►
Description
DevoWorm meeting: April 20, 2020. Attendees: Richard Gordon, Ujjwal Singh, Bradly Alicea, Vinay Varma, and Susan Crawford-Young
A
A
C
D
B
C
A
C
A
A
It
was
going
to
update
over
on
the
google
Summer
of
Code,
so
we
had,
we
went
through
open
were
the
open
room
foundation
for
our
applications
that
are
sponsoring
well
or
sponsoring
group.
And
then
our
organization
is
I,
am
C
F,
which
stands
for
the
International,
neuro
informatics
coordinating
foundation,
and
so
they
are,
you
know,
they're
those
intermediaries
between
Google
and
open
worm,
and
the
you
know
handle
a
lot
of
the
administrative
stuff.
A
So
thanks
to
them
and
we
had
C,
we
had
six
applicants
for
a
pre
trained
models
project
and
then
we
had
two
applicants
for
the
the
open,
D
boson
project,
and
we
can't
announce
the
winners
until
I
think
the
beginning
of
May.
So
we're
not
going
to
do
that
yet.
But
we
have
to
say
that
we
had
some
very
good
applicants
for
both
projects
and
it
was
very
hard,
a
very
tough
decision
to
make
as
to
who
to
pick.
A
Google
has
that's
in
the
fall,
so
it's
not
during
group
Summer
of
Code,
it's
the
fall
season
of
Docs,
so
as
more
documentation
oriented-
and
we
did
this
last
year
in
open
room
and
it
was
be
successful.
I
think
you
know
we
we
built
some
documentation
system
for
open
room.
Well,
I
should
say
the
the
person
who
got
the
award
did
and
and
I
comb
entered
someone
through
the
process.
So
now
we
have
this
great,
automated
documentation
system.
A
So
we
could
do
you
know
we
can
do
the
same
thing
in
just
an
evil
worm
I
have
to
still
work
on
the
application
part.
On
our
end,
but
once
that's
done
we
can,
you
know
invite
that
person.
It
wasn't
selected
for
the
opened
evil
cell
project
to
work
on
that
and
they
would
just
have
to
modify
their
application
accordingly.
So
hopefully
everyone
is,
you
know
everyone.
If
they
applied
to
them
to
the
G
sock
and
didn't
get
selected,
then
there
are
invited
to
come
into
the
group
and
and
participate
we've
had
a
couple.
A
F
A
A
So
it's
like
divorce,
it's
just
the
EM
surface
of
the
embryo,
and
so
you
can
that's
that's
like
the
ideal
way
that
you
know
we
might
visualize
the
back
surface
of
the
axolotl
embryo
and
it's
gonna
take
a
lot
of
work.
I
think
it's
gonna,
take
the
right
solution
to
do
it,
but
I
think
it
can
be
done.
I'm
optimistic,
so
they
put
something
in
the
chat
here.
A
This
is
something
that's
just
yes,
okay,
so
he's
working
on
a
paper
here,
it's
Richard
Gordon
and
Natalie
Gordon's
and
Natalie
Gore
his
wife
and
she
doesn't
come
to
the
meetings
but
she's
in
the
house
with
him
there,
let's
see
when
we
were
all
triangles
or
extraterrestrial
shaped
droplet
alig,
anole,
archaea,
origin
of
life,
okay,
so
this
is
in
preparation.
I'm
working
on
this
now,
let
me
know
if
you're
interested,
so
that
sounds
pretty
interesting.
When
we
were
all
triangles
I,
don't
know
if
I've
shown
you
the
stuff,
I
was
working
on
with
like.
A
Like
what
do
I
call
multicellular
systems
or
not,
but
that
might
actually
be
relevant
to
this
I,
don't
know
you
have
to
give
us
a
maybe
in
the
next
couple
weeks
enough
to
give
us
a
short
like
this
or
maybe
I'll,
give
the
discussion
on
that
on
multi
cell
systems,
because
they
basically
use
a
bunch
of
shapes
and
the
shapes
replicate
and
then
you're
able
to
see
like
you
know,
you
can
actually
use
mathematical
tools
to
see
sort
of,
like
other.
You
know
evaluate
whether
there
are
different
groups,
whether
there
are
different
modules.
A
So
you
have
a
lot
of
triangles
that
are
aligned
in
a
structure
and
you
use
a
line
segment
to
draw
through
the
edges
and
if
they
complete
an
Euler
circuit
than
they're
a
unified
system
or
module,
and
if
they
don't,
then
you
break
the
thing
into
two
modules
and
you
retest
it
and
it
you
know
it's
the
idea
that
you
want
to
be
able
to
have
like
exchange
between
membrane
cell
membranes,
to
get
things
in
and
out.
So
the
idea
is,
you
would
have
this
Euler
path.
A
That
would
tell
you
whether
it
was
a
you
know,
unified
structure
if
it
had
like
paths
between
only
only
one
path
between
each
membrane
and
they
would
you
know,
that's
that's
the
idea
behind
it
and
it's
really
abstract
and
kind
of
out
there
and
actually
show
you
experiments,
but
I
can
do
that
in
the
coming
weeks.
Maybe
I
don't
know,
maybe
it's
relevant
to
this,
but
it
looks
interesting.
I
know
that
you're
a
couple
years
ago.
A
A
Mean
so
yeah
well,
I,
don't
know
if
I
have
it
right
now,
I
don't
think
I
can
get
to
it
now
anyways.
So
that's
another
thing
again:
if
you're
interested,
let
Richard
know
you
have
this
contact
information,
okay,
our
care
single,
so
prokaryotes,
many
people,
you
know
I'm
sending
Bradley
a
background
paper,
so
he'll
send
me
a
paper
and
then
I
can
I
can
maybe
put
this
in
a
little
bit
more
context
at
the
next
meeting,
or
we
can
discuss
a
little
bit
about
the
idea.
A
A
Let's
see
so
today,
I
was
gonna
cover
a
couple
of
papers
that
hadn't
had
a
chance
to
cover.
In
meetings
past
we
had
things
that
we
were
talking
about
Karen
Oh.
Last
week
we
were
talking
about
the
John
Conway
memorial
tribute,
so
John
Conway
died.
What
two
weeks
ago
now-
and
we
had
some
interesting
it-
some
interesting
things
in
John,
Conway's
work
on
his
game
of
life,
so
showing
him
to
the
group.
A
A
Ok,
yes,
all
right,
so
the
first
paper
is
this
sexually
was
something
I
found
by
one
of
these
news
press
releases
and
they
made
it
sound.
Really,
like
you
know,
the
press
releases
make
it
sound,
really
super
provocative.
It's
almost
like
science
fiction
and
then
you
read
the
paper
and
it's
still
pretty
good,
but
it
has
like
you
know
it's
a
little
bit
difficult
to
see
how
they
got
the
news
release
story
out
of
the
paper,
but
I
mean
that
doesn't
take
anything
away
from
the
paper.
That's
just
an
observation.
Anyways.
A
This
paper
is
emergence
of
collective
oscillations
and
adaptive
cells
and
I
know.
We've
talked
a
lot
about
differentiation,
waves
and
things
and
cells
in
this
group,
and
so
this
is
a
sort
of
a
an
overview
of
this
set
of
experiments.
This
group
did
on
collective
oscillations
and
so,
if
you
could
believe
it
cell
membranes,
so.
A
Sensing
in
several
cellular
systems,
with
increasing
biological
complexity,
reaffirms
the
role
of
adaptation
empowering
these
oscillations,
and
so
they
argue
that
the
cell,
the
cell
communication
channel,
is
generally
dissipative.
We
talked
about
that
in
Georgia
may
Olaf
skis
presentation,
you
talked
about
dissipative
systems
are
just
dissipative
structures
and
then
oh
now,
I'm
having
a
problem
loading
more
pages
for
some
reason.
A
D
A
A
A
Okay-
okay,
that's
fine,
so
I
just
pulled
up
this
paper
on
collective,
like
cell-to-cell
communication
with
collective
oscillations,
so
this
is
a
population
of
cells
and
they're,
exhibiting
this
cell
to
cell
signaling
mechanism
and
so
here's
their
model
here.
If
I
can
zoom
in
on
this
I
guess,
I
can
okay.
A
Spontaneous
oscillations
on
a
communicating
cell
population
scenario:
mechanical
oscillators
were
cells
community
by
a
shared
displacement
s
of
the
physical
environment,
and
so
this
is
the
shared
displacement.
This
parameter
s
and
they're,
using
a
Springer
to
show
the
displacement.
It
goes
back
and
forth
here.
A
Activity,
a
against
of
a
cell
against
the
displacement,
is
regulated
by
an
intracellular
Network
which
responds
to
s
there
a
mechanical
sensor.
So
this
activity
in
a
is
regulated
by
this
Network
and
then
that
responds
to
s.
So
s
is
stimulating
the
cell
as
this
activity,
which
is
driven
by
the
these
biochemical
networks
and
then
that's
the
thing.
That's
regulating.
D
A
So
it's
it's
regulating
this
whole
interaction
and
then
B
is
down
here.
Is
it
illustration
of
chemical
oscillations
where
cells
interact
via
shared
extracellular
signal
s?
The
signal
is
sensed
and
secreted
by
individual
cells.
So
this
is
the
s.
This
is
the
signal
concentration,
so
this
could
be
any
number
of
secreted
proteins
or
morphogens,
as
they
call
them
or
whatever,
and
they
are
secreted
by
the
activity.
A
of
these
biochemical
networks.
A
They
are
secreted
into
the
extracellular
matrix,
which
is
area
between
the
cells
and
then
they
communicate
with
the
cells.
So
these
essences
are
related
I.
Suppose
the
shared
displacement
is
the
displacement
of
the
cell
by
this
physical
environment,
and
then
the
s
here
is:
are
these
signal
concentrations
in
the
physical
environment
not
clear
exactly
what
the
exact
relationship
is,
but
they
both
have
they're
both
you
know,
I.
Think
they're
related
is
what
they're
saying.
So
you
have
this
sort
of
forcing
mechanism
on
the
cell.
A
It's
doing
something
inside
the
cell,
it's
forcing
it
to
adapt
to
its
conditions,
and
then
it's
exporting
some
signal
and
it's
telling
the
other
cells
that
there's
something
going
on
in
the
environment.
Now
this
is
all
going
on
in
parallel,
so
you
can
see
that
when
you
have
this
sort
of
relationship,
it's
sort
of
a
collective
behavior
type
thing
where
you
know
the
cells
act.
A
A
So
they
talk
about
homogeneous
cell
populations
are
able
to
exhibit
a
rich
variety
of
organized
behaviors
among
them
periodic
oscillations,
so
periodic
oscillations
or
oscillations
that
are
coordinated,
but
also
periodic
and
time.
So
you
know,
if
you
see
a
bunch
of
cells,
go
from
red
to
blue
and
then
back
to
red
again
having
that
sort
of
very
orderly
transition
amongst
all
cells,
that's
a
sort
of
coordinated
organized
behavior.
A
In
these
examples,
communications
or
chemical
or
mechanical
signals
and
essential
to
activating
Classen
cells,
dub
dynamical
quantity
to
emphasize
the
role
of
increased
cell
density
in
triggering
the
oscillations.
This
class
of
behavior-wise
outside
the
wall
known
promote
o
paradigm
of
oscillator
synchronization.
A
So
this
car
moto
paradigm
is
it's
a
special
type
of
model
for
modeling
oscillatory
behavior,
it's
a
set
of
differential
equations
and
the
place
it's
most
famously
been
observed
is
in
Firefly
colonies.
So
you're
familiar
with
Steven
Strogatz
is
a
mathematician.
You
wrote
a
book
called
sink.
This
is
about
twenty
years
old
and
in
that
book
he
gives
a
good
sort
of
a
popular
science
example
of
fireflies
synchronizing
their
flashing.
A
So
if
you
are
familiar
fireflies
they're,
you
know
they're
out
in
the
summertime
in
the
grass
and
they
fly
above
the
grass
and
they
give
off
this.
This
glowing
signal
from
their
tail
and
it's
a
luciferase
protein,
so
it
glows
when
they
their
nervous
system,
sends
a
signal
to
the
sack
of
the
celery's,
and
they
can
do
this.
They
can
synchronize
their
glowing
so
that
they're
glowing
in
in
some
sort
of
orderly
pattern,
and
so
they
observe
their
neighbors
and
they
do
this
sort
of
glowing.
A
Dynamical
Korn
sensing
I'm
not
familiar
with
that,
but
I
do
know
about
quorum.
Sensing,
which
is
you
commonly
see
it
in
bacteria
where
they
have
to
you,
know,
meet
a
quorum
in
their
social
group
to
you
know,
do
some
sort
of
behavior,
so
they
execute
a
behavior
based
on
what
they're
sensing
of
the
neighbors
what
their
neighbors
are
doing,
and
then
they
make
a
decision
to
do
things
in
unison.
So
that's
what
they're
kind
of
proposing
for
these
cell
populations
that
they're
coordinating
their
activity
in
this
way
and
that
they're
acting
in
unison.
A
So
they
go
through
some
equations
here,
so
they
give
some
necessary
conditions
for
Otto
induced
oscillations.
There
are
some
equations
I'm
not
going
to
walk
through
them,
because
I
really
haven't
dug
into
them
to
see.
You
know
what
the
relationship
is
between
them,
but
they're
looking
they
think
of
it
in
terms
of
an
equilibrium
state
of
s,
so
the
equilibrium,
as
we
talked
about
s
up
here.
A
A
A
So
they,
this
is
a
type
of
term
regulation
here.
A
phase
leading
response
to
a
low
frequency
signal
has
also
been
reported
in
the
activity
of
e.coli
chemoreceptors
and
in
the
Osmo
response
in
yeast,
and
so
these
are
examples
of
adaptive
sensory
systems.
There's
a
response
to
a
step
signal
at
t.
Minus
zero
is
shown
in
Figure
two
a
so
this
is
this.
Is
the
figure
two
a
so?
A
This
is
all
basically
the
dynamical
response
of
the
signal
on
the
cell,
so
the
signal
is
sensed
by
the
cell
and
the
biochemical
networks
respond
and
they
respond
in
a
certain
way.
It's
sort
of
like
sort
of
a
cellular
version
of
signal,
processing,
so
they're
getting
this
signal
in
they're
trying
to
determine
how
to
behave
but
because
you're
getting
you
know,
sort
of
a
lot
of
like
it's
based
on
a
concentration.
A
So
a
is
response
to
a
stepwise
signal.
So
if
you
provide
a
stepwise
signal
based
on
s
over
time
where
this,
this
is
another
type
of
signal
that
they're
using
so
response
to
a
stepwise
signal.
After
a
transient
response,
it
returns
to
its
pre
stimulus
state
with
a
small
error
epsilon.
So
this
is
the
error.
So
this
is
the
response
curve.
This
is
the
stimulus
curve,
so
a
here
over
time.
A
In
the
simplest
case,
the
transient
response
is
controlled
by
the
activity
shift,
timescale
and
the
circuit
feedback
timescale
solid
and
dashed
lines
in
here
correspond
to
the
over
damped
and
under
damped
situations
respectively.
So
this
is
the
solid
line
is
an
over
damped
response.
The
one
is
an
under
damped
response
and
then
damp
responses.
A
It's
just
damped.
It's
you
know
it's
not.
It's
already
been
inhibited
in
some
way
responds
to
a
sinusoidal
signal
at
low
and
high
frequencies.
So
this
is
B.
This
is
a
response
to
a
sinusoidal
signal
as
input.
So
s
over
time
is
this
red
signal.
This
is
a
sine
wave.
That's
the
thing
that's
going
into
the
cell
and
this
blue
function
is
the
thing.
A
That's
the
cellular
response
to
that
forcing
so
you
can
see
it's
dampened
a
bit
in
terms
of
its
amplitude
and
then
this
is
this
is
phase
lead
and
then
this
is
phase
lag
so
there
now
you
have
this
S,
which
is
the
forcing
mechanism
this
red,
some
sine
wave,
and
then
you
have
blue,
which
is
the
response
of
the
cell,
and
that
is
a
trial.
Adding
the
bit
rather
than
dampen
I
mean
it's
a
little
bit
dampened,
but
it's
not
it's
mostly
wagging
here,
and
so
that's
there.
A
That's
part
of
their
model
here
and
the
argue.
There
are
six
item
old
ein
Amex.
So
these
DQ
s,
these
these
dynamical
quantum
sensing
systems
indict
st
liam,
which
is
a
slime,
mold
and
other
eukaryotic
cells,
take
the
form
of
pulsed
release
of
signaling
molecules.
The
highly
nonlinear
two
component,
Fitzhugh
Nago
anaguma
model,
is
often
employed
for
such
excitable
phenomenon.
So
Fitzhugh
Nagumo
is
a
oscillation
mechanism.
That's
sort
of
based
on
neuronal
models,
so
consume
Nagumo
is
it's
kind
of
like
the
kurodo
model.
A
So
they
assume
that,
with
an
action
potential
that
you
have
this,
you
have
the
stimulating
this
forcing
mechanism
and
then
there's
a
response
by
the
cell,
and
this
response
is
that
one
of
these
types
of
functions
that
you
see
here,
action
potential
is
very
distinct.
It
has
like
a
couple
of
components
to
it,
so
it
has
like
a
Rises
and
then
Falls
and
then
comes
back
to
an
equilibrium.
In
this
case
you
don't
really
see
that
but
they're
using
that
model
to
to
get
at
this
mechanism.
So
they
often
yet.
A
A
Sensory
did
similar
to
the
sensory
adaptation
model
discussed
above
each
FHN
circuit,
as
a
memory
know
why
that
keeps
its
activity
at
a
low
RA
keeps
its
activity
of
a
law
which
is
the
parameter,
a
the
resting
state
under
a
slow,
varying
signal
s
over
time.
So
as
a
signal
s
over
time,
stimulates
a
cell
there's
activity
in
the
cell,
but
it's
kept
low.
A
A
So
they're
doing
a
lot
of
this
I
think
in
the
absence
of
like
recordings,
but
batch
I
think
they
may
have
some
recordings
in
here,
but
they're
showing
this
in
terms
of
like
simulation
versus
theory.
So
so
this
is
a
pretty
interesting
paper.
I
guess
I'm
not
going
to
go
through
much
more
of
it,
because
it's
I
don't
know
if
I
could
really
do
justice,
but
they
talk
about
these.
A
Like
like
Aletta
cos
elations
in
yeast
as
an
example
of
this
type
of
quorum,
sensing
and
then
yeah
so
I
think,
and
then
they
go
back
to
dicta.
Still
it's
the
William
a
bit.
It's
a
very
good
paper.
I
mean
it's
a
bit
slow
over
I
had
actually,
but
it's
it's
a
nice
example
of
how
cells
communicate
and
how
they're
communicate
to
different
set.
You
know,
signals
and
how
they
collectively
respond
to
that.
Okay.
So
we
have
little
disruption
here.
A
Okay,
so
that's
that
paper,
then
this
is
the
next
paper
here,
topological
turbulence
and
cell
membranes.
So
this
is
a
nice
one.
This
is
a
nature
physics
paper
that
just
came
out
recently
and
again
this
one
of
these
press
releases
attached
to
it.
So
you
know
the
press
release.
Actually,
the
press
release
in
the
paper
were
both
equally
is
inspiring.
So
I'd
like
this,
this
paper
quite
a
bit-
and
this
actually
is
more
direct
as
more
directly
relevant
to
this
stuff.
A
Actually,
that's
a
heart
and
brain
and
cell
death.
Many
advances
have
been
made
in
understanding
and
throwing
the
defect
dynamics
and
active
and
passive
non-equilibrium
fluids.
Yet
it
remains
unknown
whether
the
statistical
laws
of
govern
the
dynamics
of
defects
in
classical
or
quantum
fluids
extended
the
extend
to
active
matter
and
information
flows
and
living
systems,
so
they
tuck
here
they
talk
about.
A
We
show
that
a
defect
needed
turbulence
underlies
the
complex
wave
propagation
patterns
of
rho
g
GP,
signaling,
I'm,
the
membrane
of
starfish
egg
cells,
a
process
relevant
aside
of
skeletal
remodeling
and
cell
proliferation,
and
so
they
talk
about
this
and
very,
very
physical
terms.
So
they
talked
about
several
key
statistics
and
scaling
logs
of
scaling
laws.
They
use
a
tunnel
on
secure
point
cortex
model
as
well
as
a
generic,
complex,
ginsberg,
Lando
continuum.
C
A
Typical
bz
reaction,
this
image
in
e,
so
this
is
a
picture
of
what
it
would
look
like,
so
they
do
that
they
pour
some
chemicals
into
a
into
of
that
and
they
take
an
image
of
it,
like
the
interactions
between
different
chemicals
happen
in
this.
So
it's
a
reaction,
chemical
reaction
and
there's
all
this
pattern
formation
going
on
and
then
actually
what
they're
doing
here
is
they're
showing
defects
in
the
pattern
formation.
So
this
is
a
card,
so
this
is
toppled.
A
Topological
defects
populate
the
phase
field
of
RO
gtp
waves,
so
a
as
membrane,
ro
G,
a
gtp
waves
are
visualized
with
gfp
and
RGB
d
reporter
and
live
starfish
show
sites.
So
that's
a
and
of
course
you
have
the
weather,
showing
the
biochemistry
here
then
they're,
showing
what
the
pattern
will
look
like
on
the
surface
of
the
egg.
A
A
A
He
is
a
representative
phase
field,
reconstructed
from
pixel
oscillations
or
the
dense
population
of
topological
defects.
So
this
again
is
a
phase
field
view
of
this
surface
and,
like
I
said
it
looks
like
a
Java
tuna
melisach
Jabotinsky
reaction,
but
this
is
actually
an
image
of
this
of
these
pattern
formations
shown
in
B.
This
is
just
a
different
view.
They
reconstruct.
A
The
fluorescence
of
these
images
using
a
phase
field
reconstruction
and
then
they
look
for
defects,
and
so
they
find
some
defects
in
these
boxes
here
and
so
then
F
is
time,
lapse
snapshots
of
the
phase
field
in
the
vicinity
of
a
topological
defect
every
10
seconds.
So
this
is
showing
broken
out
by
in
time
in
terms
of
this
reaction:
G,
trajectories
of
defects
undergoing
creation
and
annihilation
events.
A
So
this
is
a
where
they
have
creation,
annihilation
events.
I,
don't
know
how
exactly
they
do
these,
how
they
did
this
analysis
but
they're,
showing
you,
the
defects
in
this
field,
sort
of
in
this
figure
and
they're
showing
how
sort
of
it
moves
the
defects,
I
guess,
are
short-lived
I
can't
really
tell,
but
both
types
of
events
always
have
all
involve
pairs
of
oppositely,
charged
defects,
red
being
the
positive
and
blue
being
the
negative.
The
defect
density
fluctuates
around
a
constant
value
at
steady
state.
I
characteristic
wave
numbers
are
positively
correlated
with
defect.
A
A
D
A
This
is
a
comparison
of
what
they
see,
experimentally,
with
a
discrete
holds
on
Sagar
Point
vortex
model,
a
complex,
ginsburg,
landau
continuum
model.
So
these
these
two
different
models,
one
is
a
point,
four
text
model,
nia,
there's
a
continuum
model
and
they
they
basically,
you
know
just
compare
the
two
models
with
the
empirical
observations.
A
So
I
think
next
week
I'm
going
to
present
this
may
be
a
little
bit
more
detail.
This
is
the
paper
we
want
to
show
people,
but
it
didn't
really
give
it
a
lot
of
time
didn't
arrive
at
a
time
allotted.
We
have
to
technical
difficulty
here,
so
I'm
going
to
prison.
Maybe
present
this
a
little
bit
more
a
little
bit
more
detail
on
future
meetings.
I
will
send
these
papers
out
in
the
because
we
didn't
have
a
lot
of
people
at
the
meeting.
A
I'm
gonna
send
these
papers
out
enough
in
an
email
link
to
the
the
repository
where
I
have
the
papers
and
I
think
this
yeah
I
think
this
is
a
nice
paper,
but
I'm
not
sure
if
I
totally
understand
what's
going
on
in
it
might
be
relevant
to
some
of
the
stuff
we're
doing
with
differentiation
waves.
We'll
see,
maybe
you
know
maybe
and
then
of
course
the
other
thing
we're
going
to
talk
about
is
the
work
on
the.