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From YouTube: DevoWorm #12: Aneural models, soft materials/spherical projections, topological morphogenesis
Description
Update on soft materials and spherical projections of the Axolotl embryo, models of aneural cognition (psychophysics) in diatoms, topological approaches to morphogenesis in development and evolution. Attendees: Susan Crawford-Young, Karan Lohaan, Harikrishna Pillai, ABD, Jiahang Li, Richard Gordon, and Bradly Alicea
B
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So
all
right,
like
I
accidentally,
ended
up
with
this
feature
like
if
I
click
on
a
particular
part
of
the
cell,
like
it
creates,
like
a
snapshot
of
this
part
of
that
part
zoomed
up.
So
I
think
that
it
can
be
useful
if,
like
if
someone
wants
to
study
two
different
parts
of
this
together,
so
the
snapshots
can
use
that's
what
what
that's?
What
I
wanted
to
show
you.
F
E
D
Yeah
yeah,
I
think
that
would
be
useful
and
then
also
like
if
people
want
to
do
like
distances
across
the
sphere-
and
you
know
if
they
find
two
features,
they
want
to
know
what
the
size
scale
is
between
them
and
the
size
scale
would
be
the
size
scale
of
the
embryo,
not
necessarily
the
sphere
so
yeah
I
mean
we
have
that
information
right.
Susan.
A
D
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A
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F
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F
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G
A
A
G
A
Oh
okay,
oh
well,
anyway,
she
made
a
whole
bunch
of
these
networks
and
the
red
are
pluripotent
ectoderm
cells
and
then
the
green
ones
are
on.
The
margin
are
multi-potent,
she
says
mesoderm,
so
that's
that
and
then
so.
The
yolk,
our
yolk
cells,
are
kind
of
they're
red
and
the
animal
portion
are
kind
of
the
the
green
here
and
anyway,
she
made
his
networks
and
then
studied
them.
A
D
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D
It
almost
looks
like
finite
element
modeling
in
some
ways.
I
don't
know
if
that's
what
they're
doing
like
or
that's
what
they're
inspired
by,
but
we
had
talked
about
doing
that
at
one
time
in
the
group
by
doing
finite
element
modeling,
which
is
where
you
break
the
surface
down
into
elements,
and
you
have
these
nodes
and
you
have
edges
and
then
you,
I
guess
you
play
around
with
the
elements
that
you
get
as
a
result
of
that
process,
so
you
segment
it
into
different
pieces
and
then
you
play
around
with
it.
H
G
B
B
A
H
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D
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Bye
going
to
repeat
what
I
said
last
week
about
the
project,
so,
if
you're
interested
in
the
different
gsoc
projects,
this
is
gnns
and
development
as
developmental
networks,
that's
22.1.
This
is
on
neurostars.
So
if
you
want
to
join
neurostars,
it's
a
discourse
server
that
incf
is
set
up
and
they
have.
We
have
the
description
here
and
we
have
some
conv
some
sort
of
conversation
about
this
project.
D
D
D
D
The
other
project
is
this
22.2,
which
is
digital
microsphere,
and
this
is
the
one
that
we
just
saw
a
demo
of
with
the
axolotl
embryo.
So
this
has
you
know
the
description,
references
that
you
need
to
become
familiar
with
and
then
a
couple
of
you
know
we
have
the
the
basic
data
that
we've
been
working
with,
but
they're.
D
You
know
we're
gonna
try
to
get
more
data
later,
so
this
hasn't
had
as
many
inquiries
but
still
looks
like
ari
christian
is
working
on
it
and
we
might
have
some
other
applicants
for
it
as
well.
The
deadline
for
the
proposals
is
the
17th,
so
this
is
something
that
I
think
it's
the
17th.
I
can't
remember
exactly
what
the
date
is,
but
it's
coming
up.
D
D
Otherwise,
that's
you
know.
I
I
think
I've
gone
over
the
in
previous
lab
meetings.
I've
gone
over
the
outline
of
what
what
to
expect-
and
it's
just
a
matter
of
you
know,
taking
your
own
perspective
on
it
and
running
with
it,
seeing
where
it
goes
so,
more
yeah
we'll
talk
about
that
more
maybe
next
week.
I
don't
really
have
anything
more
to
add,
but
I
just
wanted
to
keep
people
on
top
of
that.
D
A
A
D
A
D
D
So
this
is
paper
and
basil
area
and
none
what
we
call
non-neuronal
or
anal
a
neuronal,
or
sometimes
people
call
it
basal
cognition,
and
so
this
is
the
psychophysical
world
of
the
motile
diatom
basal
area,
paradoxa,
and
so
the
the
point
of
this
paper
was
to
sort
of
take.
Let
me
go
to
the
actual
image
of
these,
so
these
are
what
was
that.
D
These
are
the
so
diatoms
are
very
diverse,
and
I
don't
know
if
people
haven't
seen
this
presentation
before
you
know
they're
all
different
types
of
diatoms,
they're
algae.
They
have
a
silica
cell
wall.
So
they're
not
like
you
know,
like
c
elegans,
where
they
don't.
You
know
they
have
their
animals
and
they
have
a
cells
softer
cells.
D
So
you
know:
diatoms
live
in
aquatic
environments,
they
live
in
in
areas
near
the
shore
in
different
locations
in
the
world
and
there
are
different
types
of
diatoms.
So
it's
a
very
diverse
group
of
organisms.
This
one
is
the
basilaria
which
is
the
a
specific
type
of
diatom.
It's
this
long,
elongated
cell
type,
and
they
live
in
these
colonies
that
have
this
shape
and
they
kind
of
you
know
the
colonies
are
stuck
together
and
they
move
around.
So
they
have
movement
and
they
can.
D
You
know
they
can
change
their
shape
or
they
can
move
depending
on
the
environmental.
You
know
environmental
stimuli
that
they
encounter.
So
there
are
things
like
light.
There
are
things
like
water
chemistry.
There
are
other
types
of
things
that
happen
that
these
this
chain
of
cells
or
this
basilaria
colony,
responds
to
so
this
paper
then
talks
about
we
took.
D
But
in
this
paper
we're
talking
more
about
maybe
what
the
cells
are
doing
in
terms
of
internal
sort
of
model
of
what
their
environment
is.
You
know
how
to
communicate
with
other
cells
in
the
colony
and
other
things,
it's
pretty
hypothetical,
but
it's
based
on
a
lot
of
the
information
from
the
baccalaureate
literature,
and
it's
also
based
on
information
from
some
of
the
neuroscience
and
neuroscience
modeling
literature.
D
D
So
most
of
the
theories
of
diatom
movement
have
really
been
around
physics,
and
last
week
I
showed
this
paper
on
another
organism
on
a
ciliated
organism
that
behaves
collectively
based
on
its
physics.
So
you
know
there's
this
physical
aspect
to
it,
where
you
know
there
are
different
biophysics
going
on,
and
this
ciliated
organism
is
responding
to
it.
And
you
know
it's
it's.
D
It
looks
intelligent,
but
it's
you
know
it's
really
a
collective
biophysics
in
this
case,
where
we're
proposing
that
there's,
maybe
this
internal
world
of
the
the
basilaria
that
it's
actually
it
isn't
so
much
that
it's
not
physics,
it's
just
that
there's
some
information
processing
that
goes
on.
That's
happens,
aside
of
physics,
so
this
is
this
is
where
this
paper
goes.
Reviews
a
little
bit
about
basilarian
about
diatoms.
D
So
this
could
be
like
light
intensity,
water,
salinity
and
temperature.
So
they
have.
You
know
if
the
temperature
drops
or
if
I
think,
their
differences
and
like
oxygenation
and
salinity,
there's
changes
in
light
concentration
because
they
live
in
these
in
these
tidal
areas
where
they
get
exposure
to
white
in
different
dosages.
You
know
throughout
the
day
throughout
the
seasons,
so
they
have
to
respond
to
that
and
this
has
an
effect
on
their
reproduction
which
we'll
talk
about
in
this
paper.
D
But
you
know
there
are
a
lot
of
interesting
connections
there
with
what
they're.
You
know
the
stimuli
that
they're
experiencing
in
some
of
their
biology,
but
that's
not
really
what
we're
focused
on
here,
but
they
also
a
lot
of
them,
have
to
live
in
these
colonies
and
they
have
to
sort
of
communicate
with
neighboring
cells
and
that's
where
it
gets
interesting
because
they
do
this
sort
of
collective
behavior,
where
they're
coordinating
their
behavior
with
their
neighbors
they're.
Also
there's
a
lot
of
cell
to
cell
signaling,
which
isn't
really.
D
I
didn't
think
there
was
much
in
the
literature
on
that.
But
but
what's
interesting
is
that
these
extracellular
spaces,
if
you
look
here
at
these
cells,
that
you
can't
really
see
it
here,
but
along
the
edge
there's
this
extracellular
space-
and
this
is
where
a
lot
of
the
interactions
are
mediated
because
there's
this
sort
of
adhesive
area
so
there's
an
extracellular
space
that
acts
as
both
a
site
of
stigmargy,
which
is
what
we
might
call
collective
intelligence
and
like
using
different
mechanisms
in
that
area
to
coordinate
collective
behavior.
D
So
stigma
g
would
be
like
an
ant
colony
where
ants
lay
down
pheromone
and
other
ants
will
follow
the
pheromone
and
they'll
form
trails.
They
can
form
this
collectively
because
they
have
this
common
signal.
So
that's
what
stigma
g
is
and
these
extracellular
spaces
act.
You
know
maybe
act
to
coordinate
what
different
cells
are
doing
when
they're,
interacting
or
when
they're
next
to
one
another,
and
then
it
can
also
act
as
a
memory,
medium
and
so
memory
medium
can
be
both
like.
You
know
something
like
we
have
in
humans,
where
we
have
long-term
memory.
D
So
there
you
know
there
are
these
mechanisms
that
sort
of
exist
and
we
have
to
characterize
them,
and
you
know
we
can
maybe
link
them
to
some
of
these
models.
Of
of
what
we
might
call
basal
cognition,
so
you
know
just
just
you
know
just
ask
the
question:
maybe
is
this
some
sort
of
cognition
that
doesn't
exist
with
the
brain,
because
these
diatoms
don't
have
a
brain?
Obviously,
it's
just
some
algal
cells
that
are
living
in
association,
so
you
know
doesn't
mean
that
you
can't
have
something
that
resembles
cognition.
D
D
So
it
may
not
be
simple
physics
governing
this,
so
this
is
a
neural
cognition.
In
the
paper,
the
term
a
neural
cognition
is
used.
It
could
be
some
people
use
basal
cognition.
Some
people
use
non-neuronal
cognition.
It's
like
the
terminology,
isn't
you
know
very
tight
there,
but
it's
basically
the
same
thing.
So
there
are
a
lot
of
papers
on
this
binet.
Who
is
a
psychologist
from
the
19th
century.
D
Verwern
was
another
person,
I
think
a
biologist
from
around
the
turn
of
the
last
century.
They
first
proposed
a
psychological
psychophysiological
study
of
protists,
so
these
are
single
cell
organisms
and
you
know
they're,
saying
basically
that
they
have
some
sort
of
psychophysiology.
It's
kind
of
interesting
that
you
go
way
back
to
the
1800s.
When
you
know
people
were
just
kind
of
working
with
these
organisms.
D
You
know
not
really,
there's
not
really
a
strong
literature
on
it
and
the
first
thing
they
say
is
well
there's
some
sort
of
cognition
here:
okay,
so
that
that
maybe
says
that
there's
something
there,
but
it
also
may
say
that
you
know
maybe
people
were
projecting
their
own
thinking
onto
the
onto
the
single
cell
organism,
in
other
words,
they're
saying
that
looks
like
something
humans
do
so.
Let's
say
that
it's
the
you
know
it's
basically
some
sort
of
little
brain
inside
of
the
cell.
We
don't
know
so.
D
You
know
there
are
two
ways
to
look
at
that
either
they're
on
to
something
or
that
they're
anthropomorphic
projecting.
So
that
means
it's
b.
You
know
they're
projecting
human
intentions
on
cell
behavior,
and
so
you
know
there's
some
more
work
done
on
this
one
time
or
at
that
same
time
the
amoeba
was
thought
to
be
an
evolutionary
linchpin
that
connected
the
behaviors
of
single
cell
organisms
with
that
of
humans.
So
they
wanted
they
said
well,
we
can
use
amoeba,
which
are
these
single
cells,
to
tell
us
something
about
humans.
F
D
Know
the
best
parallel,
but
this
this
sort
of
work
continued
on
throughout
the
20th
century,
and
so
even
today
we
have
work.
People
are
doing
on
stentor,
which
is
a
small
cilia
and
paramecium,
which
is
another
single
cell
organism,
and
so
people
are
working
on.
You
know
these
are
recent
papers
that
have
been
published
on
this,
and
so
people
are
still
looking
at
this
problem
and
they're.
D
You
know
maybe
using
some
of
the
mechanisms
within
the
cell
to
make
these
claims
that
you
know
there's
some
sort
of
cognition,
and
so
you
know,
cognition
is
actually
very
broadly
defined
as
information
processing.
So
when
they
say
cognition,
they
mean
that
there's
some
information
processing
going
on,
and
you
say
well
of
course,
there's
information
processing
in
the
cell.
You
have
genes
and
you
have
gene
expression.
You
have
all
that
they're
talking
more
about
like
more
directly
related
to
behavior,
and
so
you
know
there
are
a
lot
of
ways
you
can
define
this.
D
D
So
in
these
examples
you
see
that
you
can
have
this
sort
of
behavior
both
outside
of
a
brain
and
also
in
collect
cell
collectives.
Where
those
you
know
those
those
cells
are
doing
things
collectively,
but
they're
also
doing
things
they're
learning
and
they're
exhibiting
learning.
That
looks
like
something
that
might
happen
in
the
brain.
So
it's
not
like
that
shape
memory
that
I
told
you
about
earlier,
where
you
push
into
a
surface
and
it
rebounds.
D
It's
more
like
you
know
it's
it's
learning
and
maybe
there's
a
habituation
period.
So
it
exhibits
some
of
these
aspects
of
learning
that
we
see
in
say,
like
there's,
there's
an
organism
called
the
sea
slug
that
people
use
as
a
model
organism
and
the
sea
slug.
You
can
do
different
things
to
its
gill
slits.
You
can
expose
them
to
water
in
different
ways
and
their
gill
slits
have
this
habituation
that
that
they
can
measure
and
it's
a
very
famous
biological
model
for
habituation.
So
you
know
people.
G
G
G
C
H
G
G
Well,
there's
a
presumption
that
diatoms
do
go
into
the
sediment
if
they
find
themselves
with
too
much
sunlight
to
reduce
the
amount
of
sunlight.
Now,
whether
basilaria
can
do
that
or
not-
I
don't
know
so.
There's
some
literature
on
that.
No
one
has,
as
far
as
I
know,
has
done
anything
with
a
periodic
stimulus
that
lights
going
on
and
off.
G
G
G
G
D
Yeah,
so
this
is
yeah,
so
this
is
like
an
overview
of
what
a
neural
cognition
is
or
or
basal
cognition.
The
reason
they
call
it
basal
cognition
is
because
the
thinking
is
is
that
you
know
cognition
or
that
information
processing
comes
from
like
sort
of
an
evolutionary,
you
know,
sort
of
a
precursor,
so
every
organism
that
has
cognition
there's
some
origin
point
where
you
know,
maybe
what
we
see
in
organisms
like
diatoms,
for
example,
or
single
cell
organisms
resembles
which
you'll
see
later
with
brains.
D
D
G
C
D
Okay,
go
ahead,
yeah,
okay,
so
yeah,
so
so
there
we
have
a
lot
of
literature
review
on
this
for
different
things.
That
are,
you
know
in
this
area,
so
we
actually
have
some
interesting
things
on
algae
and
diatoms
in
this
section,
where
we
talk
about
some
of
the
things
that
they
do,
you
know
like
dick
was
talking
about
with
responding
to
chemical
stimuli,
to
light,
there's
a
lot
more
literature
out
there
on
this.
D
G
G
If
you're
not
across
any
any
literature
on
small
or
single
cell
organisms,
responding
to
sound
or
shock
waves
or
somebody
tapping
on
the
microscope,
or
something
like
that,
yeah
I
have
not.
I
don't
think
we
know
that
single
diatoms
seem
to
reverse
direction
when
they
hit
something
and
I'm
wondering
if
they
actually
feel
that
they
hit
something
or
are
they
responding
to
a
buck.
Yeah.
A
D
A
D
Worms
actually
respond
to
like
vibrations,
so
people
have
done
experiments
with
vibrations
and
yeah
yeah,
so
nematodes
don't
have
ears,
but
they
have
mechano
sensors
and
they
have
different
types
of
touch
sensors
so
that
that's
what
they're
feeling
that
vibration
yeah?
Maybe
you
could
send
us
a
paper
on
that
yeah.
D
D
D
So
there's
you
know,
they'll
test
a
range
of
intensities
you'll
respond,
whether
you
solve
it
or
not,
and
then
they've
been
able
to
create
these
psychophysical
laws
that
basically
show
with
intensity.
What
human
response
is
to
that
to
that
intensity
of
light,
so
you'll
usually
get
some
sort
of
mathematical
function
that
results
from
this.
So.
I
D
So,
basically,
all
the
vertebrates
and
they've
been
able
to
work
out
that
it's
across
species
phenomena,
but
those
organisms
all
have
brains,
and
so,
when
you
don't
have
a
brain,
it's
a
little
bit
different.
You
know
it
may
be
a
little
bit
different
approach,
but
you
still
have
the
basics.
You
just
look
at
the
different
intensities.
D
They
have
to
make
predictions
about
the
world
based
on
their
sensory
inputs,
so
they're
taking
in
information
they're
predicting
the
next
course
of
action,
and
then
that's
that's
what
you
know,
that's
something
that
is
adaptive
in
some
way.
So
you
know
this
is
something
that
is
usually
thought
of
in
terms
of
a
brain,
but
organisms,
of
course
do
this,
because
organisms
are
anticipatory,
they
can
see
like
you
know
they
can
respond
to
things
in
the
environment.
D
And
so
the
question
is,
is
you
know?
How
would
you
measure
that?
And
how
would
you
you
know
what
what
kinds
of
maybe
lawful
relationships
are
there
and
then,
if
it's
not
a
brain,
you
know,
maybe
there's
still
something
going
on
there
with
the
organism's
physiology.
But
to
describe
it
in
this
way
gives
us
a
framework
for
looking
at
that
sort
of
anticipatory
behavior.
D
So
if
this
this
this
lead
cell
will
move
up
and
down,
then
these
cells
behind
it
will
move
up
and
down
and
they'll
have
this
sort
of
rhythm
that,
where
the
colony
is
moving
collectively
and
that's
a
collective
pattern
generator
now.
This
took
inspiration
from
what
they
call
central
pattern,
generators,
so
central
pattern.
Generators
are
things
that
generate
an
oscillatory
movement,
and
you
see
this
a
lot
in
like
insect
limbs.
D
So
when
insect
legs
are
moving
up
and
down,
there's
a
central
pattern
generator
in
the
in
the
notochord,
that's
moving,
and
it's
it's
allowing
it
to.
I
shouldn't,
say:
notochord,
it's
a
different
mechanism,
but
it's
it's.
It's
basically
a
bunch
of
neurons
that
are
firing
together
and
creating
this
oscillatory
output,
where
the
legs
are
moving
up
and
down,
and
so
they're
doing
this
rhythmically
so
that
they
stay
coordinated.
D
D
D
Some
social
insects
behaving
collectively.
You
could
use
this
sort
of
copg,
which
is
not
talked
about
in
this
paper,
but
you
could
use
the
copg
technique
to
look
at
those
and
don't
kind
of
look
at
how
those
you
know
how
like
rhythmic
behaviors
in
that
colony
be
operate
so
and
then,
of
course,
we
expect
that
the
copg
will
generate
lawful
responses,
things
that
we
can
like
characterize
mathematically
with
respect
to
maybe
some
of
the
fluctuations
in
the
stimulus
or
some
of
the
external
fluctuations
that
you
might
find.
D
If
you're
thinking
about
a
social
insect
colony,
you
know
you
might
have
availability
of
food
or
you
know,
maybe
some
you
know
stimulus
like
water
or
heat
or
something
like
that
and
it
might
have
an
effect.
So
this
is
something
that
is
kind
of
introducing
in
this
paper,
but
something
that
might
be
something
to
follow
up
on,
and
so
that's
all.
I
wanted
to
talk
about
with
respect
to
that
paper,
remind.
D
G
D
D
Yeah,
I'm
not
sure
how
yeah
I
think
I
remember
I
went
to
a
conference,
it
was
like
a
soft
robotics
conference
and
they
had
someone
who
worked
on
insects
and
they
used
to
do
these
crazy
experiments
where
they'd
pull
off
legs
and
they'd
see
how
the
how
the
insect
adapted
to
it.
So
they
pull
off
some
legs
and
then
you
know
and
they
were
able
to
adapt
to
it
pretty
well.
So
it's
interesting
how
they
can
do
that
it.
You
know,
but.
C
F
D
I
I
D
F
H
F
F
D
I
guess
in
complexity,
so
I
I'm
gonna,
go
through
these
three
things
and
we'll
talk
we'll
think
about
them,
maybe
going
on
so
we
talked
about
the
1d
ising
models,
for
example,
and
we
talked
about
morphogenesis
and
cellular
automata.
That
was
several
months
ago
now,
and
then
we
also
talked
about
we've
had
a
long-running
theme
on
patterning
and
skin
and
in
in
different
surfaces
and
development,
so
these
papers
kind
of
fit
into
that
area.
D
D
D
So
this
is
this
argument
here
is
that
you
have
these
morphological
forms
and
they
represent
topologies.
So
what
are
topologies?
And
so
the
abstract
reads.
The
solution
to
the
problem
of
evolution
has
proven
refractory
to
experimentation
so
how
fish
have
evolved
in
demand
phylogenetically,
for
example,
because
evolution
is
thought
to
be
due
to
random
mutations,
obviating
the
opportunity
to
test
such
a
mechanism,
leaving
only
correlations
and
associations.
It
should
not
show
causation.
D
So
this
is
where
they're
saying
you
know
that
in
evolution
we
typically
think
of
things
as
being
due
to
random
mutations,
or
maybe
totally
due
to
random
mutations,
and
there
have
been
people
in
something
called
the
extended
synthesis
who
have
said.
Well,
that's
not
really
the
only
mechanism
for
evolutionary
change.
You
have
things
like
you
know,
developmental
changes
that
mediate.
Some
of
the
mutational
changes.
You
have
niche
construction,
which
is
actually
a
feedback
between
the
environment
and
how
organisms
modify
the
environment
and
evolution.
D
So
you
know
we're
talking
about
natural
selection
here
and
we
talk
about
evolution.
So,
what's
selecting
upon
genes
is
it
you
know
just
random
mutations
and
then
there's
some.
You
know
natural
selection
on
those
mutations
is
that
all
there
is
or
other
is
there
more
to
it
than
that?
And
so
that's
that's
where
the
people
in
the
extended
synthesis
are
coming
from.
So
in
this
case,
torde
is
talking
about
some
of
the
morphological
features
which
is
kind
of
how
you
know.
D
D
So
ideally
there
would
be
some
comparable
process
that
could
be
exploited
in
order
to
deconvolute
evolution,
which
I
guess
is
this:
how
do
we
know
what's
contributing
to
it,
but
none
has
been
forthcoming
up
until
now
having
been
introduced
to
not
theory,
a
subheading
of
topology.
So
this
is
where
we're
getting
into
mathematical
topology,
and
so
I'm
not
going
to
get
deeply
into
that.
What
that
is,
it's
basically
the
study
of
surfaces
and
and
shapes,
but
not
theory,
is
interesting,
because
there
are
many
different
ways.
D
You
can
tie
a
knot
and
people
have
done
these
studies
or
they
look
at
knots
being
made
and
they
try
to
study
the
different.
You
know
it's.
It's
like
a
whole
fascinating
area
of
mathematics,
but
topology
concerns
itself
with
how
geometric
objects
maintain
themselves
under
continuous
deformations.
So
this
is
where
you
have
a
knot.
You
have
like
a
string
or
a
rope
and
you
tie
it
into
a
knot
and
that
knot
is
just
turning
the
that
rope
in
different
ways
and
like
making
some
sort
of
formation.
D
D
So
this
is
what
what
why
you
might
be
interested
in
not
theory
and
topology
when
talking
about
these
morphologies,
not
not
theories
of
studying
mathematical
knots
inspired
by
the
knots
occurring
in
everyday
life.
Now
it
occurred
to
us
that,
since
knots
tie
things
together,
they
are
similar
and
kind
to
the
cell
cell
signaling
mechanisms
that
tie
physiologic
traits
together
during
development,
culminating
in
physiology
tied
together
by
homeostasis.
D
So
that's
an
interesting
point
that
you
have
this:
these
different
systems
that
exist
and
they
kind
of
they're
developing
independently,
and
then
they
have
to
be
tied
together.
Somehow
so
you
know
you
have
these
different
cells
that
are
differentiating,
maybe
even
forming
collective.
We
think
about
it.
D
Maybe
is
sort
of
collectively
forming
tissues,
but
there's
also
a
physiology
there
that
they
have
to
function
together
in
a
single
organism,
and
so
when
you
have
these
different
systems
that
form
as
they
differentiate,
we
think
this
is
differentiation
or
that
they're
they're
being
pulled
apart,
but
actually
they
have
to
come
back
together
in
some
way.
So
now
they
have
to
come
back
together.
D
They
have
to
be
physiologically
sort
of
working
together
and
then
they
have
to
exhibit
this
term
this
property
of
homeostasis,
which
means
that
they
can
maintain
stability,
which
is
the
I
think,
the
most
challenging
thing
of
all.
You
know
you
look,
we
I
think
we
talked
a
couple
weeks
ago
about
homeostasis
and
you
know
different
types
of
system
configuration,
but
what's
really
interesting
is
how
these
things
kind
of
are
put
together
in
development,
how
they've,
how
they
emerge,
and
so
this
is
where
he's
going
with
this
paper.
D
So
so,
due
to
the
due
to
competition
between
prokaryotes
and
eukaryotes,
the
latter
became
began,
forming
multicellular
organisms
through
cell
cell
communications,
mediated
by
by
growth
factor,
growth
factor,
receptive
signaling
mechanisms.
So
it's
just
that
cells
are
signaling.
One
another
with
what
they
call
growth
factors,
such
cellular
pathways
for
growth
and
differentiation
during
embryologic
development
subsequently
became
the
homeostatic
basis
for
physiology.
D
So
this
is
where
you
get
this
sort
of
you
get
these
mechanisms
that
are
going
on
at
the
cell
cell
level
and
they're
building
to
this
homeostatic.
You
know
these
homeostatic
mechanisms
in
the
process
of
doing
so.
The
cells
involved
produce
extracellular,
matrices
that
stabilize
them
physically
forming
true
knots
that
literally
tie
together
these
cells
as
organisms
or.
I
D
So
basically
you
get
this
homeostatic
process
that
emerges
through
a
lot
of
this
cell
cell
communication
signaling,
you
get
growth
factor
exchange
between
cells
between
tissues,
and
this
maintains
this
sets
up
the
sort
of
relationship
for
homeostasis,
and
you
know
there
are
a
number
of
people
who
have
argued
that
you
know
what
one
of
the
things
that's
really
important
in
health
is
maintaining
homeostasis
or
the
ability
to
maintain
healthy
tissues.
Healthy
bodies
is
this
sort
of
homeostatic
ability,
and
so
you
know
it's
it's
kind
of
magical,
because
it's
like
what
do
you?
D
How
do
you?
You
know?
The
body,
for
example,
maintain?
You
know
humans
and
other
homeotherms
maintain
their
body
temperature,
but
it
isn't
like
a
given
that
that's
the
case.
I
mean
that
has
to
happen
through
a
number
of
complex
mechanisms.
Those
can
break
down.
In
some
cases
you
know
it's
with
environmental
stimuli
or
with
some
other
dysfunction.
D
So
you
know
this
is
something
that
is
maintained
in
development
continues
through
adulthood,
and
we
have
to
understand
how
that
worked,
or
else
you
know
complex
organisms.
You
know
they
had
to
evolve
this
ability
as
they
evolved,
or
else
they
couldn't
really
maintain
right.
We
couldn't
maintain
very
large
bodies
without
some
of
these
homeostatic
mechanisms.
D
So
this
is
one
example
here
of
formation
of
my
cells
from
lipids
in
water,
so
this
shows,
like
you,
have
a
simple
system
of
a
micelle
that
is
like
a
empty
cell.
It's
basically
a
lipid
bag,
and
you
can
end
up
with
a
chemical
homeostasis
here
as
it's
pushed
down
into
the
water
away
from
the
atmosphere.
D
G
Bradley
yeah
there's
a
one
small
area
made
there.
My
cell
is
not
a
vesicle,
but
my
cell
consists
of
the
charged
molecules
on
the
outside
and
the
hydrophobic
parts
are
inside,
but
the
micelle
has
no
essentially
no
water
inside
okay,
whereas
a
vesicle
is
a
layer
of
amphiphiles
with
water
outside
and
inside.
D
If
they
live
in
the
same
organism,
they
can
fend
off
threats
more
easily.
So
this
is
one
of
the
compartmental,
and
this
the
same
thing
happens
in
the
organism
that
you
know,
if
you
have
things
in
the
organism
that
are
not
working
together.
Having
them
work
together
is
a
better
strategy
for
fending
off
external
threats.
So,
if
x,
y
and
z
were
also
systems
that
interacted
in
different
ways,
they
formed
like
a
circuit,
or
you
know
something
else.
D
This
is
something
that
would
help
the
organism,
and
so
finally,
this
relationship
between
topologic
knots
and
cellular
revolution
on
the
left
is
a
schematic
for
not
so
simple
circles
that
can
then
form
complex
configuration,
but
then
can
be
unknotted
to
perform
as
circles.
So
you
can
have
these
knots
that
can
reconstitute
back
into
cells.
D
So
this
is
interesting,
goes
through
a
lot
of
things
here
in
terms
of
the
sort
of
the
emergence
of
these
different
things,
so
the
evolution
of
heart,
lung
and
kidney.
You
have
the
lung
here
that
shows,
and
you
know
it-
it
evolves
in
different
ways
in
different
organisms,
but
you
have
these
basic
components.
D
So
now
the
the
next
two
papers
I'm
going
to
go
over
a
little
bit
more
quickly
because
we
don't
have
a
lot
of
time
left
this.
This
is
lizard
skin
patterns
in
the
izing
model,
and
so
we
talked
about
this
several
months
ago
with
tom
portages,
and
this
is
this
1d
izing
model
that
we
talked
about,
and
this
is
a
paper
on
lizard
skin
patterns.
So
this
is
where
you
have
lizard
skin
has
a
lot
of
nice
patterns
on
it
and
they
form
through
this
sort
of
morphogenetic
process.
D
So
in
this
case
the
oscillated
lizard
exhibits
an
intricate
skin.
Color
pattern
made
of
monochromatic
black
and
green
skin
scales.
The
dynamics
of
color
flipping
are
known
to
be
well
modeled
by
a
stochastic
cellular
automaton.
So
there's
this
intricate
pattern
of
scales.
They
flip
their
colors
based
on
some
process.
The
stochastic
process,
which
is
basically
some,
is
as
simple
as
flipping
a
coin.
Flipping
a
coin
is
a
stochastic
process
and
it's
it's
a
random
process,
but
it's
that
process
of
generating
that
random
state,
which
is
the
the
idea
behind
stochasticity.
D
We
show
that
the
wait
time
probability,
distribution
of
the
pattern
corresponds
to
the
canonical
probability
distribution
of
the
ising
model
and
can
be
generated
by
dynamics
different
from
the
commonly
used
globber
model.
We
comment
on
the
skin
scale
patterns
generated
by
the
izing
model
on
the
triangular
lattice
and
the
low
temperature
limit,
so
they.
D
Talk
about,
let
me
see
if
they
have
any
images
here,
so
this
is
an
image
of
of
scales
here
on
the
on
this
organism,
and
so
this
is
the
dorsal
surface
which
is
the
back,
so
you
can
see
the
top
of
the
organism
or
the
back.
This
is
what
it
looks
like.
So
you
have
this
patterning
that
you
know
goes
from
29
weeks
to
162
weeks.
D
D
G
E
D
So
if
you
look
around
each
of
these
scales,
they
have
neighbors
and
the
neighbors
should
be
like
either
the
same
color
or
a
different
color,
and
so
the
idea
is
is
that
if
it's
the
same
color
it's
more
homogeneous,
if
it's
a
different
color,
the
model
is
more
heterogeneous
and
the
rules
of
the
system
may
or
may
not
tell
it
in
the
next
step
to
to
become
like
more
like
its
neighbor.
That's.
The
idea
is
that
you
know
this
sort
of
morphogenetic
process.
G
I
D
The
number
of
neighbors
of
same
color,
the
frequency
is
higher
earlier
and
in
the
biological
example,
it
takes
a
little
bit
longer
for
it
to
reach
its
maximum
frequency,
so
the
frequency
would
be
like
if
you
have
a
bunch
of
neighbors
how
many
of
those
are
of
the
same
color.
If
it's
0.2,
then
that
means
on
average
it's
like.
Maybe
the
2
out
of
10
or
you
know,
one
out
of
five
neighbors
is
the
same
color,
and
so
that's
that's
where
the
frequency
comes
in
so.
G
D
D
A
A
D
Yeah,
let
me
see
if
they
have
any
other
images
that
might
be
informative.
Well,
this
is
just
where
they
show
the
simulation
here.
So
this
is
where
they
show
the
hexagons,
but
this
is
the
stochastic
cellular
automata
and
then
they
show
the
single
flip.
Izing
actually
is
another
model
for
this,
so
they
don't
include
the
light
green
skills,
no,
no
yeah,
just
the
green
and.
D
C
G
D
So,
let's
see
if
there
any
other
images
here,
so
this
is
the
parameter
space
of
the
triangular
ising
model
and
polar
coordinates.
So
this
is
where
they
kind
of
go
through
some
of
these,
I'm
not
sure
what
these
polar
coordinates
are.
But,
like
you
have
the
black
state,
pure
green
state,
and
then
you
have
like
a
black
where
black
dominates
and
then
green
dominates,
and
then
you
have
the
shift
in
the
density
of
the
cells
from
one
to
the
other.
D
So
you
can
see
that,
like
the
green
dominate
green
dominates
and
then
black
starts
to
dominate
when
the
black
cell
or
the
black
scales
become
more
prevalent,
and
so
they
play
this.
They
place
this
out
on
a
radio
map
so
that
they
can
show
some
of
these,
and
this
is
something
I
think
with
respect
to
the
ising
model
and
the
temperature,
the
izing
model,
being
a
rough
approximation
of
a
physical
system,
so
that
that's
why
they
use
that
kind
of
an
analysis.
D
D
D
C
C
D
So
this
is
something
that
we
can
follow.
Maybe
someone
can
follow
up
on
it.
I
can
send
the
papers
out
and
we
can
look
at
it
more.
I
don't
know.
Maybe
tom
has
something
to
say
about
it
too,
since
he's
done
work
yeah,
the
final
paper
epigenetic
forest
and
flower
morphogenesis.
So
this
is
a
paper
on
flower,
more
food.
We
don't
talk
much
about
plants
in
this
group,
but
this
says
this
paper
studies,
the
epigenetic
process
that
leads
to
angiosperms
flower
architecture
or
flowering
plants.
So
these
are
flowering
plants.
D
D
D
Finally,
they
solve
this
using
a
genetic
algorithm,
so
they
simulate
it
using
a
kinetic
algorithm.
The
optimal
solution
found
by
the
algorithm
correctly
recovers.
The
flowers
architecture
is
observed
in
wild-type
flowers
and
recovered
another
theoretical
work,
and
then
this
the
method
is
directly
applicable
to
the
other.
D
So
this
is
a
picture
of
the
flowering.
This
is
an
angiosperm,
the
typical
anatomy
here,
and
then
this
is
a
directed
graph
that
represents
a
boolean
network.
This
is
the
net
the
gene
regulatory
network
that
they
use.
We've
talked
about
like
different
ways.
You
can
do
this.
This
is
just
the
way
they've
managed
to
do
this.
This
is
from
experimental
work.
So
there
are
a
lot
of
interactions
here
between
different
genes,
and
the
question
is
like
between
these
interactions.
D
What
are
the
outputs
that
we
can
model?
And
so
they
use
this
approach
called
epigenetic
forests,
which
is
based
on
the
epigenetic
landscape,
which
is
this
landscape?
Where
you
have
the
ball
and
it
falls
down
into
these
different
wells
and
that's
the
the
trajectory
of
the
phenotype,
so
we've
talked
about
this
before,
but
it's
basically
it's
similar
to
some
sort
of
like
energy,
landscape
or
fitness
landscape.
Where
you
have
these
paths
and
there's
actually
a
tree
underlying
this,
because
the
idea
is
that
you
want
to
characterize
different
branch
points
based
on
these.
D
D
So
that's
what
they're
doing
here
and
so
the
discrete
dynamical
system
is
shown
here.
They
don't
show
the
math,
but
they
show
like
that,
as
your
state
increases
your
state
changes
from
I
to
I
plus
one,
the
state
of
the
matrix
changes
here
and
you
get
this.
You
model
these
transitions
as
a
discrete
dynamical
system,
and
then
you
can
build
a
transition
graph
where
you
can
show
transitions
between
states
and
yeah.
D
I
D
D
D
D
So
the
orange
path,
together
with
the
yellow
nodes,
is
a
path.
A
chain
and
red
nodes
correspond
to
the
stable
states.
So
these
red
points
are
the
stable
states
and
they
built
upon
these
different
dependencies
coming
down,
and
then
this
orange
path
is
sort
of
puts
these
things
together.
I
guess
yeah.
So
it's
another
really
dense
paper,
but
I
can
send
this
out
to
people.
You
can
take
a
look
at
it
if
you're
interested
you
can
follow
up
on
it
later.
D
A
Everyone
should
have
attended
the
aps
physics
meeting,
though
in
the
biophysics.
It's
it's
about
what
you
talk
about
every
week.
Yes,
it's
amazing,
it
was
amazing
and
I'm
just
I
haven't
even
shown
you
even
a
drop
of
it
yet
so
reference,
and
these
are
the
people
she
was
working
with,
and
I
just
thought
it
was
fantastic.