►
Description
Attendees: Bradly Alicea, Mayukh Deb, Vinay Varma, Ujjwal Singh, Susan Crawford-Young, and Abhishek Bvs. GSoC Updates and discussion, paper planning for Periodicity of the Embryo.
B
A
B
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A
C
C
C
C
B
A
Think
what
on
this
idea,
putting
it
on
different
under
different
projects,
because
you
guess
you
end
up
generating
these
unwieldy
URLs.
So
it's
like
you're
mentioning
you
know:
you'd
have
one
for
each
project
and
it
would
be
like
you
know,
maybe
all
different
names
and
I'm
not
sure.
Well,
maybe
if
we
linked
sort
of
on
the
front
end
to
those
models,
the
models
are
actually
sort
of
underneath
the
surface
and
were
able
to,
like
you
know,
just
kind
of
link
to
them.
You
know
I
mean
like.
B
A
D
C
E
C
C
C
C
D
B
A
E
E
D
D
E
E
D
C
A
All
right,
I
think
that's
a
good
place
to
stop.
Yes,
a
good
conversation
want
to
keep
going
in
slack
and
yes,
this
is
a
very
good
tutorial
sort
of
introduction
to
it.
So
that's
very
that
looks
very
good
and
Susan
said
that
it
would
be
very
helpful
to
her
as
a
beginning,
Python
user.
So
this
is
a
gift,
someone
already
who
finds
it
to
be
useful.
So
that's
good.
Thank
you
for
the
update,
I'm
gonna
can
I
share
my
screen
thanks
yeah.
A
A
A
A
A
E
E
B
B
A
Work
threat
up
a
data
set,
that's
in
progress,
I
guess
because
we're
still
kind
of
working
with
the
data
and
then
design
evaluation,
that's
still
open,
but
I
think
we're
we've
kind
of
gotten
some
feedback
on
that
I'm,
not
sure
what
we
actually
have
in
the
comment
here.
I
can't
remember
where
we
were
putting
that
stuff,
but
I
haven't
you
know:
I've
gotten
a
little
bit
of
feedback,
but
I
guess
I've
communicated
it
to
you.
A
Let's
see
static
hosting
number
forty
we
were
talking
about
this
today,
so
that's
sort
of
in
progress,
yo
move
that
in
progress,
and
then
this
one
here
number
seven,
that's
that's
the
ones,
movement
tracking
and
prediction:
it's
still
on
hold
integrating
models
with
web
portal.
We
were
talking
about
that
as
well
as
part
of
40,
so
we'll
move
that
doing
progress
and
then
integrate
cell
track
with
a
segmentation.
That's
a
bit
down
the
road,
so
I
think
that's
that's
good.
A
I,
like
I
said
Krishna
is
still
open
for
a
presentation
that
I
took
I've
talked
to
him,
he's
still
working
on
it.
I
guess
so!
That's
good!
That's
the
key
sock
board.
I
know
I
wanted
to
talk
about
this
periodicity
paper,
so
I
I
think
we've
all
brought
it
up
last
meeting
or
I
think
we
talked
about
it
last
meeting
this
periodicity
paper.
That
is,
if
I
go
to
this
link
here,
I
think
I
can
pull
up
the
outline.
A
A
This
is
the
idea
of
tempo
and
mode
which
they've
used
them
in
the
context
of
evolutionary
biology,
but
we're
also
looking
for
us
to
look
at
that
question
in
the
context
of
development,
and
so
it's
you
know,
there's
a
mix
of
computation
and
data
science
and
sort
of
biological
interpretation
here,
so
you
know
they're
a
couple:
have
you
agreed
the
extended
abstract?
Why
don't
I
put
up
in
the
chat,
so
people
could
read
it
and
I?
A
Can
you
can
tell
me
if
you're
interested
or
if
you
have
ideas
for
contributing
to
this
or
not,
and
so
it
just?
Basically,
it's
finding
these
patterns
in
different
number
and
different
types
of
embryos,
these
temporal
patterns,
so
in
zebrafish
during
early
embryogenesis-
and
this
is
the
zygote
stage.
This
is
the
cleavage
stage
you
have
these
pulses
of
cell
divisions
or
these
realms
of
cell
divisions
in
time
ne
and
C
it
towards
the
end
of
the
cleavage
stage
it
kind
of
breaks
apart
and
becomes
asynchronous.
A
You
see
some
of
that
in
the
C
elegans,
as
well.
With
these
patterns
of
divisions,
these
realms
of
division,
you
even
see
it
in
the
post
embryonic
worm
where
you
have
cells
that
are
differentiating,
and
so
there's
there's
that
pattern.
So
that's
that's
the
idea
behind
the
paper
now.
What
I've
done
this
week
is
I've
come
up
with
an
outline
for
a
sort
of
a
path
forward
of
us.
So
this
is
the
to
do
study
plan.
So
there's
an
objective.
We
have
participants,
I
can
add
Susan
if
she
would
like
division
of
labor.
A
So
we're
gonna
divide
up
the
labor
I
think
my
yoke
is
division.
Events
analysis.
We
could
maybe
use
some
of
that
in
this
paper.
We
could
just
you
know
if
you
want
to
generate
a
couple
graphs
from
the
data
we
can
maybe
see.
Some
of
these
we'd
have
to
transform
the
data
differently
than
what
you
have
there.
You
know
one
way
to
show
this
effect
is
to
use
histograms
with
bins
of
different
sizes.
They
put
50
minutes
there.
That's
sort
of
arbitrary
but
I.
A
Think
extracting
the
data
directly
from
images
is
an
interesting
that
is
set
to
use
because
it
might
show
it
might
confirm
what
we've
seen
in
some
of
the
the
nucleus
tracking
data.
Then
it
was
well.
You
know
you
can
do
a
number
of
things
that
analysis
frequency
analysis,
as
you
mentioned,
I'm
kind
of
coordinating
this
and
Jesse
and
dick
are
interested
in
manuscript
review
and
probably
other
things
and
then
Susan
I
can
put
you
in
for
something.
If
you
want.
A
Okay,
yeah,
the
data
sets
are
right.
Now
we
have
C
elegans,
so
we
have
C
elegans
and
the
division
of
150
minutes
of
you
know
embryo
Genesis
we
have-
and
this
is
an
you
know
a
example.
This
is
the
control
here,
the
simulated
embryo
which
you've
not
shown
to
the
group.
But
it's
this
this
program
that
you
know
you
just
basically
take
the
idea
of
dividing.
A
You
know,
cells,
dividing
and
you
do
it
at
different
according
to
different
distributions,
and
you
generate
these
data
that
are
simulated
and
I,
call
that
the
simulated
embryo
and
that
we
use
that
as
a
control
to
compare
that
to
C
elegans
and
zebrafish,
and
so
we
have
the
zebrafish
data
of
the
450
minutes
as
well,
and
that's
a
different
data
set.
We
can
go
over
there's
this
z
fin
site,
which
actually
goes
through
the
different
stages
of
zebrafish
development.
A
So,
if
you're
interested
in
learning
about
zebrafish
development,
they
they
have
like,
they
have
all
the
sort
of
laid
out.
So
zebrafish
developments
different
from
C
elegans
development.
You
have
the
zygote
and
the
cleavage
in
the
blastula
gastrula
segmentation
stage
for
in
gila
stage
hatching
stage,
so
this
is
a
different
time
scale
than
C
elegans
and
it's
a
bit
more
complex
than
C
elegans,
but
these
data
exists.
We
have
the
nucleus
tracking
data
for
this,
that
we've
gotten
from
one
of
our
partners
in
the
data
acquisition
and
then
we
this.
A
So
this
is
like
the
canonical
sort
of
accounting
of
cell
division.
Here
you
can
see
that,
like
you
get
into
the
at
the
end
of
the
cleavage
stage,
120
cells,
so
you
know
we
have
a
bit
beyond
that.
But
well
you
know
we
can
kind
of
hash
that
out
as
we
go
through
this,
and
so
there
are
some
ideas
about
like
looking
at
the
difference
between
C
elegans
and
zebrafish
data.
A
You
might
be
able
to
simulate
those
data,
then
to
maybe
shift
the
shape
and
timing
of
cell
division
to
see
what
happens.
I
know
that's
a
little
bit.
Autism
isn't
a
concept,
but
you
know
basically
taking
the
taking
the
existing
data
and
sort
of
you
know
applying
some
sort
of
distortion
to
it.
To
see
what
happens
to
sell
the
vision,
you
know,
if
you
do,
that
and
I
mean
I'm
just
thinking
of
like
ways
you
could
like
well
computational
experiments
with
the
data,
but
that
might
show
something
as
well.
A
A
Yeah,
so
that's
the
that's
our
this,
this
periodicity
of
the
embryo
paper
and
we'll
be
working
on
this
for
awhile,
because
we're
trying
to
target
a
special
issue
of
a
journal
next
year.
That's
the
first
draft
is
due
next
year,
so
in
2021.
So
we
have
a
bit
of
time
to
work
on
this,
but
I'll
be
bringing
it
up
in
the
meetings
and
we'll
see
how
much
progress
we
can
make
on
it.
So
if
you
have
any,
you
know
ideas
about
it.
Let
me
know
next
thing:
I
want
to
talk
about.
A
B
A
A
But
then
it
breaks
down
after
a
while
when
the
idea
would
be
to
like
figure
out
where,
in
developmental
time,
these
cycles
are
happening
and
maybe
trace
it
back
to
the
embryo,
or
you
know,
do
some
sort
of
simulation.
That
shows
that
you
know
there's
some
importance
to
the
well.
You
actually
do
have
the
XYZ
coordinates
of
these
data
too.
So
it's
not
like.
We
can
go
back
to
that
and
look
at
like
what's
going
on
in
the
in
the
embryo
in
terms
of
their
the
cell
positions.
A
So
we
have
these
see
these
in
zebrafish
here.
So
the
idea
of
the
zebrafish,
the
Z
fin
page
I,
showed
you
was
a
stub
that
just
basically
described
the
progression
of
development.
So
on
C
elegans,
you
know
you
don't
have
too
many
stages
within
everyo.
You
have
like
a
comma
stage
and
it's
a
pretzel
stage
and
some
other
stages,
but
in
zebrafish
you
actually
have
very
distinct
phases
of
sort
of
how
the
embryo
exists.
A
So
this
is
the
zygote
stage
here
as
as
the
cleavage
stage
and
then
there's
a
sort
of
breakdown
in
this
sort
of
periodicity
of
cell
division.
So
we
might
ask
why
that
is,
and
you
know
maybe
it's
that
it's
getting
you
know
we
get
enough
cells
or
maybe
it's
starting
to
develop
tissues
that
just
you
know,
account
for
this
sort
of
changing
regime,
and
so
this
is
a
very
qualitative
account.
Well,
we
want
to
maybe
do
is
link
it
back
to
the
data
to
the
anatomy.
A
Yeah
after
that,
it's
you
know
at
different
intervals.
You
have
these
different
patterns
so
like
what
would
my
up
show?
It
was
like
sort
of
a
cumulative,
you
know
increase
in
the
number
of
cells,
and
you
can
see
that
pattern
where
kind
of
levels
off
and
then
it
comes
back
up
and
if
you
express
that
as
a
histogram
you'll
see
this
pattern
of
the
cyclical
pattern.
But
then
you
know
the
the
question
is:
how
do
you
interpret
say
the
period
of
that?
A
So
the
period
between
Peaks
you
know
does
that
is
that
the
same
across
development
as
a
decrease
increase?
Is
that
something
you
know
because,
like
there's
a
period
of
time
between
cell
divisions
rate
Iran's
ups
over
the
you
have
them
at
10
or
15
minutes,
maybe
20
minutes
and
they
they're
not
necessarily
regular
over
every
cell,
some
cell
divisions.
But
there
are
these
also
apparently
they're
these
sort
of
time.
You
know
things
that
happen
in
a
certain
period
of
time
and
then
there's
no
soul
division.
A
Then
there's
another
peak
of
cell
division
and
there's
a
period
between
those
two
waves.
And
so
the
question
is,
is
what
is
so
this
period
in
between
here,
for
example?
What
is
a
significance
of
that?
That's
you
know,
above
and
beyond,
like
individual
cell
divisions,
this
is
like
the
it's
sort
of
the
mode
of
this
developmental
process.
Remember
this
is
all
occurring
on
a
on
a
tree
of
cells,
differentiating
so
they're
differentiating
on
this
lineage
tree,
and
we
talked
a
lot.
A
A
E
A
A
So
I
put
the
links
in
the
chat,
so
why
don't
I
move
on
to
this
thing?
So
this
week,
I
went
to
a
conference
called
artificial
life
and
I
had
a
chance
to
do
a
an
impromptu
presentation,
open
one
and
so
I
was
introducing
open
room
to
this
group
of
people
who
may
or
may
not
have
heard
about
it.
I'm
gonna
go
through
it's
a
five-minute
talk,
so
I'm
gonna
go
through
it
really
quickly
and
see
what
you
guys
think.
A
So
this
is
a
group
of
people
they
doing
simulations
of
life,
simulations
of
evolution
and
some
development.
This
is
some
of
them
have
heard
of
it.
Of
course,
some
of
them
not
but
I'm
going
to
go
through
this,
and
you
know
as
if
I
were
running.
This
on
educated
group
was
interested
in
knowing
more
about
open
one.
A
Elegans
connect,
oh
so
C
elegans
connectome
is
302
neurons
and
we
know
the
connections
between
them
and
that's
not
true
of
every
connect,
though
so
it's
a
unique
situation,
theologist
and
because
we
know
all
the
connections
we
can
reconstruct
the
connectome
relative
to
behavior.
So
all
they
did
was
simulate
this
connectome
and
use
that
to
drive
the
robot.
A
You
know
that
explore
various
aspects
of
this,
so
we
have
the
open
were
movement
database,
which
is
a
database
of
movement
data.
So
if
you
want
to
understand
what,
while
the
worm
is
moving
around
and
in
behaving,
we
have
data
on
that
we
have
a
sort
of
next-generation
robot,
so
this
actually
looks
like
a
worm
and
they're
using
the
same
principle
to
drive
the
worm,
but
it's
a
bit
more
refined.
So
we
have
that
going
on.
A
We
have
worm
sim,
which
is
a
program
that
allows
you
to
simulate
the
nervous
system
Gepetto,
which
is
similar
to
worm
sim,
and
these
are
models
that
plug
into
like
platforms
like
neuron,
so
you
have
a
good
basis
for
reusing
the
models
and
making
them
more
relevant.
To
you
know
the
neuro
computational
neuroscience
communities
you
have
cybernetic,
which
is
a
pro.
It
is
a
software
that
simulates
by
out
the
biophysics
of
the
worm.
So
it's
not
it's
movement,
but
it's
the
biophysics,
not
the
behavior
and
then
evil
worm
which
we
explore
development.
A
You
know
about
that.
So
this
is
something
I
plan
to
sort
of
tongue-in-cheek.
We
have
so
many
things
going
on
that
someone
commented.
It
looks
like
the
complexity
of
the
dependency
graph
between
the
github
repos
of
the
open-room
project
will
soon
be
comparable
to
that
of
the
nervous
system
of
the
actual
rock,
and
so
this
is
in
the
open
worm
hub
repository.
You
have
all
these
different
dependencies
and
that's
the
idea,
but
you
know
that's
that's
what
happens
in
an
open
source
project
and
when
you're
trying
to
do
something
like
this.
A
So
by
simulating
a
small,
embodied
nervous
system,
can
we
learn
about
its
information,
processing,
organizational
principles
and
we
can
similarly
think
about
emergence,
and
so
this
is
the
idea
behind
open
worm
in
one
sense
is
the
model
realism,
and
so
you
can
see
in
this
case
you
have
two
frogs
and
two
airplanes.
One
is
a
real
frog
and
a
real
airplane.
The
other
is
a
model,
but
the
models
are
realistic,
so
they're
they're
hyper
realistic
and
not
abstract
models,
and
so,
as
a
ray,
you
can
see
an
example
of
two
of
the
open
worm
model.
A
This
is
sort
of
the
inside
of
the
worm,
the
muscle
walls
and
the
axons
leading
to
motor
neurons,
and
so
this
is
very
hyper.
Realistic
and
I
would
recommend
this
special
issue
of
the
Royal
Society
Journal.
There
are
a
bunch
of
papers
in
there
on
unopened
worm
in
the
various
ways
in
which
you
can
model
worms.
It's
really
it's
really
a
good
exposition
of.
What's
going
on
in
open
world,
and
so
actually
Matteo
Pantera
was
another
person
he
founded
open
worm.
He
talked
about
at
the
Royal
Society
meeting.
They
had
this.
D
A
About
a
Turing
test
for
warm
movement,
and
so
a
Turing
test,
of
course,
is
something
that,
where
you
test
a
computer
to
see,
if
it's
you
know
the
they
do
in
AI
four
different
programs
to
make
sure
that
they're
not
generating
randomness.
You
know
this
is
something
you
can
do
for
a
worm
phenotype.
So
one
of
these
is
a
simulation.
The
other
is
natural
worm
crawling
on
an
agar
plate,
and
so
he
says,
can
you
tell
which
is
which
and
of
course
the
idea
is?
A
Maybe
you
can't,
but
that's
the
idea
we,
you
know,
we
don't
have
a
Turing
test
for
worms
but
yeah.
You
know
this
is
something
that's
a
really
fascinating
idea.
When
you
have
these
realistic
simulations,
you
know
you
can
actually
ask
questions
like
this.
The
problem,
though,
is
that
you
have
this
hyperrealism.
A
A
Hyper
realistic
models,
but
they
actually
don't
a
lot
of
times
account
for
things
like
adaptability,
robustness
and
evolved
ability
that
you
might
expect
out
of
a
living
system.
So
that's
a
problem,
but
nevertheless
we
have
these
nice
models.
So
they
come
from
this
a
classic
paper
by
white
in
1986
and
they
explored
the
anatomy
of
the
covering
and
so
they're
looking
at.
A
These
are
anatomical
diagrams
of
the
nerve
ring
and
its
network,
so
they're
a
bunch
of
neurons
here,
they're
defined
with
names,
nomenclature,
names
and
they
have
connections
and
even
figure
out
all
the
connections
like
they
said
it's
not
a
very
big
connectome
and
you
have
these
diagrams
at
different.
You
know
anatomical
orientations,
and
so
you
take
that
sort
of
data
and
produce
something
like
this,
which
is
a
worm.
This
is
a
worm
viewer
view
of
this
is
from
browser
open,
worm
dorg.
A
So
you
can
play
with
this
online,
and
this
is
the
sort
of
the
muscles
and
you
can
add
muscle
wall
and
you
can
add
cuticle
and
you
could
actually
have
a
full
worm
where
you
can
take
away
layers
and
have
this
minimal
representation
of
the
world.
But
it's
hyper
realistic
and
that
it's
all
sort
of
in
place-
and
this
is
the
adult
worm.
So
this
is
not
development,
so
you
can
visualize
the
302
so
connectome.
So
why
would
you
want
to
have
a
hyper-realistic
model
based
on
real
data?
A
Well,
one
reason
is
you
want
to
build
these
type
of
models
and
you
know
simulating
behaviors,
but
you
also
want
to
visualize
the
data
and
have
these
relations.
You
know
find
these
new
relationships.
So
in
this
case
we
have
a
sensory
network
of
sensory
neurons
in
a
sensory,
remote
network
of
motor
neurons.
These
are
the
I
think
the
chemical
synapses,
and
these
are
the
electrical
synapses
or
the
gap
junctions,
and
so
those
two
networks
give
you
different
results.
A
Junctions
can
do
some
of
those
things,
but
it's
a
different
mechanism,
and
so
we
can
ask
things
like
how
are
gap,
junctions
and
synaptic
components
of
a
connectome
generated
development,
and
so
there's
this
paper,
that's
coming
out
in
frontiers
in
so
we
lure
neuroscience
something
I've
been
working
on,
it's
this
model
of
connective
Genesis,
so
you
know
I'll
send
this
out
to
the
group
one!
That's
out!
A
There's
also
this
thing
about
single-cell
simulation
of
ion
channels.
So
for
each
cell
we
know
that
we
know
the
cell
name
and
we
know
kind
of
how
it's
connected.
Well,
we
can
also
model
within
the
cell.
We
can
model
these
ion
channels
as
blocks,
so
we
have
like
models
where
we
model
different
components
of
flow
of
ions
in
these
channels,
and
so
this
is
the
kind
of
thing
that
allows
a
cell
to
become
B
polarized
and
create
a
action
potential.
But
we've
treated
all
this
as
blocks.
A
This
is
series
of
blocks
and
computational
elements
and
then
there's
this
basically
a
directionality
to
this
so
I
mean
this
is
a
very
stripped
down
version
of
it.
But
this
is
basically
what
we
can
do,
and
so
we
can
actually
simulate
this
a
multiple
scale,
so
we
can
simulate
ion
channels
at
the
single
cell
level.
We
can
add
those
cells
in
the
networks
and
manipulate
all
of
it.
A
You
know
sort
of
a
connectome
model,
so
this
is
C
302,
which
is
another
project
name
allows
you
to
simulate
a
lot
of
the
connectivity
and
the
dynamics
of
it.
So
that's
that's
it.
That's
the
whole
talk
and
then
I
guided
them
to
this
docker
simulation.
So
this
is
the
docker
container
I've
talked
about.
This
is
actually
available
on
the
open,
worm,
github
and
you
can
download
it
and
it's
you
know
you
can
use
it
in
in
Linux.
A
There's
no
windows
version
yet,
but
and
then
you
can
visit
all
of
our
little
outlets
media,
let's
github
Twitter,
YouTube
and
joint
slack
and
then
senior
contributors,
and
there
are
a
lot
of
other
contributors
and
I
mentioned
that
you
know
it's
like
hundreds
of
people
working
towards
the
school
and
then
I
encouraged
people
to
join
in.
So
that
was
the
that
I
gave
to
this
group
of
people
was
pretty
well-received.
A
So
let
me
look
at
the
comments.
Oh
could
you
post
your
presentation
with
some
of
your
word
explanation
sure,
as
this
talk
will
be
on
YouTube
and
then
I'll
send
out
the
links
to
the
slides,
and
then
you
can
use
the
chaos
theory
for
a
type
of
Turing
test,
so
yeah
you
could
use
that
yeah.
You
could
look
at
like
how
the
different
like
people
looked
up
like
we
have
movement
data
so
like
I
know,
my
hope
has
been
looking
at
the
movement
data.
You
know
kind
of
simulating
it,
but
it
exists
in
this
database.
A
We
can
actually
decompose
some
of
the
movement.
The
movements
are
pretty
stereotyped
in
C
elegans,
but
I
know
people
have
done,
like
you
know,
looked
at
other
organisms
like
looked
at
like
traveling
patterns,
there
are
a
lot
of
theories
about
wake
levy,
flights
and
things
like
that.
That
happened
in
movement
of
organism,
so
they
explore
a
little
bit
of
space
and
then
they
move
on
like
they
take
us
transit
to
another
place
on
the
explore
they
transit
to
another
place,
and
there
are
a
lot
of
you
know
ways
you
can
characterize
that
with
chaos.
A
A
D
A
D
A
A
Intermediary
behaviors
that
are
not
well
explored,
they
have
class
and
they
have
like
the
encounter
may
be
a
stimulus
to
back
up
and
then
you
know
maybe
make
terms
but
other
than
that
we,
you
know
there
are
all
sorts
of
things
that
we
don't
really
I.
Don't
think
people
of
I
think
there
are
a
lot
of
behaviors
that
are
characterised
how's
that
so.
D
A
Using
less
noise
grows
up
slower
doing
my
reading
queue
that
I
didn't
get
to
today,
but
that's
you
know.
That
was
something
interesting
though
so,
thanks
for
attending
today,
I
think
everyone
is
doing
really
well
and
you're.
Staying
safe
and
we'll
talk
next
week.
Next
week
we
have
updates
on
visa
and
then
maybe
we'll
have
some
presentations
I
pressure
on
maybe
some
other
people
so
have
a
good
week
all
right,
bye,
the
bucking
our
exams,
my
own.
Yes,.