►
From YouTube: DevoWorm #40: Tensegrity in Sim and Cells, DW Annual Review, Feedback and Waves, Morphogenesis
Description
Modeling tensegrity using COMSOL, MATLAB, and cells. DevoWorm annual activity review (for the OpenWorm annual meeting). Discussion of paper on morphogenetic waves (vs differentiation waves), and assorted papers on materials and morphogenesis. Attendees: Richard Gordon, Susan Crawford-Young, Bradly Alicea, and Morgan Hough
A
A
B
C
A
C
C
B
I'm
hoping
that
the
new,
the
new
version
of
console
will
actually
allow
that,
because
it
they
kind
of
give
an
introduction
to
that,
and
they
said
that
it
would
do
an
experiment
for
you
or
it
would,
for
instance,
put
in
different
frequencies
and
say
where,
where
it
was
unstable,
rather
than
just
throwing
errors
like.
Oh.
B
B
C
B
B
C
C
B
A
C
C
C
A
C
C
B
B
B
B
C
A
C
A
C
B
C
B
C
B
B
C
B
Then
to
work
better
because
it
certainly
didn't
want
to
mash
this
like
away
and
said
I,
don't
even
care
I,
don't
care
what
it
looks
like
I
I
put
in
like
I
tried
a
circle
first
and
it
didn't
have
enough
nodes
on
it.
It
had
two.
It
made
it
to
like
two
halves
spheres
together,
and
so
there
weren't
any
points
in
which
I
could
bought
my
structure
out
with.
Oh
so
I
made
this,
and
then
it
wouldn't
Mash
anyways.
C
I
have
a
suggestion:
yeah,
okay,
there's
an
old
problem.
It's
been
sitting
around
and
that
is
the
stability
of
the
sulf-state
splitter
as
a
tensegrity
structure.
And
if
you
go
back
to
the
old,
the
first
paper
that
wave,
Broadway
and
I
did
to
be.
It
shows
a
whole
I
think
it
might
be
the
appendix
it
shows
a
whole
variety
of
possible
structures
for
States
South,
State
splitters.
C
B
C
C
B
B
C
B
C
C
C
Yeah
some,
sometimes
you
get
a
sheet
and
you
also
get
I'm,
not
sure
how
to
describe
a
sheet
of
cells
which
are
have
spaces
in
between
the
cells.
Oh,
some
of
the
cells
touch
from
a
network
of
cells
and
the
whole
network
of
cells
moves
rather
than
moving
as
a
Confluence
sheet.
Okay,
okay,
all
right.
When
that
happens,
the
place
I
saw
that
was
in
zebrafish
pigment
cells,
moving
over
the
yolk
sac
yeah
and
even
face
pigment
yeah
we
didn't.
Unfortunately,
we
got
our
paper,
we
got
reject
them.
We
never
got
published.
B
B
C
C
C
C
A
A
B
A
A
So
I'm
gonna
talk
about
this
week
or
tomorrow.
The
open
worm
Foundation,
is
having
its
annual
meeting
and
they
usually
every
year
they
have
a
meeting,
that's
where
the
board
gets
together
and
some
of
the
senior
contributors
will
give
you
updates
on
the
projects
and
talk
about
the
year
going
forward.
A
So
this
here
has
been
you
know,
kind
of
we've
had
some
things
going
on
in
the
in
the
foundations,
and
things
have
kind
of
sort
of
you
know
we
need
more
I
think
we
need
more
fresh
blood
in
the
organization,
but
that's
just,
but
we
have
done
some
interesting
things
this
year
and
I'm
going
to
give
a
talk
on
Diva
worm
and
I'm,
giving
a
very
short
talk.
It's
not
a
exhaustive
talk,
so
I'm
just
going
to
go
over
some
things
here
and
I'm
going
to
share
my
screen.
A
A
A
So
we
had
forced
that's
a
lot
of
students.
Typically,
we
have
one,
maybe
two,
but
we
had
four
of
us
here.
So
one
of
the
projects
was
developmental
graph,
neural
networks.
These
are
graph
embeddings
for
analyzing,
cellular
development,
and
so
the
the
nice
thing
here
is
that
wattaro
and
Jihan
work
together
on
this
and
they
took
the
project
and
they
developed
a
pipeline
which
are
focused
on
the
image
segmentation
focused
on
implementing
the
graph
embeddings.
A
A
So
now
it's
this
software
was
brought
to
completion
to
some
level
of
completion
and
it's
now
been
incorporated
into
the
diva
learn
platform.
It's
actually
in
the
repository,
not
in
the
release
that
we
have
yet,
but
there's
this
devograph
repository
at
evil.
It's
a
diva
learn
organization
and
you
can
download
the
software
from
there.
You
can
go
through
the
you
know,
documentation
and
other
things
and
and
maybe
run
it
for
yourself.
So
this
would
be.
This
is
a
nice
addition
to
the
Steve
alarm
platform.
A
I
haven't
really
been
able
to
follow
up
on
it.
Yet
I
think
our
students
are
busy.
You
know
they.
They
spend
the
summer
very
intensely
involved
in
the
summer
of
code.
Then
they
have
things
to
do
in
the
fall
and
then
eventually
you
know
they'll,
maybe
you
know
come
back
and
do
some
more
work
or
you
know
maybe
next
summer
we
can
get
new
people
to
work
on
it
some
more,
but
we
have
the
Devon
platform
which
was
developed
a
couple
years
ago
or
over
the
past
couple
years.
A
Now
this
Devo
graph
component
to
it.
So
that's
going
to
be
a
nice
platform.
It's
going
to
be
a
nice
addition
to
that
platform
and
it's
going
to
make
that
platform
much
more
powerful,
and
so
you
know
we
can
use
the
graph.
So
we
can
use
the
graph
representations
for
looking
at.
You
know
the
spatial
structure
of
embryos.
A
We
can
even
look
at
things
like
tensegrity
and
we
could,
you
know,
use
that
as
sort
of
a
way
to
extract
these
networks
out
of
the
biology
and
then
put
them
into
this
world
of
the
computer.
So
the
next
project
was
digital
microspheres.
This
was
creating
an
atlas,
an
analysis
tool,
the
embryo
surface,
so
Susan
Crawford
young
has
developed
different
Imaging
tools
such
as
the
ball
microscope,
which
is
this
nine
point
of
view
device.
A
You
can
view
you
could
put
a
number
here
on
a
stage
and
view
it
from
nine
points
of
view.
You
have
two
dimensional
data,
which
then
needs
to
be
piled
onto
a
three-dimensional
surface
such
as
we
have
with
the
embryo.
So
this
is
a
screenshot
of
the
one
of
the
final
products.
We
had
two
approaches
that
were
kind
of
you
know:
they're
separate
apps
right
now,
but
they
can
be
made
into
a
single
platform.
A
A
C
A
A
Okay,
yeah
yeah
here
yeah
yeah,
okay,
and
so
we
had
two
approaches
in
the
you
know.
The
two
students
did
these
rival
approaches
they
work
together
because
they're
from
the
same
University
they
were
able
to
work
in
person
somewhat.
They
used.
Maybe
what
might
be
complementary
approaches,
but
you
know
we
have
to
figure
out
how
to
make
this
into
a
powerful
platform
with
the
two
different
approaches.
A
You
know
perhaps
one
approach
Works
in
some
cases
better
than
others,
perhaps
if
we
bring
new
organisms
into
it,
so
we
for
this
example.
We
used
Axolotl
data
from
the
Axolotl
embryo,
which
is
very
big
and
allows
you
to
see
other
transparency.
There's
transparency
in
that
embryo,
where
you
don't
have
that
in
other
embryos-
and
you
can
you
know
it's
a
good
model
to
work
from,
but
there
are
other
organisms
that
you
can
use
as
well,
and
that's
where
you
know
we
may
need
to.
A
Maybe
people
may
need
to
use
one
technique
over
another
to
get
a
satisfactory
result,
but
this
is
this
is
something
that's
going
to
be
released.
I
I,
don't
know
when
we're
gonna
do
that.
I
know
that
the
two
students
have
been
busy,
but
again
that's
that's
something
that's
maybe
for
next
year
and
we
went
we're
on
the
virtual
conference
circuit.
So
we
did
a
number
of
different
talks
and
different
visited
different
different
conferences
to
get
the
word
out
about
evolum
and
what
we're
doing
so.
A
We
were
at
netsci
and
network
Neuroscience,
which
was
a
satellite
session
of
netside,
and
this
was
in
July
22,
and
this
was
supposed
to
be
held
in
Shanghai,
but
they
had
to
hold
it
virtually.
But
you
got
to
see
a
lot
of
the
work
from
some
of
the
Chinese
Network
scientists,
so
that
was
an
interesting
time.
They
had
a
lot
of
interesting
work.
In
our
case,
we
presented
in
the
main
session
hypographs
demonstrated
anastomoses
during
Divergent
integration,
and
we
talked
about
this
talk
in
some
of
the
previous
meetings.
A
This
is
where
you
have
a
developing
embryo
and
you
can
extract
networks
out
of
that
embryo
and
then,
as
the
tissues,
differentiate
and
form
distinct
tissues
and
organs.
These
networks
then
diverge,
but
they
still
remain
connected
through
these
anastomoses
and
it's
kind
of
a
loose
anatomical
analogy,
but
sometimes
there
are
actual
anastomoses
like
you
might
find
in
the
heart,
but
sometimes
they're
just
linkages
between
the
networks
functionally.
A
So
but
in
any
case,
this
paper
talked
about
hypergraphs,
which
are
these
graphs,
where
each
like
hyper
node
contains
a
bunch
of
individual
nodes
and
they
can
behave
independently.
But
you
have
this
meta
representation
where
you
have
these
nodes
that
are
connected
to
one
another
and
so,
and
you
can
do
a
lot
of
interesting
things
with
hypographs,
you
can
extract
power
Spectra,
you
can
do
other
things
that
you
can't
do
with
regular
Networks.
So
that's
an
interesting
tool
to
bring
to
bear
on
this
problem,
and
so
that
was
just
a
presentation
on
that.
A
We
also
attended
the
incf
assembly,
which
was
a
talk
from
some
of
the
work
I'm
doing
with
another
group
on
in
developmental,
embodied
neurosimulation,
and
this
involves
some
work
with
breitenberg
vehicles,
but
more
about
sort
of
letting
the
nervous
systems
develop
in
different
ways
in
which
they
might
develop
an
agents
and
so
that
that
was
a
poster
that
was
presented
there.
Finally,
the
cyan
workshop
on
network
science-
this
was
held
in
September,
and
this
followed
up
on
what
was
going
on
and
that's
I.
This
is
a
different
audience.
A
C
B
A
Mathematical,
like
I,
said,
but
in
this
case
the
focus
was
on
embodied
hypergraphs,
which
were
these
kind
of
hypergraphs
that
exist
in
the
like
that
represent
the
anatomy
from
anterior
to
posterior
end.
So
you
have
like
the
networks,
has
a
polarity
and
it
has
a
structure
that
mimics
some
of
the
features
of
the
anatomy,
so
anterior
posterior,
left,
right
and
so
forth,
and
so
that's
important
for
information
processing
within
the
sort
of
the
anatomy.
You
know
where,
where
do
you
say,
get
sensory
information?
A
A
So
this
is
just
three
examples:
we'll
plan
to
do
more
conferences
in
2023.,
then
we've
continued
with
our
weekly
meetings.
So
these
are
our
weekly
meetings
right
now,
but
usually
these
are
Mondays
at
10,
A.M
eastern
time
in
North
America.
So
if
you're
in
North
America,
you
can
get
the
meetings
in
the
morning
if
you're
on
the
west
coast
it's
early
morning
and
then,
if
you're
in
Europe
it's
afternoon
late
afternoon
in
India
it's
evening
and
in
East
Asia,
it's
you
know
late
at
night,
but
we
try
to
make
it
accessible.
A
So
this
is
an
example.
You
know
of
a
slack
message
calling
people
together
for
the
meeting.
We
have
topical
reviews
on
different
topics
like
we
just
talked
about
tensegrity,
but
we
also
have
you
know
we're
bringing
papers
to
Bear
a
lot
of
times
and
go
through
them
on
a
number
of
topics:
morphogenesis
embryophysics
approaches
to
Imaging
systems,
biology,
developmental
biology.
A
Then
we
have
biology
and
computation
updates.
So
these
are
updates
on
people's
projects
a
lot
of
times
during
g-soc
will
have
people
give
weekly
updates
on
their
projects,
but
these
can
be
demos
or
things
about
collaboration
management
which
we
haven't
had
in
a
while,
but
we
do
that
and
then
papers
and
presentations.
So
people
want
to
give
a
presentation
like
we
had
a
couple
weeks
ago.
A
The
floor
is
open
for
that
and
then
finally,
the
tutorials
that
we
usually
do
to
share
information
about
different
things
like
we've,
had
tutorials
on
Jupiter
and
collab,
notebook
use,
model,
building
and
other
technical
topics.
So
you
know
again,
those
are
things
that
afford
learning
in
the
meetings.
A
He'll
afford
people
to
do
work
on
their
own
and
you
know
really
give
give
people
an
idea
of
what's
going
on
under
the
hood
of
some
of
these
talks
and
papers,
and
things
like
that,
then
we
have
this
Innovative
new
theme,
which
is
cognition
and
psychophysics
and
cells
embryogenesis,
and
this
comes
from
two
papers
that
are
sort
of
ongoing.
This
one
is
going
to
be
published
in
the
book,
mathematics
and
biology
of
diatoms.
This
is
a
paper
about
the
psychophysical
world
of
the
motile
diatom
bacillary
paradoxa.
B
A
This
kind
of
a
review
of
different
techniques
from
what
we
call
the
psychophysical
field,
where
they're
looking
at
sensory
inputs-
and
you
know
response
times
or
other
types
of
things
that
organisms
do
in
response
to
sensory
stimuli.
And
so
you
know
it's
a
little
bit
hard
to
do.
You
know
you
can
do
experiments
in
small
organisms.
You
can
do
experiments
in
single
organisms,
sometimes
without
a
brain,
but
to
have
like
a
collection
of
things
that
you
might
be
able
to
how
you
might
be
able
to
interpret
some
of
those
things.
A
And
so
there
are
a
number
of
techniques
that
are
listed
in
the
paper
that
you
know
you
might
revisit
or
be
Revisited
to
interpret
some
of
these
things,
and
so
we
have
a
single
colony
of
single
cell
organisms.
Here
there
are
a
number
of
different
technique:
modeling
techniques
by
which
you
can
better
understand
some
of
these
responses
to
stimuli
and
and
other
things.
So
this
is
one
example,
and
then
the
latest
example
is
this
embodied
cognitomorphogenesis
as
a
route
to
intelligence
systems.
This
is
a
paper
where
you're
taking.
A
This
is
a
for
a
special
issue
of
we're
all
Society
interface
focus
on
symmetries
and
life
and
mind
or
in
mind
in
life.
So
the
journal
was
a
Royal
Society,
interface,
focus
and
there's
a
special
issue,
and
the
idea
here
is
to
take
some
of
these
Concepts
that
we
talk
about
in
the
meeting
and
talk
about
them
as
sort
of
this
way
to
look
at
information
processing
in
morphogenesis,
but
also
the
role
of
the
body
itself.
A
So
the
body
of
the
organism
is
it's
developing,
plays
a
key
role
in
some
of
this,
and
so
we're
using
things
like
differentiation
trees.
This
is
an
example
from
sea
sports
that
was
done
in
another
paper
that
we
did,
and
it
just
shows
the
Symmetry
and
fluctuating
asymmetry
and
symmetry
breaking
events
in
that
embryo.
This
is
a
tensegrity
structure.
A
These
two
things
are
tensegrity
structures
and
we're
making
the
point
that
you
can
use
to
Integrity
to
sort
of
understand
the
the
body
of
these
organisms,
so
they
have
to
have
a
rigid
body
or
something
that's
structurally
sound,
and
so
you
know
there's
that
aspect
of
it
and
then
we
also
use
the
embodied
typographs
as
well
and
I.
Don't
have
a
picture
of
those
here,
but.
C
A
A
A
A
So
anyways,
that's
that's
nice!
There
it's
a
kind
of
a
new
theme,
it's
emerging
here,
and
this
is
something
that
we
may
bring
our
tools
to
Bear,
both
the
Imaging
to
or
the
analytical
tools
and
theoretical
tools,
and
then
finally,
I'd
like
to
thank
our
contributors
for
this
year.
I
think
I
have
a
complete
list.
It's
kind
of
hard
to
find
it
remember
everyone
who's
like
participated
in
the
group,
but
this
is.
We
have
a
pretty
decent
list
this
year,
and
so
that's
all
I
have
for
that.
A
A
B
A
I
could
be
yeah,
yeah,
I
know
what
Susan's
talking
about
the
book
that
she
shared
a
couple
weeks
ago.
I
went
through
it
and
they
actually
have
some
Matlab
code
for
like
the
stability
modes,
I'm,
not
sure
if
it's
something
that
is,
it
can
really
make
a
lot
of
sense
of,
though,
like
you
run
the
code,
and
it
gives
you
like,
a
number
I
would
have
to
would
have
to
do
some
more
discussion
about
how
to
do
that.
But
I
think
it
might.
C
B
I
haven't
gotten
in
enough
into
that
linear,
algebra
I
need
the
paper.
I
need
my
my
structure
to
work
and
I
need
to
do
some
digital
experiments
on
it.
Okay,
I
need
to
write
up
a
thing:
that's
6
000
words
long
hand
it
in,
and
hopefully
that
will
be
my
candidacy
project
and
then
I
can
do
some
of
these
other
things,
which
are
more
important
other
than
other
console
to
see.
If,
if.
B
B
C
C
Yeah
I,
don't
know
if
I
told
you
the
the
the
funny
situation.
I
was
that
I
attended
a
night
course
on
linear
algebra.
It's
in
Washington,
DC
and
I
was
the
only
student
who
asked
many
questions,
and
then
we
had
a
party
when
this
course
was
over
and
I
asked
the
instructor.
Why
I
was
the
only
one
who
asked
questions
he
said
well,
it
was
a
remedial
course
for
kids
trying
to
get
into
mathematics.
C
A
A
This
is
Joe
grodstein
and
Mike
11
closing
the
loop
on
morphogenesis,
the
mathematical
model
of
morphogenesis
by
closed-loop
reaction,
diffusion
and
in
in
their
in
the
paper.
They
talk
about
these
waves
that
are
very
similar
to
differentiation,
waves
and
I.
Don't
know
how
similar
dissimilar
they
are,
but
we
can
go
through
the
abstract
and
see
where
they're
going
with
this.
So
the
abstract,
yeah
morphogenesis,
the
establishment
or
a
pair
of
emerging,
complex
Anatomy
by
groups
of
cells.
That
was
their
definition.
Morphogenesis
is
a
fascinating
and
biomedically
relevant
problem.
A
One
of
its
Most
Fascinating
aspects
is
that
a
developing
embryo
can
reliably
recover
from
disturbances
such
as
splitting
into
twins.
So
this
is
more
like
focused
on
like
regeneration,
I
guess
I
mean
you
know
it's
it's
kind
of
not
I,
don't
know.
If
that's
like
the
norm
of
like,
if
you
had
an
embryo
I
mean
you
could
do
experiments
with
it
and
show
this,
but.
A
C
A
Wow
well,
this
reliably
implies
some
type
of
goal:
seeking
error
minimization
over
a
morphogenetic
field.
So
this
is
the
idea
that
you
know
you
have
this
error,
minimization
process.
You
want
to
minimize
errors
and
there's
some
goal
and
there's
this
morphogenetic
field.
I
think
we've
talked
about
these
with
Tom
porges,
and
this
was
like
one
of
the
things
that
we
discussed
in
the
context
of
more
more
of
a
morphinosis
or
morphinostic
one
of
the
things
he
was
working
on
where
he
was
calling
the
morphogenetic
fields,
and
he
didn't
like
that.
A
So
there
are
many
gaps
with
respect
to
detailed,
constructive
models
of
such
a
process
being
used
to
implement
the
collective
intelligence
of
cellular
swarm.
So
this
is
now
you're
moving
from
these
sort
of
embryo
experiments
and
regeneration
to
cellular
swarms
and
collective
intelligence.
So
this
is
like
the
cells,
basically
in
an
embryo,
or
have
some
sort
of
collective
intelligence.
We
know
that
it's
not
necessarily
intelligence,
but
it's
Collective.
Behavior
really
is
the
key.
Okay.
C
A
C
A
C
C
C
C
A
A
A
A
A
A
C
A
A
Specifically,
we
create
a
reaction,
diffusion
pattern
with
n
repetitions
where
n
is
easily
interchangeable.
Furthermore,
the
individual
repetitions
of
the
Rd
pattern
can
be
easily
stretched
or
shrunk
under
genetic
control
to
create
EG.
Some
morphological
features
larger
than
others,
so
this
is
like
size
and
shape
control.
So
this.
C
B
A
Then
this
computation
wave,
which
they'll
Define
later
scans,
the
morphogen
pattern
unidirectionally
to
characterize
the
features
that
the
negative
feedback
thing
controls.
So
you
have
the
cellular
or
automaton
it's
using
this
computation
wave,
which
I
guess
goes
over
the
the
surface.
It
stands
the
pattern
and
characterizes
the
features
and
the
negative
feedback
uses
that
to
control
the
future
iterations
of
it
so
I
guess
it
corrects
for
things.
So
it's
basically
like
there's
this
I
guess
this
field,
and
you
have
to
remember
that
Michael
11
works
on
flat
ones,
which
are
these.
You
know,
organisms.
B
A
C
C
A
C
A
A
C
A
Ahead
so
let's
see
if
they
they
talk
about
some
of
this
in
more
detail
in
the
paper,
they
give
some
mathematical
formulations.
They
attack
the
butt
in
terms
of
reaction,
diffusion,
positional,
information
and
scaling.
So
how
can
collections
of
cells
cooperate
to
reliably
produce
the
same
species-specific,
Target
morphology?
A
And
then
they
talk
about
planarian,
flatworms,
of
course,
which
is
the
example
I
just
talked
about.
Amphibian
embryos
maintain
the
right
proportions,
even
when
many
cells
are
missing
or
made
too
large.
So
this
is
homeostasis
homeostatic
properties,
and
so
that's
what
they're
trying
to
sort
of
understand
with
this
and
then
this
is
of
course
extended
to
this
idea
of
collective
intelligence
shown
as
how
groups
of
cells
competent
and
physiological
and
metabolic
spaces
and.
C
C
C
A
C
Yeah
well,
the
the
place
where
the
shadow
space
comes
in
is
that
the
global
can
change
it
to
local,
which,
in
the
case
of
differentiation
waves,
is
the
differentiation
stains.
The
cells,
okay,
okay,
now
I,
don't
know
if
that's
robust
or
not
yeah.
That's
that's
one
question
we'll
have
to
answer
yeah
yeah.
You
know
because
it
is
a
positive
feedback
system
with
some
negative
feedbacks
locally
between
differentiation
waves
and
the
tissues
that
go
through.
C
So
the
white
kind
of
ask
that
question:
what
is
the
stability
of
a
differentiation
tree
great,
the
more
branches
it's
got?
It
would
seem
to
be
less
stable,
but
is
there
any
evidence
for
that
all
right
for
all
the
negative
feedbacks
in
many
different
stages
and
at
many
different
scales,
I,
don't
know
yeah.
A
A
A
B
A
So
they
talk
about
it
here.
It
achieves
this
goal
with
inclusive
negative
feedback.
Controller
of
that
number
one
employs
a
cellular,
automated
account,
Peaks,
so
peaks
of
like
activity
or
something-
and
let's
count
the
current
number
of
pattern
repetitions.
It
uses
computation,
wave
fronts,
and
so
this
is
a
powerful
Concept
in
cellular
Computing.
So
this
is
actually
something
people
I
think
have
come
up
with
in
this
field
of
cellular
Computing
that
takes
advantage
of
a
existing
asymmetry.
B
B
C
C
A
B
A
A
So
the
first
paper
is
this
called
byocidal
skeletal
web,
and
this
is
kind
of
an
overview
paper
in
nature.
Physics,
as
is
in
physics
of
the
cell
and
the
preview
reads:
A
biomolecular
condensates
from
busy
cellular
environments.
Statistical
image,
analysis
of
heterogeneous
structures
now
enables
quantification
of
macromolecular
interactions
between
condensates
and
cytoskeletal
filaments.
So
we
were
talking
earlier
about
tensegrity
and
we're
talking
about
cells
being
the
nodes
in
those
networks
and
filaments
being
the
edges
or
the
rods.
Sometimes
they
call
them
in
tensegrity
and.
B
A
So
the
paper
starts.
It
is
becoming
increasingly
apparent
that
living
cells
rely
on
the
dynamic
and
self-organized
compartmentalization
of
specific,
specific
molecular
components,
the
formation
of
regions
and
cells
with
distinct
biochemical
composition,
where
striking
resemblance
to
the
partitioning
of
molecular
species
and
phase
separation
systems,
and
this
is
where
you
may
have
like
two
liquids
that
are
of
different
densities
and
they
separate
out
so
that
you
can
see
bands
in
them
as
they
approach
equilibrium.
So
these
regions
of
cells
will
form
these
condensates
these
little
masses
and
they're.
A
Basically,
it's
phase
separation
because
they
have
different
regions
of
Bio
biochemical
specialization
in
Cellular
Systems.
However,
the
condensation
of
biomolecules
must
occur
in
a
multi
component
environment
that
is
not
an
equilibrium,
so
in
cells
of
course
not
a
lot
is
an
equilibrium.
We
can't
really
assume
that
thus
understanding
the
mutual
interactions
between
biomolecular
condensates
and
the
diverse
cellular
structures
that
surround
them
is
crucial
for
both
their
quantitative
understanding
of
Cell
Biology
and
our
appreciation
of
non-equilibrium
intracellular.
A
Jackalope
stress
granules,
which
are
biomolecular
condensates,
so
they
have
a
picture
here.
This
is
figure
one.
This
is
where
you
have
a
cell
with
nucleus
and
in
the
cytoplasm
you
have
these
microtubules,
which
actually
are
you
know,
enveloping
a
lot
of
the
cytoplasm.
They
keep
the
structure
of
the
cytoplasm
and
the
parts
of
the
cell
that
aren't
the
nucleus
and
they
actually
have
to
work
in
the
nucleus
as
well,
but
they
basically
are
home
to
these
stress
granules.
So
these
stress
granules
are
with
it
been
embedded
within
this
microtubule
Network.
A
Then
they
actually
take
images
of
cells
and
they
take
high
resolution
images
where
they
can
get
these
condenses
with
their
microtubules
associations,
and
they
take
a
number
of
images
they
average
over
them,
and
this
looks
like
a
combination
of
images
where
they
overlay
everything
and
they
get
a
network
with
this
condensate.
In
the
middle,
and
then
they
actually
analyze
this
further,
they
zoom
in
and
they
play
the
microtubules
connected
to
the
stress,
granule
and
they're.
These
individual
tubulin
subunits
I
kind
of
sit
outside
the
Grain
on
the
edge
of
the
granule.
A
So
that's
an
interesting
thing,
and
not
necessarily
with
respect
to
tensegrity,
but
definitely
with
respect
to
what's
going
on
in
the
cell,
and
certainly
is
these
networks
reconfigure.
We
can
say
things
about
the
stability
of
the
cells,
the
biochemistry
of
local
biochemistry
of
the
cells
and
some
of
these
Global
local
interactions.
A
So
that's
an
interesting
a
first
paper.
The
second
paper
I
want
to
talk
about
here
is
something
we've
talked
about
a
couple
times
in
different
meetings,
and
this
is
this
curvature
in
in
developing
tissues.
So
this
is
an
epithelial
cells
and
a
sheet
where
epithelial
cells
adapt
to
curvature
induction
by
a
transient,
active
osmotic
swelling.
So
you
can
see
from
the
graphical
abstract
here
you
have
this:
this
sheet
of
cells
actin
the
epithelial
layer
and
this
bilayer.
A
A
Sheet
now
looks
like
this:
you
have
a
new
tissue
equilibrium
in
this
curve.
Shapes
of
this
flat
sheet
is
in
equilibrium,
and
is
this
rolling
process
occurs?
There's
an
instability
that
is
this
rolling
kind
of
reaches
its
conclusion
by
reaching
another
part
of
the
sheet,
then
it
goes
back
into
equilibrium,
and
you
can
see
that
there's
this
profile
here
in
time
where
the
actin,
the
membrane
tension
and
the
cell
volume
all
change.
The
cell
volume
goes
up,
the
actin
goes
down
and
the
membrane
tension
goes
down
soil
and
then
recover.
A
They
all
recover
to
the
pre-perturbation
point
when
they
were
in
equilibrium
in
this
flat
sheet.
So
it's
an
interesting
way
to
look
at
this
process.
This
isn't
in
brief.
They
examine
the
response
of
epithelial
cells
to
rapid
changes
in
curvature.
So
this
is
a
rapid
process.
It
takes
about
maybe
13
or
14
minutes
to
have
this
entire
process
complete.
So
it's
not
very
long.
They
show
that,
upon
curvature,
induction
cell
volume,
transiently
increases,
which
means
it
increases
briefly
but
not
permanently,
while
membrane
tension
decreases
and
actin
depolymerizes.
A
A
B
A
Summary
reads:
generation
of
tissue
curvature
is
essential
to
morphogenesis,
however,
how
cells
adapt
to
changing
curvature
is
still
unknown
because
tools,
the
dynamic
weight
control
curvature
into
each
room
are
lacking.
So
here
they
developed
self-rolling
substrates.
To
look
at
these
changes.
There's
a
isotropic
change
of
curvature,
which
means
it
occurs
not
evenly
across
that
sheet.
We
show
that
Primary
Response
in
an
active
and
transient
osmotic
swelling
of
cells.
So
there's
a
swelling
of
cells.
A
The
cell
volume
increases,
the
cell
volume
increase
is
not
observed
on
inducible,
wrinkled,
substrates
or
concave
in
convex
regions
alternate
each
other
over
short
distances.
So
where
you
have
an
inducible,
wrinkled
substrate,
you
don't
observe
this
volume
increase
only
in
these
flat
sheets
and
this
finding
identifies
swelling
as
a
collective
response
that
changes
a
curvature
with
a
persistent
sign
over
large
distances.
A
So
we
have
this
ability
to
have
these
different
aspects
of
the
cell
sheet
of
individual
cells
and
across
individual
cells,
regulated
in
a
Time
sequence,
and
this
contributes
to
sort
of
going
from
a
stable
state
to
an
unstable
State
and
then
back
to
a
stable
state.
You
can
see
here
that
you
have
this
curvature
here.
Let
me
prepare
this
hop
to
make
this
preparation
and
then
they
show
the
curvature
other
microscopy,
and
you
know
so
they
have.
This
is
a
nice
paper
if
you're
interested
in
this
topic
an
interesting
set
of
experiments
there.
A
This
paper
is
talks
about
nickel
location
like
model
of
directed
cell
migration,
so
we
talked
about
cells,
curving
and
acting
collectively.
We've
talked
about
condensates
in
the
formation
of
networks.
Now
we
can
talk
about
an
echolocation
like
model
of
directed
cell
migration
within
growing
tissues.
So
what
is
this
all
about?
A
This
is
so
during
development
and
regeneration
cells
migrate
to
specific
locations
with
ingrown
tissues.
These
cells
can
respond
to
both
biochemical
signals
and
mechanical
cues,
resulting
in
directed
migration.
So
there's
these
cues
in
the
environment,
it's
responding
to
them.
It's
moving
towards
them
using
different
types
of
taxes
and
other
types
of
very
simple
responses.
A
A
Yeah,
how
cells
respond
to
migratory
signals
in
a
robust
manner
within
a
growing
domain
remains
an
open
problem.
Here
we
propose
a
model
of
directed
migration
and
growing
tissues
motivated
by
echolocation
migrating
cells
generate
a
signaling
gradient
that
induces
a
response
signal
from
the
moving
system
boundary.
A
A
Finally,
we
discussed
the
relevance
of
such
a
model
to
fibroblast
migration
and
location
within
the
developing
zebrafish
autofit,
so
they
use
an
animal
model
to
look
at
the
fibroblast
migration,
which
may
be
regulated
by
opposing
signal
gradients
of
slit,
Robo
pathway,
components
so
they're
using
molecular
gradients
to
sort
of
guide
these
processes.
So
you
have
sort
of
a
response
to
a
stimulus,
and
you
also
have
this
molecular
gradient,
which
serves
as
a
constraint,
and
so
this
is
a
this
is
a
mechanism
that
can
align
cells
in
different
positions
stably.
A
So
we
don't
have
to
you
know
we
don't
have
the
sort
of
random
process
that
doesn't
really
lead
to
a
stable
phenotype.
So
this
is
an
interesting.
This
is
okay.
So,
let's
see
if
we
find
some
graphs
here,
this
is
the
Echolocation
model
for
cell
migration,
so
these
echolocation
in
quotes,
but
basically
you
have
the
source
and
you
have
a
response.
So
the
response
goes
in
this
direction
here
and
the
source
is
here
so
there's
a
source
and
it
responds
by
the
cell.
A
So,
in
a
illustration
of
the
model
of
single
cell
migrating
in
one
dimension,
a
cell
migration
or
migrates
in
the
system
of
length,
l
ope
that
grows
rate,
feed,
subdom
and
so
there's
a
rate,
there's
a
characteristic
length
that
it
generates
when
it's
triggered
by
stimulus,
the
cell
generates
a
source
signal
SXT
with
that
elicits
a
response
signal
rxt
from
the
system
boundary
at
x,
equals
LT
proportion
to
sltt
to
sell
speed.
V
sub
cell
is
a
function
of
the
response
signal
and
it
detects
at
RX,
sub
c
t
cell
migration.
A
Speed
is
modeled
with
the
phenomenological
function
here,
where
y
equals
some
of
the
components
from
the
first
part,
so
they're
just
basically
showing
that
there's
this
response.
It
can
be
characterized
mathematically
and
that
the
response
curve
here
there's
a
source
and
over
time
and
then
a
response
over
time.
Foreign.
A
Mathematical
modeling,
and
then
they
say
the
results
that
biphasic
response
of
cell
velocity
to
signal
laws
for
boundary
detection.
So
there's
this
cell
velocity
that
results
from
a
signal
and
it
can
detect
there's
a
biphasic
response
that
can
be
involved
in
boundary
detection.
So
cells
need
to
reach
a
final
position.
They
can't
just
migrate
forever,
and
this
is
one
of
the
things
that
helps
them.
Do
that,
as
the
cell
approaches
the
final
position
or
the
boundary,
our
XC,
they
Define.
This
mathematically
is
this:
gradually
this
boundary
gradually
increases.
A
A
Okay,
they
also,
you
know
they
do
they
do
a
lot
of
modeling
in
this
paper.
So
this
is
a
lot
of
modeling
there's
some
observation,
but
you
have
this
basically
you're
trying
to
work
out
a
model
if
I
go
location,
echolocation
like
behavior
Can,
enable
cells
to
position
separately
within
a
domain
without
the
need
for
processes
such
as
contact
inhibition,
so
they're
trying
to
figure
out
sort
of
a
way
to
have
like
a
almost
like
a
sensory
domain
and
maybe
sensory
processing
without
implying
that
there
needs
to
be
an
intelligence.
A
A
lot
of
this
is
controlled
by
physics
and
physical
constraints.
So,
and
then
this
shows
actually,
this
is
mesenchymal
cell
migration
in
the
zebrafish
median
finfold.
So
this
is
where
you
have
the
finfold
developing
from
zero
hours
to
24
hours,
and
you
see
in
this
case
where
you
start
to
get
cell
migration
and
they
migrate
out
to
this
boundary.
And
so
you
can
see
how
they
arrange
themselves.
They
responded
with
signal
and
they
radiate
out
as
they
migrate
and
they
form
the
structure.
A
Okay,
the
next
paper
we're
going
to
talk
about
here
is
this
paper
3D
organization
of
cells
and
pseudostratified
epithelia,
and
so
this
is
again
another
paper
about
the
epithelium
and
how
cells
are
organized
and
stratified,
and
so
they
talk
about
this
in
three
dimensions,
which
is
often
missed
in
a
lot
of
the
biology.
We
look
at.
We
look
at
these
two
dimensional
images,
but
we
have
to
consider
the
third
dimension
so.
A
They
don't
act
dynamically
with
many
more
cells
invisible
at
the
surface.
Here
we
review
recently
developed
New
Perspective
on
epithelial
cell
organization,
so
this
is
some
a
review
of
this
seemingly
random
at
First
Sight,
the
cell
packing
configurations
along
the
entire
apical
basal
axis
all
fundamental
geometrical
relationships.
So
in
this
case
it
isn't
so
much
the
physics,
it's
the
geometry,
that's
sort
of
constraining
things,
this
geometry,
minimizes,
the
lateral
cell,
so
contact
energy
for
our
driven,
cross-sectional
cellular
area
variability.
A
A
The
complex
3D
cell,
neighbor
relationships
and
pseudostratified
epithelia.
That
emerge
thus
emerge
from
a
simple
physical
principle,
and
this
principle
is,
of
course,
energetic
constraint.
This
paves
the
way
for
the
development
of
data-driven
3D
simulation
Frameworks.
That
will
be
invaluable
in
the
simulation
about
the
filial
Dynamics
in
development
and
disease.
So.
A
If
you
can
develop
a
mathematical
model
or
a
characterization,
especially
in
physics
and
geometry,
you
can
develop
a
software
platform
that
can
simulate
these
things.
So
this
is
why
we're
interested
in
this
type
of
thing
again
they
talk
about
the
3D
cell
shapes
so
is
3D.
Segmentation
of
cells
has
become
Possible
only
very
recently,
3D
cell
shapes
has
long
have
been
depicted
as
prisms,
which
retain
the
same
size
and
neighbor
relationship
along
the
entire
apical
basal
axis.
So
as
we've
sort
of
where
we're
Imaging
three-dimensional
contexts.
A
Now,
in
the
past,
people
really
kind
of
thought
of
these
cell
shapes
as
prisms.
They
thought
of
them
as
uniform,
and
what
we're
finding
with
some
of
those
Imaging
is
that
that's
not
the
case
cells
improved
up
at
the
oil
monolayers
are
commonly
pictured
as
frusta
or
bottle
cells,
as
the
apical
and
basal
areas
must
differ.
A
A
difference
is
a
neighbor
Arrangements
between
the
apical
and
basal
side
points
to
the
neighbor
changes
along
the
apical
basal
axis
and
range
of
epithelia.
So
there
are
things
like
prismatoids
that
accommodate
the
neighbor
change
in
either
surface,
and
so
there
are
different
ways
that
these
things
can
vary
different
types
of
relationships.
A
They
come
up
with
a
term
scutoid
which
is
where
the.
If,
if
the
neighbor
relationships
change
somewhere
in
between
the
apical
and
basal
axis,
the
cell
shape
is
reminiscent
of
that
formed
by
Beetle,
scutum,
scotellum
and
wings,
which
are
these
scutoids
without
the
before,
with
up
to
14
neighbor
changes
along
the
apical
basal
axis
pseudostratified
epithelium
cells,
developing
in
the
Muslims
that
will
resemble
where
the
pancake
rock
formations
in
New
Zealand's
West
Coast.
So
they
have
these
different
names
that
they
derive
from
different
things
in
the
world.
A
These
are
these
punicoids.
These
are
so
you
can
see
them
in
a
sort
of
an
Imaging
context.
So,
let's
see
if
we
can
figure
this
out
here,
so
this
is
a
scotellum.
This
is
a
shape
with
this
Beetle.
This
is
punakaiki
in
New
Zealand.
So
these
are
these
punicoids
and
then
the
plutocoids
are
just
like
a
repetition
of
this
sort
of
arrangement
of
rocks
and
so
they're.
A
Using
this
as
an
analogy-
and
these
are
an
epithelial
cells-
so
you
see
they
look
kind
of
maybe
like
rocks,
but
they're
actually
epithelial
cells.
So
you
have
a
number
of
phenomena
here.
You
have
the
increase
in
decrease
of
Neighbors,
with
the
frequency
of
T1
Transitions
and
the
position
relative
to
the
nucleus
and
then
the
distance
from
the
apical
axis
and
the
cellular
area
variation.
You
can
see
that
as
well
and
so
there's
a
neighborhood
increase
in
a
neighbor
decrease
and
there's
variation
around
this
mean.
A
So
this
is,
there:
are
these
processes
where
the
two
vertices
that
share
an
edge,
merge
and
decompose
in
a
different
direction,
such
a
neighbor
relationships
change.
So
you
have
this
process
where
there
is
changing.
You
know
changes
in
neighbor
relationships,
the
cell
changes
shape,
and
there
are
these
very
complex
three-dimensional
shapes
so
that
there
is
a
lot
of
variation
over
over
the
course
of
the
development
of
the
epithelium.
A
This
is
another
figure.
These
are
phenomenological
laws
in
epithelial
cell
Arrangements.
So
in
a
we
have
the
mouse
and
we're
going
to
column,
lung
epithelium
and
we're
slicing
through
it
from
the
apical
to
the
basal
surface,
and
we
see
that
they
have
these
different
cross
sections
where
we
have
these
different
neighbor
Arrangements.
A
So
in
this
first
cross
section,
which
is
closer
to
the
apical
surface,
you
have
certain
I,
guess
our
neighborhoods,
and
then
you
have
this.
These
are
polygonal
abstractions
of
the
cells
and
then
you
have
these
different
T1
transitions.
As
you
move
towards
the
basal
surface,
so
this
cross
section
closer
to
the
basal
surface,
has
a
different
arrangement,
and
so
what
they
have
to
say
about
that
is
that
this
reveals
the
complex
shape
and
packing
structure
of
pokenoids,
even
over
short
distances.
A
Numerous
t1l
transitions
occur,
leading
to
vastly
different
cell
relations
cell
neighborhood
ships,
they
call
them,
and
so
this
is
definitely
you
know
a
nice
way
to
show
that
these
things
are
not
uniform,
that
there's
a
lot
of
heterogeneity
and
variation
across
single
cell
groups.
Even
so
this
is.
This
is
nice.
This
is
this
shows
kind
of
some
of
these
things
that
you
have
these
different
things
like
the
polygonal
distribution.
A
Okay
and
then
our
final
paper
here
is
the
adhesive
forces
Network,
and
this
goes
back
to
networks
again,
and
this
is
where
we
have
non-specific
adhesive
forces
between
filaments
and
membraneless
organelles,
and
so
this
is
the
paper
by
botiker
that
was
mentioned
in
the
first
paper.
So
this
is
also
nature
physics,
so
many
membraneless
organelles
are
liquid-like
domains
that
form
inside
the
active
viscolastic
environment
of
living
cells
through
phase
separation.
A
So
these
are
these
these
common
States
and
then
the
networks
of
filaments
to
investigate
the
potential
coupling
of
phase
separation
with
the
cytoskeleton
we
quantify
the
structural
correlations
of
membraneless
organelles,
or
these
stress
granules
and
a
cytoskeleton
filaments,
or
these
microtubules
any
human
derived
epithelial
cell
line.
We
find
that
microtubule
networks
are
substantially
denser
in
the
vicinity
of
stress
granules,
which
we
saw
in
that
image.
That
figure
in
the
first
paper
when
microtubules
are
depolymerized
the
subunits
localized
near
the
surface
of
the
stress
granules.
A
This
means
that
when
the
microtubules
Fall
Apart
the
subunits
localize,
and
then
we
interpret
these
data
using
a
thermodynamic
model
of
partitioning
of
particles
to
the
surface
and
bulk
of
the
droplets.
In
this
framework,
our
data
are
consistent
with
a
weak
Affinity
of
the
microtubule,
some
subunits
or
stress
granule
of
interfaces
as
microtubules
polymerize,
their
interface
interfacial
Affinity
increases
providing
sufficient
adhesion
to
deform
droplets
in
or
the
network,
so
there's
a
depolymerization
process
and
a
polymerization
process.
So
you
have
these
two
processes
working
to
shape
these
networks.
A
I
already
suggests
that
proteins
and
other
objects
in
the
cell
have
a
non-specific
affinity
for
droplet
interfaces
that
increases
with
the
contact
area
and
becomes
most
apparent
when
they
have
no
preference
for
the
interior
of
a
droplet
or
the
rest
of
the
cytoplasm.
We
validate
this
basic
physical
phenomena
in
vitro
through
the
interaction
simple
protein
RNA
condensates
with
microtubules,
so
I,
don't
know
they
have
some
pictures
here.
They
have
the
stress,
granules
and
the
surrounding
microtubule
Network.
You
see
the
microtubule
Network
and
that's
an
an
a
and
then
in
B.
A
A
You
know
you
get
this
response
when
stress
granules
are
in
the
area
versus
not
so
the
microtubule
network
is
always
reforming
a
network,
but
they
don't
necessarily
organize
around
anything.
Specifically,
the
stress
cranials,
being
one
exception
to
that.
So
this
is
kind
of
what
they're
showing
here
and
then
finally,
I
don't
know
if
there
are
any
other
images
we're
showing
here.