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From YouTube: DevoWorm (2021, Meeting 40): Diatom Model of Directional Movement, Morphogenesis and Models
Description
Update on submissions document and Diatom Deep Learning. Research on Diatom Model for Directional Movement and Jerkiness. Papers on Turing Morphogenesis, Branching Morphogenesis, and Soft Material analogies for Cell migration during Morphogenesis, and Attendees: Jesse Parent, Susan Crawford-Young, Richard Gordon, and Bradly Alicea.
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So
I
got
a
message
from
mujwal
and
azmid
and
and
arun
saying
that
they
would
be
wouldn't
be
able
to
make
it
at
this
time,
often
so
we're
not
getting
an
update
from
them
today
on
the
vessel
area,
but
thanks
for
thanks
to
them
for
joining
in
on
oktoberfest
and
working
on
the
vassal
area
stuff.
So
it
looks
like
they're
making
some
progress
on
that,
I'm
not
going
to
say
anything
more.
A
They
might
come
and
update
us
in
this
meeting,
but
we'll
meet
separately
because
it's
more
convenient
for
them,
but
yeah
they're,
making
some
progress
on
the
images
and
segmentation.
So
last
week
we
talked
about
how
they're
saying
you
know
how
they
their
choices
of
segmentation
and
how
they're
annotating
the
images.
So
I
think
that's
that's
good.
A
A
So
let's
go
to
the
screen.
Share
here
sharing
my
screen,
the
first
is
to
go
over
the
submissions,
and
so
again
our
submissions
are.
We
have
a
number
of
things
that
are
outstanding.
We
have
the
boring
billion
the
kindle
book.
Actually
in
the
boring
billion
area.
I
was
thinking
of
a
project
that
might
combine
the
boring
billion
with
some
of
the
early
embryo
stuff.
We
did
in
a
paper
back
in
2018.
A
A
A
So
yeah
this
boring
billion
ideas,
something
that
I
I
I'd
haven't
I've
kind
of
sketched
it
out,
but
I
have
to,
like
you
know,
put
a
couple
slides
together
to
explain
what
it
is,
this
mathematics,
a
diva
worm.
This
is
still
outstanding.
This
is
like
a
poster
of
different
equations
and
and
models
that
are
used
in
diva
worm.
I
was
thinking
maybe
like
a
short.
It
could
be
a
presentation.
It
could
be
a
short
paper,
I'm
not
really
sure
yet,
but
we
still
have
that
outstanding.
A
A
A
A
A
Okay,
that's
good!
That's!
So
that's
ongoing!
Then
there's
this
book
by
stephen
mcgrew
eye
of
nature.
I
was
reading
through
it
it's
a
very
interesting
book.
I
don't
know
what
we
want
to
do
with
it.
Of
course
that's
his
post-mortem
book
or
it
would
be
a
post-mortem
thing
that
we
would
do
to
honor
steve.
A
So
I
don't
know
what
that
would
look
like,
but
that's
then
we
have
these
different
conferences
and
workshops.
We
have
nur
ips,
which
is
in
december
and
the
neural
match,
which
is
in
this
early
just
early
a
little
bit
earlier
in
december.
So
it
doesn't
look
like
the
overlap.
I
know
we
were
talking
about,
maybe
the
overlap,
but
in
terms
of
near
ips
if
people
are
interested
in
machine
learning,
deep
learning
and
they
want
to
go
to
a
good
conference.
This
is
the
hallmark
conference.
A
That
would
you
know,
maybe
run
during
this
period
or
near
this
period
and
my
drawing
people
from
neuromatch-
and
I
think
jesse
and
I
have
been
talking
about
that
a
bit,
but
we
haven't
really
planned
anything
out.
We
gotta
we're
gonna
kind
of
have
to
move
on
that.
If
we
want
to
do
that
because
it's
already
november
1st,
so
I
don't
know,
but
your
thoughts
are
on
that
jesse.
A
F
F
H
A
A
A
I
I
G
B
G
G
G
G
G
G
G
Tiny
polystyrene
beads
sticking
to
the
mutage
outside
the
canal,
wraith
act
as
external
markers
of
this
transport.
In
summary,
the
lighting
movement
is
generated
by
mucilage
secretion,
coupled
with
cytoplasmic
transport.
This
motile
system
can
generate
significant
force
when
this
nichia
gets
stuck.
The
cell
shows
remarkable
persistence
in
trying
to
move
the
force
it
generates
loads
up
the
wall
like
a
spring.
G
E
E
E
E
E
E
E
E
E
This
is
a
picture
of
a
rafe
with
these
molec
with
these
mouths
sticking
out
and
you
can
see
they
look
like
they're
in
a
one-dimensional
row,
and
what
can
I
say
this
is
this:
is
the
only
picture
of
this
type
I've
ever
seen,
but
it's
suggested
in
a
one-dimensional
role.
We
we
call
these
things
rayfan,
okay,
rayfat
and
molecules.
They
are
probably
what
are
called
mucopolysaccharides.
E
E
E
Okay,
now,
if
it's
confined
to
a
one-dimensional
space,
it
will
fall
left
or
right.
Okay,
so
we
we
are
assuming
that
the
that
these
molecules,
while
they
come
straight
down
from
let's
call
this
the
membrane.
If
they
come
straight
down,
we
assume
that
they
will
swap
one
way
or
the
other
okay.
So
that's
what
our
assumptions
that
they
that
they
fall
over
okay.
So
now
I'm
going
back
to
my
okay
back
to
the
table
here.
E
That's
what
the
zero
is
an
empty
site.
Okay,
then
we
calculate
what's
called
the
run
length.
Okay,
so
the
run
length.
There
are
seven
of
these
in
a
row
they're
two
in
a
row,
there
are
two
zeros
in
a
row
three,
what
minus
ones
in
a
row
and
we
number
these
runs
okay.
So
this
is
our
basic
data
on
the
what
we
call
a
configuration
of
the
molecules.
E
Now
if
the
site
is
empty,
then
one
of
these
rayford
molecules
can
come
in
from
the
cell
membrane,
and
so
these
will
be
positive
numbers
force
for
number.
Seven,
eight,
nine.
Okay.
If
the
cell
is,
if
it's
already
full,
we
assume
that
nothing
can
come
in,
but
that
two
other
three
other
kinds
of
events
can
happen
that
either
the
whole
group
can
move.
Let's
take
this
one
and
number
10.
the
whole
group
can
move
left
or
it
can
move
right
or
it
can
hydrate
and
and
leave
and
leaves
it
will.
E
E
Okay,
so
that's
the
basic
part
that
they
move.
Groups
of
them
will
move
together.
Now,
let's
see
so.
This
indicates
a
full
site.
The
right
fan
can
hydrate
to
the
left
and
if
it
goes
to
the
left,
it
pushes
the
diatom
and
increments
to
the
right
and
it
becomes
empty.
If
it's
already
empty,
then
it
can
be
filled.
If
it's
fold
full-
and
it's
tilted
this
way,
then
it
can
hydrate
to
the
right
and
push
to
the
diamond,
the
diatom
in
the
opposite
direction
to
the
left,
and
it
also
becomes
empty
okay.
E
So
these
are.
This
is
sort
of
our
symbolic
arrangement.
Now
we
have
one
other
problem
that
sometimes
you
can
get
a
mixed
group
like
this.
We
assume
that
the
screws
can
rapidly
all
become
the
same,
and
we
assume
that
there's
a
bias
involved
which
is
possibly
introduced
by
an
electric
field
inside
the
cell.
That
would
cause
them
to
flip,
say
being
molecules.
They
can
just
do
it
by
what's
called
rotational
grounding
motion,
okay
and
for
now
we're
not
simulating
that
we're
just
assuming
it
happens
very
rapidly.
E
Okay,
cooperative,
rational
rotational,
brownian
motion,
okay,
okay,
now
so
what
we
do
is
we
can
we
have
a
step
called
concatenation
where
these
groups
become
consolidated
one
way
or
the
other,
with
a
bias
assumed
that
if
there's
an
electric
field?
Okay,
now,
let's
see
okay.
This
is
just
a
curious
thing,
which
is
not
explained
yet.
This
is
the
speed
of
diatoms
versus
temperature,
and
you
can
see
it
falls
off
and
I
make
a
reference
to
a
very
funny
paper
on
similar
curves
for
the
flight
of
the
house.
Slides.
E
H
E
G
E
E
E
Okay,
so
that
we
use
the
domino
theory,
then
for
the
propulsion,
so
here's
a
configuration
of
ray-finned
molecules
in
the
ray
if
they're
tilted
this
way
you
get
forward
propulsion
of
the
diatom
and
if
they're
tilted
this
way
you
get
backwards.
Propulsion,
okay,
see
nobody's,
come
up
with
an
explanation
for
backwards
for
backwards
propulsion,
but
when
the
diatom
is
moving,
as
the
movie
said,
it
moves
in
a
jerky
fashion.
It's
not
simply
jerky.
It
actually
goes
backwards.
E
E
G
E
Okay,
so
let's
let's
go
back
here?
Okay,
now?
What
does
the
swelling
do?
Well,
if
you
start
here
with
this
is
representing
the
reef,
and
this
is
the
diatom
trail.
If
we
start
in
this
configuration,
it
looks
like
each
ray
reef
when,
at
least,
if
you
have
a
high
speed,
diatom
high
speed
means
about
30
microns
per
second
okay.
If
you
have
high
speed
diatom,
then
for
each
one
that
comes
out
the
the
diatom
moves
three
units
in
in
the
opposite
direction.
We
don't
have
that
which
is
indicated
by
this.
E
E
However,
while
this
is
coming
out,
we
had
to
simulate
that
it's
also
filling
from
the
membrane
in
into
the
rafe,
so
it's
like
having
a
set
of
diaminos
fall
over
where
you
can
set
up
as
fast
as
they
fall.
Okay,
okay!
So
that's
a
bit
different
from
the
human
situation.
Okay,
now
one
of
the
things
that's
kind
of
neat
here
is,
if
you
look
at
these-
are
wolfram
patterns
developed
by
his
by
an
assortment
of
his
rules,
and
some
of
them
are
what
are
called
chaotic
pattern.
E
E
E
Okay,
so
this
this
shows,
you
know,
here's
one
slipping
and
then
finally,
hydrate,
let's
see
over
here,
is
one
that
diffused
one
unit
over
here.
We
only
we
only
have
50
50
events
simulated
here,
so
there's
one
change
from
every
between
each
line:
okay,
yeah,
okay,
so
this
program
took
us
a
couple
of
months
to
be
debugged
by
the
way
and
shredding
christian
did
a
hell
of
a
job
yeah.
I
imagine
I
I
gave
her
a
middle
name
after
the
the
new
martian
rover
persistence.
Oh.
D
E
E
E
Okay,
yeah
here,
okay,
now
here's
an
example
where
there's
no
bias,
so
the
diatom
keeps
moving
forward
and
backward
and
forward
and
backward
backward
forward
forward
forward
backward
back.
Okay,
now
one
thing
we're
doing
is:
we
include
a
gradient
like
like
sort
of
like
this,
that
falls
off
exponentially
so
that
most
of
the
raphin
comes
on
the
left
side.
So,
despite
that,
this
is
pretty
erratic,
but
if
you
make
the
bias
increase,
you
start
getting
motion
mostly
in
one
direction.
Here's
point
six,
and
this
is
only
50
events.
E
So
here's
point
seven
goes
forward
and
then
the
whole
thing
reverses:
okay,
here's
the
bias
of
0.8,
and
now
you
can
see
it's
going
forward
most
of
the
time,
but
occasionally
it
goes
backwards.
Okay,
this
is
probably
a
more
realistic
one
and
then,
if
you
make
the
bias
completely,
let's
say
this
is
0.9.
Okay,
0.9
does
pretty
much
the
same
same,
going
backwards
forwards.
It
so
there's
a
general
average
velocity
of
the
diatom
but
occasional
motion
backwards,
and
if
you
make
the
bias
one,
then
it's
all
forward.
E
E
J
E
A
D
D
A
E
E
Okay,
but
the
drift
the
drift
is
substantial,
which
gives
the
diatom
generally
moving
in
one
direction
and
all
diatoms
move
one
direction.
Then
they
eventually
reverse.
We
don't
know
what
reverse
what
makes
it
reverse
we're
speculating.
There's
an
electric
field
that
inside
the
diatom
that
can
reverse,
but
we
don't
know
nobody's
looked
for
it,
okay,
okay,
our
attitude
about
the
simulation
is
that
it
approximately
produces
the
jerky
behavior
of
the
diatom.
E
Okay,
the
I
think,
one
of
the
novel
things
here
is
the
introduction
of
the
domino
theory
which
leads
to
constant
motion
constant
speed,
except
for
the
jerkiness
right
so
and
then
the
concerted
diffusion
allows
whole
groups
to
move
at
once.
We're
not
sure
how
important
that
is,
that
that
would
require
further
playing
with
the
program
or
enhancers.
D
E
A
E
E
Yeah
24
yeah
yeah
this
book
just
was
just
public
right.
Okay,
okay
and
stan
cohen's
got
a
lot
of
a
lot
of
information
that
he's
put
in
it
on
the
response
of
diatoms
to
light
and
and
whatnot
a
little
bit
of
discussion.
They
implicate
it
might
imply
an
electric
field,
but
the
no
measurements
yet
either
external
or
sticking
or
trying
to
stick
an
electrode
in
them.
E
Okay,
I
think
that's
a
critical
thing
about
basilaria,
I'm
not
sure
if
any
of
the
models
and
the
parameters
of
the
model
would
allow
smooth
motion-
or
not
I
mean
we,
you
know
we
went
for
jerky
motion,
then
we
got
it
okay,
yeah,
but
we
had
to.
We
introduced
these
novelties
of
concerted
motion.
Conservative
fusion,
concerted
hydration
and
the
domino
effect
okay,
great.
G
E
A
E
E
C
E
E
E
E
They're
not
picked
uniformly
they're
picked
in
random,
proportional
to
the
rate
constants
you
assigned
to
the
different
events:
okay,
yeah,
and
then
you
execute
that
event.
You
change
the
configuration
you
do
it
all
over
again,
so
it's
a
lot
of
computing,
but
it
didn't
slow,
it's
very
fast.
Anyway,
at
least
for
for
only
50s.
E
E
E
A
C
E
E
E
So
you
know
we're
not
saying
that
this
is
the
mechanism,
we're
just
saying
it's
a
possible
mechanism
and
it's
the
first
attempt
to
explain
it
at
a
molecular
level
now,
by
the
way.
One
of
the
one
reason
we
did.
This
is
because
in
reviewing
this
stuff
that,
if
you
look
at
that
volume
on
diatom
gliding
locomotion
motility,
there
are
a
couple
of
papers
which
state
very
strongly
that
there
is
no
evidence
that
the
molecules
inside
the
cell
are
linked
to
the
diatom
trail.
E
That's
a
standard
model
which
most
people
believe
and
there's
absolutely
no
evidence
for
it
other
than
if
you
destroy
an
actin,
it
stops
but
see
in
our
model.
The
role
that's
why
I
showed
to
the
bubble
at
first
in
our
model,
what
we
think
the
actin
is
doing
is
cytoplasmic
streaming,
which
causes
these
narrow
channels
of
flow
in
the
cell
membrane,
which
then
directs
the
with
the
rafen
to
the
rafe.
E
Okay
and
therefore
the
the
speed
of
the
diatom
can
be
similar
to,
but
it'll
also
be
quite
different
from
the
speed
of
cytoplasmic
streaming
caused
by
the
bundles
of
actin.
You
saw
the
bundles
mentioned
by
pika
peeps
in
the
movie.
By
the
way,
pika
heaps
did
a
lot
of
beautiful
work,
that's
a
segment
of
a
beautiful
movie
he
made
and
we're
trying
to
get
permission
to
get
it
up
online
now,
because
it
was
a
commercial
product
and
he
did
beautiful
work.
Unfortunately,
he
died
last
year.
E
A
A
B
E
Know
this
program
is
is
very
simple
in
its
construct
and
it's
really
just
a
whole
bunch
of
nested
ifs
and
dens
and
whatnot,
okay
and
loops.
So
it's
structured.
You
could
write
this
in
fortran
2.
If
you
wanted,
for
some
reason,
writing
it
in
mathematica
mathematica,
we
found
extremely
difficult
to
debug
yeah
and
they
got
so
frustrating.
It
took
the
mathematica
program
and
rewrote
it
python,
and
even
then
it.
A
B
A
A
This
is
from
journal
of
theoretical
biology,
so
we've
talked
about
turing
patterns
before
we
have
the
turing
reaction
diffusion
model
and
it
generates
patterns
through
the
activity
of
the
the
model.
So
the
idea
is,
you
have
different
gradients
of
of
chemicals
and
their
boundaries
and
those
boundaries.
Are
you
know
they?
When
those
gradients
kind
of
overlap?
You
get
boundaries
and
you
get
things
like
striping
or
other
types
of
patterns
in
chemical
systems,
and
this
is
something
we
see
a
lot
in
morphogenesis.
A
A
So
if
you
look
at
naturally
evolved
systems,
how
do
you
expect
a
turing
pattern
to
form
there?
I
mean
they
form
spontaneously
to
some
extent,
but
there's
also
a
genetic
control
aspect
of
it.
So
you
get
you
know
and
even
if
it
forms
spontaneously,
you
know
you
can't
ensure
that
you're
going
to
get
a
consistent
result
and
in
a
lot
of
animals
where
you
have
striping
or
you
have
something
like
in
the
drosophila
embryo,
where
you
get
stripes
or
in
other
contexts.
Those
stripes
are
usually
pretty
consistent
in
terms
of
how
they
come
out.
A
You
know
they're,
not
wildly
different
from
organism
to
organism.
So
this
is
how
you
know
we
have
to
wonder
how
these
things
evolve.
You
know,
if
there's
a
striping
pattern
that
evolves
how
this
remains
consistent.
So
the
vast
majority
of
mathematical
studies
of
turing
patterns
have
used
continuous
models
specified
in
terms
of
partial,
partial
differential
equations.
A
So
you
know
you
have
a
set
of
partial
differential
equations
and
you
can
describe
these
patterns,
but
those
those
kind
of
models
aren't
really
amenable
to
necessarily
to
the
biology.
I
mean
they're,
just
kind
of
abstractions,
of
the
biology
saying
this
is
what
is
going
on,
but
it
doesn't
map
to
say
some
specific
mechanism.
A
So
here
we
complement
this
work
by
studying
turing
patterns
using
discrete
cellular
automata
models.
So
now,
they're
using
and
we've
talked
about
using
cellular
automata
to
look
at
different
patterns
on
seashells
or
on
other
things,
so
here
they're,
actually
replacing
the
partial
differential
equation
using
these
discrete
cellular
automata
models.
A
We
perform
a
large
scale
study
in
all
possible
two
species
networks
and
find
the
same
turing
pattern
producing
networks
as
in
the
continuous
framework,
in
contrast
to
continuous
models
which
are
the
equations,
we
find
that
these
turing
pattern
topologies
to
be
substantially
more
robust
to
changes
in
the
parameters,
so,
in
other
words,
when
you
have
changes
in
parameters
of
the
model,
you
know
biochemical
noise
or
other
fluctuations.
Those
things
are
going
to
you
know
this
model
is
going
to
be
robust
to
those.
A
A
There's
a
lot
of
a
lot
of
these
kind
of
instabilities
that
occur,
and
so
you
want
to
be
robust
to
those,
and
so
these
are
actually
weaker
predictors
in
their
discrete
modeling
frameworks.
So
in
other
words,
these
modeling,
these
cellular
automata,
are
robust
to
those
kinds
of
things
that
drive
change
and
this
they're
more
robust
in
the
sense
that
the
presence
of
an
instability
does
not
guarantee
a
pattern
emerging
in
simulations.
A
H
E
Yeah
the
problem
I
ran
into
is
that
when
you
have
say,
turing
models
are
usually
used
for
cell
differentiation,
and
if
you
try
to
apply
it
twice,
it
doesn't
work.
Okay.
In
other
words,
if
the
cell
differentiations
are
another
cell
type
and
then
that
differentiates
into
more
cell
types,
the
you
get
what
I
call
hierarchical
instabilities.
E
The
turning
mode
just
can't
explain
the
structure
of
an
organism,
it's
great
for
a
single
step
of
differentiation,
sometimes
as
a
possible
explanation.
But
the
other
thing
is,
you
know,
there's
one
of
the
big
examples.
People
like
to
bring
out
is
the
angel,
the
marine
angelfish,
which
has
all
sorts
of
intricate
patterns
on
its
surface
and
people
who
studied
it
have
shown
that
the
cells
are
migrating
producing
these
patterns
that
they're
not
they're,
not
turing
mechanisms,
even
though
they
might
match
turing
mechanisms
yeah.
A
A
E
Like
it's
like
the
snail
stuff,
you
know
snail
patterns,
look
like
wolfram
patterns,
sometimes
right,
okay
and
therefore
people
assume
they
are
wolfram
type
patterns.
But
there's
no
proof
right
know,
and
I
can
give
you
an
analogy
if
you,
if
you
look
at
dried
mud,
it
forms
beautiful
cracking
patterns.
J
E
E
A
Okay,
here's
another
paper:
this
is
well.
This
is
from
the
buyer
archive.
I
don't
know
if
it's
been
published
in
a
journal
yet,
but
this
is
the
theory
of
branching
morphogenesis
by
local
interactions
and
global
guidance,
and
so
the
abstract
on
this
reads:
branching
morphogenesis
governs
the
formation
of
many
organs
such
as
lung,
kidney
and
the
neurovascular
system.
A
So
when
they
say
branching
morphogenesis,
they
mean
it's
a
ubiquitous
developmental
process,
where
a
number
of
morphogenetic
events
cooperate
to
give
rise
to
complex,
tree-like
morphologies,
so
in
this
case
you're
looking
at
like
different
types
of
branching
within
the
in
the
organ.
So
you
know
you
have
things
like
the
lung,
where
you
have
air
passageways
that
are.
G
A
Many
studies
have
explored
system-specific,
molecular
and
cellular
regulatory
mechanisms,
as
well
as
self-organizing
rules
underlying
branching
morphogenesis.
So
again
we're
back
to
the
self-organizing
rules
aspect
and
you
know
we
could
describe
that
as
a
trying
pattern,
a
return
process,
but
in
this
case
they're
looking
at
branching.
A
Specifically
so
in
this
case
they're
looking
at
a
different
kind
of
model,
in
addition
to
local
cues
branch
tissue
growth
can
also
be
influenced
by
global
guidance.
So
now
we
have
these
local
cues
between
cells,
like
you
might
see
in
a
cellular
automata,
but
you
also
have
this
global
guidance,
which
is
where
this
tissue
is
branching
and
there's.
You
know,
like
I
said.
Sometimes
it's
fractal
in
nature,
so
you
start
with
large
branches
and
then
they
become
progressively
smaller,
and
so
that
requires
some
global
guidance.
A
You
know
not
something
that's
like
conscious
or
supervised
by.
You
know
the
entire
organ,
but
just
something
that
is
beyond,
like
just
small
groups
of
cells.
So
here
we
develop
a
theoretical
framework
for
a
stochastic
self-organized
branching
process
in
the
presence
of
external
cues
combining
analytical
theory
with
numeric
simulations.
We
predict
differential
signatures
of
global
versus
local
regulatory
mechanisms
on
the
branching
pattern,
so
these
are
things
such
as
angle,
distribution,
domain,
size
and
space
filling
efficiencies.
A
A
A
H
A
A
And
shaping
for
infrastructures
across
scales?
So
again
you
know
some
of
these
global
guidance
mechanisms,
you
know,
could
be
things
like
chemical
gradients
or
they
could
be
electrical
fields.
I
know
I
think
it
was
maybe
two
weeks
ago
we
talked
about
the
flatworm
and
what's
interesting
about
the
flatworm,
is
you
can
take
a
single
cell
out
of
a
flatworm
and
you
can
put
it
in
a
dish
and
that
single
cell
will
reconstruct
the
entire
worm?
So
each
cell
is
2d
potent
and
then
it
generates
an
entirely
new
organism
and
one
of
the
so.
C
A
Is
that
that
flatworm
there's
a
an
electrical
field,
that's
generated
by
that
cell
and
it
gives
global
information
so
as
that
cell
is
sort
of
dividing
and
filling
up
space
to
make
a
new
worm,
it's
being
guided
by
these
electro?
I
guess
electrochemical
cubes,
so
that
that's
that's
one,
one
way
of
sort
of
coordinating
globally
and
again
it's
it's
defining
that
territory.
What
is
the
worm
supposed
to
look
like
from
a
single
cell?
And
we
don't?
A
G
E
A
A
So
these
branches
kind
of
know
that
they
are
supposed
to
avoid
themselves
and
not
get
tangled
up
the
field.
Strength
increases
the
field.
Strength
increases
you
get
sort
of
this
more
sort
of
regular
branching
kind
of
like
a
fan,
in
this
case
it's
more
randomized,
and
so
you
can
see
that
this
field
strength
whatever
this
is
represented.
E
A
Yeah,
finally,
let's
see
which
paper
do
I
want
to
do
here
how
about
this
one?
This
is
soft.
Actually,
this
is
a
soft
matter
paper,
so
this
might
be
something
susan
to
be
interested
in,
but
I'll
do
it
now
and
I'll
send
her
the
paper.
This
is
nice,
I
guess
in
soft
matter.
They
have
these
nice
images
here
on
the
front
cover
yeah
fingering
in
stability
and
spreading
epithelial
monolayers.
A
So
this
is
going
towards
the
formation
of
the
epithelium,
which
is
a
part
of
the
embryo
roles
of
of
cell
polarization.
Substrate
friction
and
contact
contractile
stresses.
So
this
is
where
we're
looking
at
the
formation
of
a
sort
of
a
tissue
layer,
and
it's
basically
talking
about
the
physics
of
this
process.
So
I.
A
A
A
A
E
Kind
of
feeling,
where
a
cylinder
of
fluid
breaks
up
into
drops
right
right,
you
can
watch
it
just
by
slowly
running
a
kitchen
tap
right.
You
see
by
the
time
it
falls.
It
broke
breaks
in
the
drops,
yeah,
okay-
and
you
know
that
would
be
my
first
assumption
in
anything
like
this-
that
these
are
rayleigh
instabilities
and
they're.
You
know
they're
initiated
at
a
molecular
level.
Variations
at
a
molecular
level
is
presumed,
presumably
right.
A
E
G
A
This
is
a
migrating
epithelial
monolayer
if
we
have
a
if
we
look
at
the
cells
here,
so
these
are
moving
in
this
direction
and
you
have
all
these
different
types
of
physical
parameters
in
your
model.
So
they
have
pneumatic
length
which
you
mentioned.
Is
this
sort
of
type
of
organization
and
there's
a
length
to
it?
Shear
viscosity
traction
forces
cell
substrate,
friction
intercellular
contractility,
which
is
how
the
cells
are
contracting
against
one
another
and
then
the
surface
tension
yeah.
E
E
E
H
A
E
E
E
A
Contractile
stress
that
depends
on
cell
substrate,
friction
and
the
initial
to
pneumatic
length
ratio
characterizing
an
active
wedding,
wedding
transition.
So
this
is,
you
know.
This
is
a
lot
of
physics
that
they're
just
applying
to
this
model,
and
we've
talked
about
this
soft
materials
and
how
you
know
we
can
learn
from
some
of
the
stuff
that's
going
on
in
soft
materials
and
how
they
apply
to
biological.