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From YouTube: DevoWorm (2021, Meeting 20): OpenWorm turns 10! Open-source Community, Info Processing, and Symmetry
Description
Review of GSoC Community Period and Open-source Community Resources, OpenWorm turns 10!, presentation on Mainak's project (Improving DevoLearn), discussion about interpolating microscopy images/data, developmental game theory preview. Paper review on information processing in cells and symmetry in development. Attendees: Jesse Parent, Richard Gordon, Susan Crawford-Young, Bradly Alicea, Shruti Raj Vansh Singh, Assaf Wodeslavsky, Akshay Nair, Ujjwal Singh, and Mainak Deb.
B
B
B
Period
are
you
coming
along
with
it?
Yes,
it's
actually
going
better.
Okay,
yeah!
I
know
we've
exchanged
some
messages
and
slack
about
it.
Yeah.
It
looks
like
you're
doing
pretty
well
yeah
yeah.
So
let's
see,
I
guess,
yeah.
Let
me
start
with
some
community
things.
I
know
people
aren't
really
here
yet,
but
we'll
get
on
with
that
part
of
it
and
then
my
knock
can
give
his
presentation
on
what
he's
in
his
project
and
what
he's
been
up
to
in
the
last
maybe
week
or
two.
B
So
as
you
know,
my
knock
is
the
g
suck
student
for
this
year
and
he'll
be
working
on
the
divo
learn
project.
So
congratulations
to
my
knock,
and
so
one
of
the
things
that
they
do
in
gsoc
is
to
have
a
community
period
which
is
to
get
acquainted
with
the
organization
that
you're
working
with.
So
this
organization
is
actually
open
worm.
B
So
you
know
we
apply
through
incf,
but
openworm
is
our
sponsoring
organization.
So
we
have
our
slack
open
room
slack,
but
today
I'm
going
to
talk
a
little
bit
about
some
of
the
other
things
that
we
have
in
the
community
period
and
you
can
follow
along
with
this.
I
just
wanted
to
promote
some
of
our
community
resources
during
this
time
and
this
this
community
period
lasts
for
three
weeks
so
we're
in
week
two
of
the
community
period.
B
So
I've
been
doing
this
for
a
couple
years
and
one
of
the
things
that
we've
done
so,
let's
see
one
of
the
things
that
we've
done
is
we've
gone
over
the
different
materials,
getting
people
integrated
into
open
worms.
So,
of
course
we
have
the
slack,
but
we
also
have
all
the
other
projects
that
are
going
on
so
we're
diva
worm
here
and
that's
a
developmental
oriented
project,
and
so
we
did
this
open
house
in
2016,
and
so
it's
a
little
bit
dated.
B
But
I
think
that
the
basic
information
still
holds
here,
so
we
have
different
projects
that
are
going
on
in
open
arms.
We
have
movement
validation,
we
have
geppetto
c302,
which
are
actually
two
projects,
but
we
have
they're
kind
of
feed
off
of
one
another.
A
lot
of
simulation
nervous
system
simulation
worm
simulation
in
that
group,
then
there's
wormsim,
which
is
another
initiative.
B
It's
developed
by
some
people
in
siberia
who
work
with
the
project
and
I'll
kind
of
go
through
all
these
different
projects
as
we
go.
So
these
are
just
some
of
the
projects
actually,
because
you
know
some
of
the
projects.
Some
of
the
other
projects
that
aren't
featured
here
are
sort
of
side
initiatives.
B
B
So
basically,
you
have
to
go
through
the
set
of
exercises,
submit
something
and
then
you're
approved,
and
then
you
get
the
badge
and
it's
just
a
little
micro
credential.
But
you
can
learn
a
lot
about
some
of
these
things
as
you
go
along
so
and
then,
of
course,
there
were
in
this
open
house
that
we
had.
There
were
two
tutorials.
B
There
was
a
first
tutorial
on
morphozoic,
which
is
the
platform
that
tom
portages
developed
and
he's
in
in
the
group.
He
was
here
a
couple
weeks
ago,
and
this
is
a
cellular
automata
model
of
development
or
a
pattern
pattern
production,
but
you
know
you
can
apply
it
to
development
and
cell
modeling,
which
is
a
common
theme
in
open,
open
worm.
B
We
focus
on
modeling
cells,
so
that
was
something
that
I
think
stephen
larson
put
on
as
a
tutorial,
for
you
know
how
to
model
cells
and
he's
actually
been
pretty
successful
at
creating
a
startup
related
to
like
biological
data
and
cell
modeling,
and
things
like
that
in
in
neuroscience
data.
So
his
phd
is
in
neuroinformatics,
and
so
that
was
his
that's
his
area.
B
B
So
if
you
want
ever
wondered
where
the
origins
of
open
arm
come
from,
we
we
actually
had
in
the
slack
we
had
a
discussion
about
when
someone
asked,
when
is
the
10th
anniversary
of
open
worm,
and
so
there's
a
page
on
the
wiki
or
on
the
in
the
docs
of
open
worm,
and
I
don't
know
if
this
is
it
or
not?
Okay,
this
is
the
other
thing.
So
there's
a
page
in
the
docs
on
the
history.
B
It's
I
think
it's
full
history
or
something
like
that
and
the
full
history
gives
all
these
dates.
So
this
is
from
the
full
history
doc
page-
and
this
is
so
back
in
2010,
giovanni
udely
who's
pictured
here
and
stephen
larson
kind
of
make
calls
in
parallel
for
involvement.
In
this.
You
know
we
want
to
model
the
worm.
B
We
want
to
create
this
model
of
c
elegans,
because
c
elegans
was
already
a
well-known
model
organism,
and
so
they
wanted
to
do
something
that
was,
you
know,
a
computational
sort
of
analog
of
that,
so
they
were
kind
of
working
independently
giovanni's.
In
london
and
stevens
in
boston-
or
I
guess
at
the
time
I
don't
know
where
he
was,
but
he
has
you
know
he
has
a
lot
of
connections
in
boston,
they're
kind
of
doing
this
separately.
B
B
It's
in
siberia,
andre
and
sergey,
and
they
developed
these.
They
decided
to
join
forces
with
open
worm,
got
the
cybernetic
project
component
into
openwork.
So
there
were
a
lot
of
different
components
that
started
to
come
together
around
2010
2011,
and
so
this
is,
you
know
again.
There
are
other
people
who
are
involved
in
this
mateo
and
other
people
and
you'll
meet
these
people.
If
you
look
around
the
slack,
you'll
see
them
in
the
general
channel,
for
example,
and
then
so
then
they
started
to
produce
articles.
B
So
it's
you
know
it's
interesting.
I
actually
joined
along
with
dick,
and
I
started
this
group
back
in
2014,
so
we
were
a
bit
late
to
the
open
arm
game,
but
we're
still
pretty
you
know,
still
fairly
early
given
where
we
are
now.
But
the
date
I
think
we
should
remember
here,
is
that
september
2011
open
norm
release
one
happened,
so
there
was
a
formal
release
of
open
worm
that
happened
in
september
2011.
B
B
So
you
know,
there's
open
worms
done
a
lot
in
10
years,
and
this
was
a
response
I
got
here
in
the
slack
channel.
This
discussion
kind
of
came
up
with
this.
This
is
the
full
history.
From
this
the
same
thing
I
showed
you
and
giovanni
responded.
That's
pretty
accurate
one
year
before
that
we
were
throwing
the
idea
around
on
twitter.
We
started
getting
on
skype,
calls
only
weeks
before
finding
cyber
elegance
and
that's
when
we
met
andre
and
sergey
them
feels
so.
D
B
So
I
mean
there
there
there
have
been
attempts
at
this
before
and
open
worm
is
kind
of
stuck
with
it
for
about
10
years.
So
hopefully
you
know
in
the
next
10
years
there
will
be
some
sort
of
really
gets
big
big
things
happening,
but
we'll
see
so,
let's
see
in
the
chat
we
have
dick
said
predecessor
says
cyber
worm.
This
is
something
that
dick
cited
in
his
book.
I
don't
know
what
cyber
work,
where
cyber
the
origins
of
cyber
worm.
B
But
this
is
the
link
here.
This
is,
I
think,
in
his
booker,
oh
yeah,
there
were
predecessor
groups,
and
so
I
wanted
to
go
through
that
history,
because
a
lot
of
times
you'll
see
people
say
make.
The
public
perception
is
either
that
it's
dead,
open
norm
is
dead
or
that
it's
like
this
sort
of
thing
that
happened,
you
know,
happens
along
the
lines
of
the
singularity,
which
is
to
say
it's
not
really
based
in
doing
like
science.
B
It's
kind
of
like
this
visionary
thing
that
you
know
so
so
you
know
I
just
wanted
to
give
a
good
little
history.
I
think,
for
the
10th
anniversary.
We
should
probably
try
to
do
something
we'll
be
chatting
over
the
summer
and
the
amongst
the
senior
contributors
and
maybe
we'll
see
where
that
leads,
but
maybe
people,
if
you're
interested
in
something
doing
something
for
the
10th
anniversary.
B
We
can
do
things
like
you
know,
really
kind
of
promote
some
of
the
ideas
that
have
been
developed
in
open
worm
and
see
where
we
can
take
some
of
those.
You
do
blog
posts
or
maybe
even
we'll,
do
a
paper
on
you
know
I
don't
know.
I
haven't
talked
to
anyone
about
this,
but,
like
you
know
some
sort
of
10th
anniversary
paper,
it's
always.
B
C
Okay,
so
is
my
screen?
Physical,
yes,
okay,
so
I'll
get
started
with
the
presentation?
Okay,
so
so
my
presentation
is
essentially
about
the
community
bonding
period
about
the
upgrading
development
project,
which
is
project
3.1
for
this
year's
gsoc.
So
I'll
go
ahead
and
get
started.
Okay.
So
for
a
quick
recap,
devolent
is
basically
it's
basically
a
collection
of
computational
learning
models
which
are
used
to
accelerate
developmental
biology
research
and
it's
actually
mainly
focused
on
the
embryogenesis
of
c
elegans.
C
So
mayu
was
the
one
mayuk
and
ujjwal.
I
guess
so.
These
two
were
the
ones
who
started
this
project
last
year
in
their
gsr
project,
and
now
it
has
its
own
github
repository
and
also
it
has
been,
and
it
also
actually
has
a
wi-fi
package
of
its
own,
so
okay.
So
that
leads
me
to
this
slide,
which
which
talks
about
the
goals
of
my
project,
which
is
project
3.1.
C
So
the
major
goals
for
this
project
would
be
to
upgrade
the
existing
to
upgrade
the
existing
models
that
we
have
and
to
improve
the
usability,
then
to
add
some
more
useful
models
and
plus
we'll
also
try
to
get
some
interactive
online
demos
and
maybe
even
work
on
a
gui,
which
would
be
great,
I
guess
so,
plus
after
the
coding
period,
like
I
don't
think
I'll
be
put
I'll
start
after
the
coding
period
begins,
because
I
was
thinking
of
starting
the
blog
post
in
this
week
itself.
But
from
next
week
onwards
I'll
be
uploading.
C
My
link,
it
would
be
like
a
detailed
blog
post
in
my
website,
which
is
linked
here,
so
so
the
goals
for
this
coming
for
this
community
bonding
period.
Okay.
So
the
four
major
goals
for
this
period
for
me
would
be
to
interact
with
the
mentors
and
the
members
of
the
community,
then
to
clear
out
the
uncertainty,
the
uncertainties
in
the
project
and
then
I'll
also
try
to
read
some
more
of
the
publications
that
are.
C
C
12Th
is
when
the
phase
one
evaluation
will
happen
and
august
16th
is
when
the
final
submission
deadline
is
and
feel
free
to
suggest
changes
and
additions
to
the
project,
even
if
they
require
me
to
work
well
beyond
these
deadlines,
because
they're
there
to
just
keep
track
of
my
work,
but
like
I'll
I'll,
like
I'm
sure
I'll,
have
time
to
work
even
further
from
these
deadlines,
so
okay.
So
that
brings
me
to
the
end
of
my
presentation
and
I
guess
thank
you
for
yeah.
I
guess
that's
the
end.
Thank
you.
B
Yeah
joel
says:
congrats
manok
yeah
and
then
I
think
we
have
some
more
I'll
get
to
the
cyber
room
stuff
in
a
minute,
but
so
yeah.
I
had
a
couple
of
comments
on
that.
The
first
is
that
let's
see
go
back
a
couple
slides,
maybe
okay,
yeah,
that's
alright!
Go
forward
a
couple:
slides:
okay!
Next
one
yeah:
okay,
that's
good
yeah!
So
this
is
the
structure
basically
kind
of
wrapping
up
community
period
stuff
this
week
and
then
getting
into
coding
and
sort
of
hitting
the
ground
running.
B
C
So,
according
to
that
timeline,
the
first
thing
would
be
to
actually
like
to
get
some
feedback
from
you
mayuk
and
which
one
according
like
regarding
the
project
and
the
existing
code,
and
I've
actually
been
talking
to
you
for
the
past
week
like
to
just
clear
up
my
approach
and
what
sort
of
the
loopholes
that
he
thinks
that
should
be
fixed
first.
So
that's
what
I've
been.
B
That
need
to
be
done,
and
so
yeah
just
keep
in
contact
with
us
about,
like
things
that
I
mean
you
do,
have
the
sort
of
the
timeline
from
what
you
wanted
to
do.
You
know,
but
of
course
those
timelines
will
change
quite
a
bit
over.
You
know,
as
the
project
develops
and
you've
had
a
couple
of
things
in
the
slack
it
do
you
have
your
slack
open.
Could
you
bring
that
or
I
don't
know
if
you
want
to
show
that
you
had
some
shirts.
A
B
C
C
So
this
is
something
that,
like
okay,
so
I'll
start
from
the
bottom.
So
like
the
data
sets
that
we
have
essentially
the
videos
like
they
are
essentially
just
video
files,
so
the
video
files
they
basically
shot
on,
like
I
don't
think
they're
short
on
seven
frames
per
second,
but
the
files
are
compiled
in
seven
frames
per
second.
C
So
I
don't
think
the
frame
rate
difference
is
apparent
here,
but
I'll
share.
The
video
I'll
share
the
video
soon
in
the
chat,
so
there,
the
frame
rate
difference
would
be
apparent.
So
the
video
to
the
left
is
playing
in
seven
frames,
a
second
but
the
video
to
the
right
it
it
it.
It's
playing
three
frames,
a
second,
so
essentially
it's
the
same
data,
but
it's
actually
interpolated
to
have
more
frames
per
second.
C
B
D
C
B
B
C
C
I
have
to
refactor
their
code
and
what
this
is
doing
is
basically
it's
taking
seven
frames
and
it's
actually
interpolating
them
into
60
frames
like
while
retaining
the
data
itself
like
it,
doesn't
form
any
unnecessary
artifacts
like
the
the
data
there
is
intact,
but
it
has
more
frames.
That's
the
advantage
here
and,
as
you
can
see,
the
video
is
way
smoother
on
the
right
side.
So,
let's
is
it
using
some
sort
of
gas
network
like
how,
like.
C
No,
it's
not
actually
a
generative
network,
it's
actually
using
it.
It
actually
also
has
something
called
draft
okay.
So
what
that
draft
does
is
that
it?
It's
actually
a
very
fast
algorithm
to
calculate
the
optical
flow,
so
it's
so
it
actually
takes
the
so
it
actually
takes
the
optical
flow
in
between
the
frames
plus.
It
takes
the
two
frames
as
the
input,
the
frame
previous
and
the
frame
next
to
it
and
it
and
it
actually
uses
these
three
channels
and
it
tries
to
compute
a
frame
in
the
middle,
so
that
works
like
it
like
it.
C
D
C
B
So
yeah
so
following
up
on
something
usually
mentioned
so
the
the
coming
weeks,
when
we
maybe
next
week,
we'll
start
you
know
during
the
coding
period
we
usually
have
updates
by
the
student,
so
my
knock
would
be
giving
a
weekly
update
to
the
group
on
his
progress.
So
it
wouldn't
have
to
be
anything
formal.
Just
like
you
know,
we'll
ask
you
what
do
you?
B
What
did
you
do
this
week
and
then
you
would
summarize
it-
and
I
just
want
you
to
be
prepared
for
that,
because
we
want
to
make
sure
that
you
know-
and
this
is
something
that
I
think
that
they
recommend
for
g
suck
in
general.
Just
kind
of
you
know
if
you
do
like
a
blog
post,
you
might
bring
that
to
the
meeting
and
say
this
is
basically
walk,
walk
us
through
it.
You
know
and
then
maybe
put
a
paper.
You
know.
Maybe
if
there's
a
paper
you
reference
or
there's
a
paper.
B
B
Then
we,
you
know
we
want
to
know
about
that
and
and
sort
of
solve
that
problem
before
you
spend
too
much
time
on
something.
These
are
just
strategies
that,
like
you
know,
we've
found
in
the
last
couple
years.
You
know
people
doing
different
projects.
You
know
you
might
have
a
problem
with.
You
know:
computational
resources,
although
you
know
I
don't.
I
don't
know
if
that's
gonna
be
too
much
of
a
problem,
but
you
know
maybe,
with
some.
B
Not
working
the
way
you'd
wanted,
and
so
we
want
to
make
sure
that
you
know
that
there's
a
time
pressure
thing
here,
where
you
don't
want
to
spend
too
much
time
on
something
banging
your
head
against
the
wall
and
then
you
know
at
the
end.
Hopefully
you
know
we'll
have
some
nice
things
to
show
and
so
yeah
in
terms
of
the
schedule.
The
other
thing
I
wanted
to
mention
your
your
evaluations
are
just
going
to
be
like
maybe
kind
of
formalities.
If
you're.
B
B
B
C
B
B
You
know
movements
of
cells
and
there
are
other
types
of
things
going
on
like
I
don't
know
if
you'll
observe
calcium
waves,
but
there
are
other
types
of
like
hypothesize
that
they're
differentiation,
waves
and
embryos
and
so
trying
to
find.
But
but
those
are
all
these
sort
of
meta
features
where
they're
things
that,
like
they
happen
between
cells
across
cells,
rosettes
form?
B
If
you
have,
if
you
find
something
you
don't
know
what
it
is,
you
know
ask
maybe
myself
or
dick
or
susan
their
biologists.
You
know-
and
you
know
we
would
answer
your
question,
but
we
also
have
some
reference
reading
materials
you
can
follow
up
on.
So
you
know
if
there's
some
problem,
one
of
the
things
that
has
been
a
challenge,
I
think
for
people
who
come
from
like
a
computer
science
background
is
that
you
know
there
are
a
lot
of
things
in
biology
that
you
have
to
learn
and
they're
kind
of
unfamiliar.
B
So
don't
worry
about
like
if
they're
unfamiliar,
we
can
help
you
find
out
what
it
is,
or
you
know
you
can
read
about.
You
know
we
can
find
the
resource
that
you
need
to
read
about
more
read
more
about
it.
So
so
this
is
the
link
and
then
side
by
side,
comparison
of
input
and
output
notice,
the
frame
rate
difference.
This
is
the
note
on
the
paper
that
my
app
was
mynock
was
talking
about
previously,
so
there's
this
thing
that
came
up
in
the
chat
here.
B
B
Okay,
oh
jesse
says
the
screens
aren't
being
shared
for
you
and
I
think
it's
my
connection.
Well
yeah.
I
don't
know
why.
That's
we
haven't
shared
a
screen
in
a
little
bit
but
yeah.
It
might
be
a
problem.
So
dick
had
this
comment
here
yeah.
So
we
were
talking
before
about
like
the
time
intervals
and
about
sort
of
resampling
the
data
interpolation.
That
sort
of
thing.
So,
let's
see
he
says,
and
all
right
so,
okay,
it
starts
up
here.
B
Actually
so
we
were
talking
before
about
this
cyber
worm
project
nematodes
present
a
unique
opportunity
for
understanding
morphogenesis
and
what
might
be
you
know,
might
be
called
a
middle
out
manner
from
the
cells
and
their
interactions
to
the
organism
and
from
the
cells
down
to
the
mechanochemical
physics
of
the
cytoskeleton
and
dna,
and
this
is
something
I
think
that
involved
dick,
but
also
a
couple
other
people-
okay,
katano
et
cetera
yeah.
So
this
was
a
thing
from
1997..
B
They
would
like
to
write
a
four
dimensional
database
simulation
be
called
cyberworm
as
a
database.
It
would
contain
and
accumulate
coordinates,
cytoskeletal
arrangements,
cell
division
times,
gene
expression
and
other
data.
So
this
is
stuff
that
we've
kind
of
worked
on
in
evoworm
a
bit.
We've
got
some
data
for
that,
and
and
we've
cataloged
it
in
a
sort
of
data
structure,
and
so
the
four
dimensions
are
the
three
dimensions
of
space
and
the
one
of
time.
But
I
think
you
have
other.
B
B
B
Kind
of
goes
on
about
cyber
worm
and
the
chat
if
you
want
to
follow
along.
So
this
is
something
that
was
sort
of
proposed
in
97.
It
didn't.
I
don't
know
if
there
was
a
lot
of
progress
made
on
it,
but
we
obviously
took
a
lot
of
that
and
realized
some
of
it
would
be
evil
worm.
Steve
mcgrew
was
involved
and
he's
been
involved
in
diva
worm,
yeah,
not
just
a
thought.
B
So
this
was
you
know
this
was
early
on
with
this
as
well
before.
Like
you
know,
machine
learning
was
a
thing,
and
so
this
was
so
they
said
here.
Of
course,
it's
dangerous
to
know
it's
good
intentions,
but
this
statement
points
out
that
one
possible
direction
for
future
research
on
computer
synthesis
of
embryos.
Our
goal
is
nothing
less
to
compute
a
worm
than
to
compute
a
worm,
though
I
suspect
the
full
success
is
10
30
years
away,
so
that
was
this
was
in
no
maybe
about
25
years
ago
now.
B
B
B
You
know
I've
done
community
period
for
a
number
of
years,
and
this
is
a
resource
that
I
have
sort
of
put
together
over
that
time.
So
this
is
something
called
open
science
and
open
source.
It's
a
set
of
readings
and
I'll
put
the
link
in
the
chat,
and
I
know
a
lot
of
you
are
interested
in
open,
open
source
and
maybe
even
open
science.
I
don't
know
you
know,
maybe
that
that
set
is
smaller
than
the
open
source
group.
B
D
B
Is
a
nice
collection
of
papers
and
books
that
will
kind
of
give
you
some
ideas
about,
like
you
know
what
kinds
of
things
people
are
thinking
about
in
these
areas,
plus
there's
some
data
science
stuff
in
here
as
well?
So
this
this
one,
for
example,
is
a
paper
on
producing
open
source
software,
how
to
run
a
successful
free
software
project,
and
so
this
is
a
nice
guide.
If
you
really
want
to
know
how,
like
maybe
to
market
your
open
source
software
or
how
to
like
sort
of
develop
the
infrastructure.
B
B
So
there's
a
lot
here
I
mean
these
are
really
big
books,
so
this
is
just
a
reference
library
for
people.
If
you're
interested
in
these
topics,
you
know
you
can
look
at
it
and
just
kind
of
skim
through
and
find
something
you're
interested
in.
This
is
the
field
guide
to
data
science,
which
actually
might
be
interesting.
I
know
that
jesse
in
particular,
is
interested
in
data
science,
and
so
this
is
actually
something
that
you
know
if
you're
interested
in
sort
of
what
data
science
is
and
kind
of
going
through.
B
Some
of
this,
you
know
helping
you
through
that
process
of
sort
of
getting
acquainted
with
it.
This
is
a
nice
resource
and
then
I
have
a
number
of
topics
here.
So
you
know
communities
and
collaboration,
innovation,
just
information
on
licensing,
so
this
is
ryan
merkley,
who,
I
think
is
still
on
the
open
room
foundation
board
talking
about
attribution
and
licensing.
B
So
that's
a
very
important
part
of
software
open
source
software
production,
social
media
facilitates
openness,
working
open
and
together,
and
these
are
some
resources
from
like
in
terms
of
working
in
teams
and
working
on
team
projects,
and
things
like
that.
So
I
I
I
give
this
out
every
year
and
I
hope
that
people
find
it
useful.
I'll
put
it.
I
think
I
put
this
in
the
if
you
are
on,
I
think
it's,
the
g
suck
slack
channel
or
the
devo
learn
slack
channel,
but
I
put
it
in
there.
B
So
if
you're
interested
go,
take
a
look,
here's
the
wool
part
reference,
and
so
that's
development
is
the
egg
computable.
Or
could
we
generate
an
angel
or
a
dinosaur?
So
that's
the
reference
there
and
then,
let's
see
my
knock
said
about
the
video
interpolation
thing
I
showed
I'm
not
sure.
If
the
output
video
is
viable
to
be
used
as
data
yet
needs
more
work,
we'll
need
feedback
from
the
community
yeah,
that's
that's
fine.
I
mean
we
can.
We
can
see
what
it
looks
like
and
then
you
know
kind
of
pull
data
from
it.
B
D
B
F
E
B
E
E
B
D
F
E
Provided
by
bradley,
I
went
through
it
and
the
images
are
like
I.
I
know
it
looks
the
same.
I
I
mean
it's
just
the
white
blob
in
the
center
and
there's
nothing
much
else
like
there's
just
light
coming
from
different
directions,
and
it
totally
looks
different
from
the
paper
you
have
published,
co-authored
like
regarding
the
4d
embryo
region,
reconstruction
reacquisition.
I
guess
that's
the
people's
title,
I'm
not
sure
I
have
to
go
through
that
yeah
so
like
in
the
paper.
Those
images
are
so
clean.
D
E
D
D
D
F
B
Okay,
no
thank
you
and
so
yeah.
That's
so
I'd
like
to
move
on
to
our
submissions
document
just
to
see
where
we
are
with
that.
So
this
is
again
our
submissions
document
and,
having
I
don't
know,
if
we've
made
a
lot
of
changes
in
the
past
week
or
two
weeks,
I
guess
so.
We
have
a
couple
of
outstanding
things.
We
have
a
lot
of
these,
so
these
different
submissions
to
networks,
2021,
are
still
ongoing
and
we'll
be
getting
those
out.
D
B
Be
you
know,
sort
of
designed
by
then
and
submitted
so
that
that
isn't
too
hard
I
mean
we
can
just
kind
of
put
that
together
and
exchange
notes
and
slack.
Let's
see
we
have
couple
of
things.
We
have
the
kindle
book,
the
boring
billion
mathematics
of
diva
worm.
These
are
all
just
sort
of
open
the
williams
williamson
symbiosis
test.
B
B
B
Higher
level
derivatives
from
that
which
they
call
jerkiness-
or
you
know
whether
it's
smooth
you
can
approximate
the
movement
with
you-
know:
lower
dimensional
movement
parameters
or
higher
dimensional
movement
parameters,
that
sort
of
thing-
and
then
this
this
is
an
abstract
that
I'm
going
to
be
submitting
for
today
for
dynamics
days,
europe.
This
is
game,
theory
and
developmental
processes.
B
This
is
something
that
has
been
an
open
paper
in
on
in
the
list
of
open
papers
for
a
while,
and
I
just
decided
to
pull
up
put
together
an
abstract
for
this,
and
so
this
is
something
that's
going
to
be
submitted
to
a
conference
called
dynamics
days.
It's
going
to
be
virtual
and
it's
it's
about
applying
game
theory
to
developmental
processes.
B
So
this
kind
of
goes
through
the
typo.
There
kind
of
goes
through
game
theory
is
applied
to
development,
going
through
some
games
that
you,
you
know
you
might
be
able
to
apply
to
development
and
some
issues
of
sort
of
the
agent,
the
player
of
the
game.
So
these
are
things
that
I
mean.
Hopefully
this
is
sort
of
a
dynamical
systems
approach,
but
also
a
game,
theoretic
approach.
So
it's
it's
an
interesting
way
to
look
at
development.
B
We,
I
did
a.
We
did
a
little
bit
of
this
in
in
we
had
this
paper
in
2018
on
the
cybernetic
embryo
and
so
that
paper
actually
kind
of
previews.
Some
of
what's
going
to
be
in
this
talk
so
just
to
let
people
know
if
you
want
to
be
involved,
I'll,
probably
pull
I'll,
probably
put
together.
The
slides
like
I
think
the
conference
is
later
and
maybe
in
the
early
fall,
so
we'll
have
time
to
put
together
the
slides
on
that
for
people
to
be
involved.
B
I
see
people
left
here,
so
I
don't
know
what
we'll
talk
about
here.
I
didn't
want
to
show
this,
so
this
is
the
earlies
thing
in
early
zebrafish
development,
and
this
is
zebra
fish
rock.
It's
a
twitter
account
that
does
a
lot
of
like
zebrafish
embryos
and
things
like
that.
They
have
a
lot
of
nice
videos
and
and
gifts,
and
things
like
that
of
the
process.
B
So
I
can't
I
don't
know
if
I'm
going
to
be
able
to
get
this
video
to
play
and
I
have
it
in
the
folder,
but
this
is
the
first
22
hours
of
zebra
fish
development
in
one
minute,
so
they've
taken
this
observation
of
an
embryo
for
22
hours
and
they've,
condensed
it
into
a
one
minute
video.
So
it's
a
time
lapse
in
in
in
the
time
lapse.
You'll
see,
like
things
start
to
develop
from
this.
So
this
is
the
this
is
like
the
cell
mass.
B
This
is
like
an
outer
area
and
then
I'm
sorry,
I
don't
remember
the
terminology
here
that
they
use,
but
eventually
you'll
start
to
get
like
this
is
enveloped,
and
then
you
start
to
get
the
something
that
looks
like
the
zebra
fish.
So
I
don't
know
if
I
can
play.
Oh,
maybe
I
can
okay
here
we
go
so
you
can
see
the
process
here.
B
B
Okay,
so
that's
22
hours
of
zebrafish
development
and
we
have
in
the
one
of
the
latest
papers
we've
put
out
on
comparing
zebrafish
and
c
elegans
in
that
paper,
there's
a
reference
to
a
reference
database
that
gives
you
all
of
the
different
stages
of
zebrafish
development
and
the
timing,
and
all
that
so,
okay.
So
in
the
chat
okay,
so
we
have
okay.
So
I
need
to
add
something
here.
Dick
said:
add
quantitative
comparison
of
archaea
and
shape
droplets.
B
I
think,
like
with
respect
to
this
submission
this
thing
that's
going
on
for
networks,
the
topo
nuts,
so
maybe
applying
topo
nets,
and
then
this
we
don't
know
where
we're
gonna
submit
this,
maybe
like,
let's
see,
tie
into
topo
nuts,
maybe
maybe
what
we
learned
at
that
session
will
be
informative
here.
But
we
have
this
basically,
this
these
archaea
bacteria
that
have
different
shapes
and
then
droplets
also
shapes,
and
maybe
we
can
characterize
them
in
different
ways,
but.
A
B
B
D
B
B
Okay,
we'll
talk
about
this,
so
this
is
actually,
if
we
go
back
several
months,
there's
a
special
issue
of
philosophical
transactions
b
on
it's
sort
of
on
what
they
call
sort
of
cellular
decision
making
in
non-neuronal
cognition.
So
this
is
the
michael
levin
paper
with
one
of
his
colleagues
and
this
paper
is
called
the
cognitive
lens
of
primer
and
conceptual
tools
for
analyzing
information
processing
and
developmental
and
regenerative
morphogenesis.
B
So
they
take
the
view
that
development
can
be
viewed
as
information
processing.
Much
like
we
view
cognition
and
hence
the
cognitive
lens.
So
the
the
abstract
here
is
that
brains
exhibit
plasticity,
multi-skill
integration
of
information,
computation
and
memory
having
evolved
by
specialization
of
non-neuronal
cells.
That
already
possessed
many
of
the
same
molecular
components
and
functions,
so
they
were
talking
about
neurons
and
maybe
things
that
support
neurons,
but
all
of
those
really
differentiated
from
or
evolved
from
non-aroma
cells.
B
B
To
like
sort
of
a
machine-
or
you
know,
cogs
in
a
machine
that
are
driving
some
process,
so
you
know
cognition
that
word.
It
sort
of
denotes
a
sort
of
goal-oriented
machine
that
produces
some
output.
I
think
you
know
in
cognitive
science
you
learn
that,
like
the
brain
is
operates
like
a
computer
or
it's
a
computer
of
some
type,
and
so
that's
that's
the
sort
of
thing,
and
so
we're
extending
that
analogy
to
cells
and
basal.
Cognition
is
just
simply
that
we're
doing
this
without
a
brain.
B
We're
not
doing
this
with
like
a
lot
of
intention.
It's
just
that.
There's
this
basic
information
processing
that
happens
in
these
systems.
So
then
the
question
is:
how
can
biological
information,
processing
across
scales
of
size
and
complexity
be
quantitatively
characterized
and
exploited
in
biomedical
settings?
B
We
use
pattern
regulation
as
the
context
in
which
to
introduce
the
cognitive
lens
so
that
the
cognitive
lens
is
a
strategy
using
well-established
concepts
from
cognitive
and
computer
science
to
complement
mechanistic
investigations
in
biology
to
facilitate
the
assimilation
and
application
of
these
approaches.
We
review
tools
from
various
quantitative
disciplines,
so
that
includes
dynamical
systems,
information
theory
and
what
they
call
least
action
principles
which
are
sort
of
like
energy
minimization.
B
You
may
have
heard
of
something
called
the
free
energy
principle
which
is
base.
You
know
one
version
of
the
least
action
principles,
so
we
propose
that
these
tools
can
be
extended
beyond
neural
settings,
to
predicting
control
systems,
level
outcomes
and
understand
biological
patterning
is
a
form
of
primitive
cognition.
B
So
that's
an
interesting
view
of
that.
We
don't
really
think
about
biological
patterning
in
that
way,
we
think
of
it.
Well,
we
talked
about
the
reaction
to
fusion
models
of
chemical
morphogenesis.
We've
talked
about
you
know,
sort
of
cellular
automata,
which
are
these
emergent
models
where
you
have
a
lot
of
interactions
and
it
produces
this
thing,
but
they're
actually
arguing
that
there's
another
component
to
this,
and
it's
this
idea
of
primitive
cognition
that
contributes
in
some
way.
B
We
hypothesize
that
a
cognitive
level,
information
processing
view
of
these
functions
can
complement
reductive
perspectives,
which
is
what
many
people
have
talked
about,
reductionism,
which
is
looking
at
component.
You
know
the
parts
and
their
components
and
their
components
in
isolation,
so
you
can
do
like
controlled
experiments,
but
of
course
you
don't
get
the
information
about.
What's
going
on
with
the
system
exploration
of
the
deep
parallels
across
diverse
quantitative
paradigms
will
drive
integrative
advances
in
evolutionary
biology,
regenerative,
medicine,
bioengineering
and
artificial
intelligence.
So
this
is
so.
B
B
They
show
an
example
of
a
genetic
regulatory
network
where
you
go
from
genes
to
proteins
to
some
physics
and
then
to
emergence,
and
then
they
have
a
little
wand,
magic
wand.
Here
over
emergence
which
is
kind
of
a
plant,
you
know
a
lot
of
people.
Think
emergence
is
kind
of
like
magic.
It's
it's!
You
know
it's
a
joke,
but
still
you
go
from
genes
to
proteins
to
this
physics,
which
is
sort
of
a
black
box
here
and
then
emergence
which
is
a
bigger
black
box
of
the
wand
over
it.
B
B
B
B
And
if
you
are
then
there's
this
predictive
versus
non-predictive
distinction,
which
means
you
can
extrapolate
or
you
can't
extrapolate
and
extrapolate
means,
I
guess
extrapolate
from
the
environment.
So
there's
this
predictive
aspect
and
then
this
is
where
you
get
into
the
cognitive
realm,
because
cognition
is
a
lot
of
times
about
prediction
and
predicting
things.
B
So
this
is
the
predictive
step
and
then
there's
this
next
step.
If
it's
predictive,
it
can
be
different
types
of
predictive,
so
it
can
be
first
order,
second
order
or
whatever.
This
is
like.
You
know,
if
you're
thinking
about
like
predictions
thinking
about
thinking
about
predictions
and
so
forth.
So
if
you're
just
kind
of
thinking
about
you
know
if
you're
just
making
a
prediction,
you
might
predict
that
some
something
is
over
there
in
the
environment
versus
over
here,
but
a
second-order
prediction
would
be
thinking
about
that
prediction
and
improving
upon
it.
B
So
that's,
let's
see
if
there
are
any
more
nice
pictures
in
here.
Oh
they
just
kind
of
get
into
some
of
these
tools.
So
this
is
mapping
between
various
tools,
and
this
is
kind
of
like
we
kind
of
think
about,
like
you
know
how
to
link
things.
We
know
from
cognition
like
decision
making
and
memory
and
allostasis
homeostasis
adaptation
and
learning.
These
are
called
cognitive
phenomena
and
then
you
map
those
to
determinate
what
they
call
deterministic
tools
which
are
either
dynamic
or
algorithmic.
B
So
the
dynamical
tools
are
from
dynamical
systems
theory,
so
it
could
be
attractors
or
ultra
stability
or
feedback
or
back
propagation
feedback
control.
I'm
sorry
or
it
could
be
algorithmic.
So
you
have
kamogorov
complexity
and
algorithmic
dynamics,
and
so
you
have
these
tools
that
map
to
these
phenomena,
and
then
there
are
these
statistical
tools,
which
is
the
third
realm
that
allow
you
to
sort
of
you
know
kind
of
make,
maybe
tease
out
some
of
the
predictions
or
looking
at
how
predictions
are
made
in
these
systems.
B
So
you
have
things
like
bayesian
inference
and
variational,
free
energy
and
mutual
information,
and
so
all
those
you
can
use
those
tools
and
they're
just
methods
to
analyze
for
to
look
and
see
if
their
predictions
being
made
and
how
well
they
map
to
what's
predicted.
So
you
have
maybe
what's
observed
in
the
system
versus
what
you
might
predict
using
one
of
these
statistical
models
or
you
have
one
of
these.
You
know
you
have
say
a
cell
making
a
bunch,
you
know
behaving
as
it
does
and
then
looking
at
you
know
saying
well.
B
This
is
something
that
you
know.
This
is
an
experiment.
We
can
show
that
the
organism
is
a
memory
or
that
it's
adapting.
Where
has
it's
making
decision
making
decisions
and
we
can
map
these
to
observational
tools
like
a
an
attractor
basin,
or
you
know
back
propagation
technique
to
sort
of
sort
of
validate
these
things
as
behaviors.
B
B
B
Well,
both
of
these
are
so
if
you're
interested
in
this
go
ahead
and
check
those
out
and
here's
the
link
to
the
drive
and
we'll
do
one
more
paper
before
we
go
today,
and
that
will
be
this
one
here:
symmetry
transformations
in
metazoan
evolution
and
development.
So
this
is
a
paper
in
the
journal,
symmetry
and
the
abstract
reads.
As
such.
B
In
this
review
we
consider
transformations
of
axial
symmetry
metazoan
evolution
and
development,
the
genetic
basis
and
phenotypic
expression
of
different
axial
body
plans
and
eventually
the
main
symmetry
types
and
metazone
body
plans
such
as
rotation,
so
there's
radial
symmetry,
which
is
where
you
have
a
symmetry
around
a
radial
organization.
So
you
have
like
four-fold
and
eightfold
symmetry,
and
so
you
can
think
of
that
as
being
around
us
in
a
circle.
B
A
B
B
Some
genetic
mechanisms
of
axial
pattern,
establishment,
creating
a
coordinate
system
of
the
metazoa
body
plan,
bilateral
segmentation
and
left
right.
Symmetry
asymmetry
are
analyzed,
so
they
kind
of
go
through
some
of
these.
They
create
a
coordinate
system
for
some
of
these
types
of
symmetry
here
in
the
paper
data
on
the
crucial
contribution
of
coupled
functions
of
the
wind,
bmp
notch
and
hedgehog
signaling
pathways.
B
We
talked
about
hox
codes,
I
think
a
couple
weeks
ago,
and
these
have
to
do
with
different
genes
in
this
family
of
genes
that
are
sort
of
collinear
on
the
on
the
chromosome
that
are
sort
of
physically
adjacent
and
they
map
to
different
segments.
So
like
segments
of
the
backbone,
for
example,
where
segments
of
the
body
would
have
their
own
hox
gene
and
each
hox
gene
would
regulate
those
segments.
B
B
The
lost
body
plans
of
some
extinct,
edicarian
and
early
cambria
medicines
are
also
considered
in
comparison
with
axial
body
plans
and
posterior
growth
in
living
animals.
So
they
kind
of
get
go
back
again
to
that
those
very
early
body
plans
and
very
early
embryos
that
we
talked
we've
been
talking
about
off
and
on
for
a
number
of
weeks,
so
they
talk
about
symmetry
being
the
basic
feature,
what
they
call
the
ball
plan
or
the
body
plan
for
different
metazoan
groups.
B
Clades
is
just
another
word
for,
like
a
phylogenetic
group,
symmetry
of
biological
structures
can
be
defined
as
a
repetition
of
parts
in
different
positions
and
orientations
to
each
other.
So
you
have
these
different
types
of
symmetry,
but
also
many
biological
objects
show
scale
or
fractal
symmetry.
B
B
These
are
different
versions
of
this
and
well
they
talk
about
bilateral
here
directly,
but
you
have
other
types
of
symmetry,
especially
radial
symmetry.
The
author
reason
that
the
particular
cases
of
rotational
and
reflectional
symmetries
are
sufficient
for
biologists.
A
little
more
general
theory
of
organism.
C
B
You
know
not:
all
organisms
are
completely
symmetrical.
Our
bodies,
for
example,
are
not
completely
symmetrical.
There
are
some
asymmetries
in
like
the
heart
or
in
some
of
the
organs,
and
so
that's
something
that
you
know
there
are
other,
also
other
axes
of
symmetry
in
the
organism.
So
this
is
something
that
they're
trying
to
explore
as
well,
and
so
this
is
an
example
here
of
a
scheme
of
body
symmetry
transformation
in
planarians.
So
this
is
this
flat
worm
again
that
we
saw
with
michael
levin's
work,
and
so
this
is
the
scheme
for
body
symmetry.
B
B
B
You
have
these
blastomeres
and
they're
being
displaced
during
this
period
of
cleavage,
and
then
this
corresponds
to
shell
twisting
in
this
shell.
So
you
have
this
process
and
development
of
twisting
and
then
you
can
observe
this
twisting
in
the
cell-
and
this
is
you
know,
this
is
a
symmetry
transformation,
at
least
the
way
they're
viewing
it.
B
This
is
a
fractal
colonial
organization
of
the
rhizocephal
and
barnacle
inside
the
host
crabs.
This
barnacle
here
actually
grows
in
this
around.
In
this
way,
it's
a
fractal
structure.
It
looks
like
a
plant
root
kind
of,
but
it's
a
fractal
growth
pattern,
and
this
is
of
course,
a
very
complex
form
of
symmetry
where
you
have.
B
These
fractal
branching
patterns
is
the
main
mode
of
growth,
so
they
have
these
trophic
roots
here,
and
this
is
these:
are
genetic
changes
in
morphology
and
fractal
dimension
value
for
spinal
neurons,
so
this
is
in
the
cherry
salmon,
and
this
is
these
are
after
the
first
and
second
year
of
fish
life.
So
you
can
see
that
the
in
the
first
year
the
neurons
are
less
branched
and
in
the
second
year
they
start
to
get
more
branches.
B
This
is
a
medusa,
so
it
has
this
radial
symmetry
and
it
has
this
sort
of
structure.
This
is
the
center
point
of
the
symmetry,
so
you
have
different.
You
have
different
segments
that
come
out
like
this
from
the
center,
so
you
have
four.
I
guess
four
fold
symmetry
here
and
these
are
not
necessarily
identical,
but
they
basically
have
the
same
sort
of
plan.
So
you
have
like
the
same
sort
of
blueprint
if
you
want
to
use
that
word
and
you
apply
it
four
times
in
rotation.
B
This
is
branching
from
the
center
point
that
comes
out
and
then
they're
able
to
map
it
to
a
presentation
of
what
they
call
fractal
tree.
So
these
fractal
trees
are
mathematical
tools
that
they
can
use
to
look
at
these
bifurcations
in
this
branching
system
here.
So
it's
it's
radially
symmetric,
it's
this
four-fold
symmetry,
but
you
can
actually
model
this
using
a
fractal
tree
and
the
lengths
of
the
branches
have
some
relevance
to
their
function
and
you
can
actually
look
at
how
these
branches
occur
and
they
occur
across.
B
So
these
branches
aren't
uniform
across
each
of
these
symmetries,
but
they
basically
have
a
similar
structure,
but
it's
not
identical.
So
that's
and
I
mean
the
paper
is
a
little
bit
longer
and
that
has
a
lot
of
information
in
it.
So
if
you're
interested
check
it
out,
so
finally,
let's
see
a
lot
of
things
in
the
chat
here.
I
think
I
think
dick
posted
a
couple
things.
He
posted
some
things
on
the
theory
of
fox
genes,
so
these
are
some
papers
on
that
sure
he
says.
B
Can
we
talk
yeah,
so
shirty
and
dick
will
talk,
I
think,
did
they
miss
file,
phylo
tactics,
phylotactic
symmetries,
I'm
not
sure
I
don't
I
they
may
have,
but
I
didn't
read
the
whole
paper.
I
just
kind
of
went
through
it,
but
they
may
may
not
have
considered
that
cyanide
also
has
a
checkerboard
symmetry
so
sign
out.
As
an
author,
I
think
scinet
works
with
plants
yeah
and
so
there's
also
checkerboard.
Symmetry
fractal
pattern
shown
is
not
self-similar,
so
that's
yeah.