B
C
So
yeah,
it's
actually
what
I
am
working
on
with
my
simulation.
It's
actually
a
part
of
that
because
you
got
apoptosis
in
the
middle
of
a
grouping
of
cells
and
it
changes
the
mechanics
of
that
that
region,
or
maybe
even
the
margins
further
on
out
so
anyway,
I'm
just
doing
local
right
now,
but
yeah,
it's
been
fun.
To
put
that
together.
I'll
just
say:
yeah
quotes.