►
From YouTube: DevoWorm (2021, Meeting 8): GSoC prep, Digital Microsphere, Works-in-Progress, Forces and Stemness
Description
DevoLearn Inference Engine discussion, details of the Digital Microsphere, upcoming submissions and deadlines, Gene Regulatory Networks, Mechanotransduction and Cell Fate, and the Bioinformatics of Protein-Protein Interaction Networks. Attendees: Susan Crawford-Young, R Tharun Gowda, Bradly Alicea, Mayukh Deb, Mainak Deb, Jesse Parent, Richard Gordon, Markus Heimerl, Akshay Nair, Ujjwal Singh, and Shruti Raj Vansh Singh
A
B
A
A
A
A
A
D
A
B
A
Were
interested
in
coming
in
welcome
to
the
meeting
we're
starting
to
get
people
for
gsoc
inquiring,
so
we're
in
the
actually
I'll
go
through
the
slack
channel
in
a
minute
and
kind
of
go
through
some
of
the
new
people
and
some
of
their
inquiries.
I
think
that'll
be
good.
I
also
want
to
go
over
the
devo
learn
issues
and
that-
and
we
have
some
deadlines
of
course
coming
up,
and
so
we
have
a
lot
of
things
going
on
today.
A
A
All
right,
that's
actually
something
we'll
do
later,
but
we
have.
This
is
the
open
worm
slack
and
so
this
is.
We
have
two
channels:
diva
learning,
diva
worm,
and
so
in
the
diva
worm
channel,
we
have
xj,
who
said
he's
interested
in
doing
a
project.
I
don't
know
he
wanted
me
to
describe
digital
microsphere,
which
is
the
one
that
susan
is
with
one
that
deals
with
susan's
data
and
tiling
it,
and
so
he
wanted
to
know
a
little
bit
about
it.
A
So
we'll
probably
go
over
that-
and
I
know
that
christian
is
also
interested
in
that
project.
So
maybe
we'll
talk
a
little
bit
about
that
in
a
minute,
so
we
have
again
he
wants
to
know
he
wants
to
know
a
little
about
biology
and
a
little
bit
about
the
project.
So
I
told
him
if
he
wanted
about
biology
to
come
to
the
meetings,
and
you
know
I
used
to
give
people
a
reading
list,
but
maybe
that's
not
the
best
way
to
do
it.
A
We
also
have
the
meetings
that
kind
of
immerses
people
and
things,
so
we
also
have
alexander
barrons
and
shivank
shibank
is
here,
satyam
is
here.
So
there
are
a
couple
people
in
the
new
people
in
the
slack
channel
and
then
in
the
diva
learn
channel,
which
is
different.
This
is
more
focused
on
the
diva
learn
platform.
A
A
C
Okay-
it's
not
much
but
like
it,
doesn't
make
divorce
better
in
any
way
right
now.
But
what
it
will
do
is
that
it's
like
a
blueprint
like
like
soon
we'll
have
much
more
models.
Maybe
7
10
15
models,
but
then
we
will
need
a
blueprint
to
be
able
to
debug
all
the
models.
So
it's
basically
a
blueprint
like
until
now.
All
the
models
are
independent
of
each
other,
but
we
want
them
to
fall
under
a
common
class.
So
yeah
it's
just
easier
for
the
contributors.
A
Yeah,
I
think
that
would
be
good.
I
was
reading
it
and
it
was
trying
to
figure
out
what
you
were
thinking,
and
so
this
is
like.
If
we
take
a
bunch
of
models
and
we
put
them
into
the
platform,
we
want
to
be
able
to
check
to
see
that
they're
all
there's
some
compatibility
or
that
they're
all
basically
working
yeah,
exactly
yeah.
A
A
C
Organizations
like
since
at
least
two
years
and
yeah-
so
I
I
don't
know
if
you
have
part
of
this
organization
called
kde.
I've
been
I've
been
a
regular
contributor
since,
like
I
know
like
and
yeah,
I
have
been
mainly
focusing
on
the
website
of
this
from
the
starting.
Then
later
I
moved
on
to
this
application
called,
which
is
a
drone
controlling
application.
C
C
D
A
A
So
yeah
we
have,
we
have
actually
the
so
the
gsoc
projects.
I
think
this
the
application
so
like
they've,
been
out
in
in
public
for
a
while,
but
the
application
period
doesn't
really
start
until
beginning
of
march.
I
think
maybe
march
8th
or
9th.
So
that
means
that
coming
up
people
will
be
making
applications.
A
They'll
be
writing
them
up,
and
this
year
it's
a
little
bit
different
last
year
it
was
like
a
whole
summer,
so
this
year
it's
like
10
weeks,
so
they've
shortened
it
by
about
six
weeks,
and
that
means
that
it's
going
to
be
a
little
abbreviated
in
terms
of
how
much
you
can
get
done
and
then
people
are
going
to
have
to
think
about
that
in
terms
of
their
schedule.
So
you
know
a
typical
gsoc
application
is
where
you
have
the
main
body
of
the
application,
and
then
you
have
you
go
through.
A
Oh,
we
have
who
else
trudy's
here,
hello
shirty,
you
have
the
main
body
of
the
application
where
you
describe
the
problem
and
then
you
get
into
this.
You
have
to
propose
a
schedule
for
doing
it,
and
so
this
is
the
thing
that
I
always
go
through
with
people
every
year.
You
know
you
walk
through
the
schedule
and
you
try
to
figure
out
how
to
implement
the
project,
and
so
that's
always
a
challenge
because
they
always
people
want
to
do
everything
at
once,
or
they
don't
know
how
to
plan
out.
A
Like
you
know,
step
by
step,
and
you
know
you
have
to
design
contingencies
into
the
plan.
So
if
you
know
you
run
against
an
obstacle,
then
you
can
overcome
the
obstacle
work
on
something
else,
while
you're
working
on
the
thing
that
you're
not
quite
getting
and
I've
had
we've
had
this
several
times
come
up.
Where
you
know,
we've
had
problems
with
getting
compute
power.
A
B
A
That's
what
they're
trying
to
go
for
in
the
program?
You
know
they're
trying
to
teach
students
how
to
do
that,
so
I'm
gonna
we're
gonna
have
in
the
coming
weeks.
We're
gonna
have
an
opportunity
in
mainly
in
slack,
but
maybe
also
in
the
meetings
to
discuss
this
and
to
go
through
some
of
the
mechanics
of
an
application,
and
you
know
if,
if
you
need,
if
you
need
me
to
read
things
over
I'll,
read
them
over
and
so
give
you
feedback
on
it.
A
A
A
The
project
for
basil
area
is
to
move
it
to
you
know,
sort
of
new
vistas
as
well.
You
know
improve
the
algorithm,
improve
some
of
the.
What
we're
you
know
getting
out
of
the
we're
sort
of
segmenting
and
we're
moving
more
into
like
identifying
motion
and
things
like
that,
and
then
the
digital
microsphere
is
the.
A
So
this
is
like
susan
has
developed.
This
thing
called
a
flipping
microscope,
which
is
where
you
take
the
embryo
and
you
flip
it
you're
able
to
pull
it
into
the
water
column
or
into
into
like
a
you're
able
to
pull
it
down
in
such
a
way
so
that
it
flips
over
and
you're
able
to
like
just
take
it
a
camera
at
one
perspective
and
image
it
in
different.
You
know,
as
it
turns,
and
so
you
get
a
full
rotational
perspective
of
the
embryo
and
susan.
B
Yes,
well,
I
have
a
flipping
microscope
and
now
I
also
have
a
still
ball
microscope
that
takes
images
from
well
90
degrees
on
a
sphere,
and
I
have
eight
or
well.
I
have
nine
microscopes,
the
bottom
one
is
kind
of
a
holder
for
the
whatever
object,
you're
imaging,
so
I
have
see
if
I
can
anyway.
Yes,
so
I
was
hoping
to
take
pictures
of
living
things
such
as.
B
B
B
B
A
Put
it
into
a
like
a
public
or
a
semi,
you
know
like
a
private
place.
I
have
it
on
my
desk,
like
in
on
the
disc
you
sent
me,
but
you
know
it
is
okay.
I
can
actually,
I
think
I
know
where
to
put
it
now,
so
I
can
send
out
a
link
to
an
intro.
You
know
an
interested
party
or
a
gakshay,
and
I
can
let
him
look
it
over.
A
D
A
A
E
B
They're
they're
kind
of
like
this
and
they're
stuck
in
the
ball
at
all
angles,
and
that's
basically
it
it's
kind
of
simple,
but
it
works.
At
least
I've
got
it
so
that
it
it
it's
taking
pictures
of
a
peppercorn
as
a
dick
suggested,
a
peppercorn.
So
I'm
taking
pictures
of
the
peppercorn
marked
up
with
felt
markers
so
yeah,
I
don't
know,
let's
see
I'll,
try
to
get
an
image
of
it
up.
I
have
one
that
I'm
not
using
for
publication.
D
D
B
A
B
B
B
A
Yeah
and
then
so,
the
idea
is
to
take
those
images
of
like
sort
of
a
total
surface
of
an
embryo
and
then
to
project
it
on
to
a
an
atlas
or
some
sort
of
flat
place
where
people
can
explore
it.
So
I
think
dick
described
it
as
sort
of
like
a
google
maps
or
a
flat
map.
You
know
where
you
take
like
a
globe
and
you
take
the
surface
of
the
globe
and
you
make
a
flat
map
and
you
can
explore
a
map.
E
A
A
A
Yeah,
it
would
be
a
gui
and
of
course
we
can
do
all
these
things,
and
you
know
we
can
figure
out
a
way
to
do
it
in
a
gui
that
we
wouldn't
be
able
to
do
say
with
a
paper
map
or
with
a
you
know,
something
that
isn't
a
you
know,
a
bunch
of
images,
just
kind
of
curated.
You
know
this
would
help.
You
know
we'd,
basically
be
able
we'd
have
a
time
series
of
images.
A
We'd
have
images
from
all
you
know
all
sides
of
the
embryo,
so
you'd
you'd
have
all
those
locations
in
space
and
time
and
then
you'd
want
to
put
them
together
in
the
gui.
In
such
a
way,
people
could
explore
them,
and
you
know
maybe
in
in
sort
of
a
linear
fashion,
but
also
in
a
non-linear
fashion.
So
you
want
to
be
able
to
like
if
I
find
something
at
time
point
x
and
I
want
to
see
something
else
at
time
point
y.
I
see
some
process
going
across
the
embryo
that
you
know
you.
A
It
displayed
in
that
way
it
it,
you
know
it's
and
it
seems
a
little
abstract
in
the
apple
in
the
call
for
projects.
You
know
the
description
of
the
project.
There
are
actually
two
papers
that
are
listed
there
and
those
are
the
papers
that
you
should
read
or
look
through,
and
that
should
describe
a
lot
of
this
stuff.
It
should
make
it
concrete.
A
Just
giving
people
some
information
about
open
source,
why
you
should
contribute
to
open
source,
open
source
developers
and
kind
of
a
cute
picture
about
that,
then
the
summer
of
code
kind
of
describes
the
project
so
again
highlighting
that
it's
a
10-week
project
this
year,
and
then
this
is
the
schedule.
So
we
go
through
from
organization.
That's
not
for
us.
The
student
application
period
is
actually
march
29th
through
april
13th,
but
the
people
often
start
earlier
than
that.
A
B
A
And
then
want
to
contribute
to
diva
learn
so
then
he
has
a
bunch
of
things
that
you
can
do
to
contribute
to
the
project,
so
we
have
determining
which
part
of
your
solution
is
worth
contributing
to
adapting
the
code
and
integrating
the
desired
changes
extracting
the
relevant
code
fragments
these
are
just
all
different
ways
to
contribute
again.
We
might
have
a
we
might
make
this
into
like
a
diagram
too.
I
have
some
diagrams.
I've
been
working
on
in
another
project
that
I
might
use
for
this.
A
So
this
is,
I
mean
this
is
something
that
I
I
don't
know.
We'd
mentioned
like
starting
a
group,
an
onboarding
group,
but
I
think
that
might
be
a
little
bit
too
much
labor
for
people,
so
I
think
probably
something
like
this
would
be
better
to
do
in
terms
of
giving
people
like
when
they
come
to
us
and
ask
about
you
know
how
to
you
know
how
to
get
involved.
A
A
D
A
Yeah
all
right
yeah,
and
then
this
is
something
that
you
know.
We
can
always
give
them
like
a
lot
of
information
or
a
little
bit
of
information.
The
description
is
very
short,
but
I
think
yeah,
it's
actually.
I
think
maybe
one
way
to
do
this
is
to
point
them
to
the
data
that
we
have
for
this,
so
point
them
to
resources.
A
And
maybe
like
point
them,
I
always
say
point
them
to
readings,
but
sometimes
it's
not
sometimes
I
just
like
throw
a
bunch
of
readings
at
people.
It's
like
what
am
I
supposed
to
do
with
this.
I
guess
it's
just
yeah.
I
would-
and
I
also
probably
add
in
something
on
the
application
process
like
what
goes
into
a
successful.
A
Yeah
yeah,
I
think
that's
very
good
yeah.
I
think
that's
good,
so
we'll
update
those
I'll
get
in
touch
with
krishna
and
update
those
slides
and
then
we'll
review,
maybe
next
week
and
we'll
see
we'll
make
sure
that
they're
there
and
you
know
this
will
be
good
for
like
subs,
like
a
template
for
subsequent
years.
So
you
know
we
can
if
we
change
the
projects
next
year.
You
know
we
just
update
that
stuff
and
we
know
how
to
do
it.
So
this
will
be
good.
A
I
think
this
is
a
good
way
to
manage
when
people
come
to
the
organization
and
ask
you
know
what
are
your
projects
and
how
do
you?
How
do
I
get
involved
to
have
this
for
people,
and
then
we
can
put
it
in
the
slack,
invite
people
to
slack
so
we've
been
doing
that,
but
then,
when
they
get
in
the
slack,
just
don't
dump
them
in
the
slack,
give
them
some
back
some
onboarding
stuff
so
that'll
be.
A
A
A
Dick
gave
this
link
to
his
paper
from
2009
on
the
topic,
and
that's,
of
course,
in
the
project
description.
If
you
have
any
papers
related
to
digital
microsphere,
that's
and
then
the
other
paper.
Besides,
the
2009
paper
is
the
susan's
paper
from
2018,
in
which
she
spells
out
this
4d
flipping
microscope.
A
E
A
A
This
is,
of
course,
the
document
that
we're
using
for
different
things
that
we're
doing
in
the
group
we're
submitting
things
to
different
places
all
the
time
and
we're
getting
this
we're
kind
of
keeping
track
of
it
here
and
I
saw
jesse
in
here
last
night,
so
he's
he
was
updating
things.
I
was
updating
things
last
night,
so
this
again,
this
allows
me
put
this
link
in
the
chat,
so
people
have
access
to
it.
A
Okay,
you
can
follow
along,
though
here
so
the
first
thing
is
here
is
this
evolution
2021
venue-
and
this
is
a
conference-
it's
on
evolution,
mainly
there's
some
biology
and
the
deadline
is
actually
a
little
tricky.
I
thought
it
was
march
1st
originally,
but
I
think
the
submissions
open
march
1st
this
is
a
virtual
conference,
so
they're
trying
to
plan
it
out.
I
think,
but
the
submissions
open
today,
so
I
have
to
go
to
the
website
and
check
the
actual
submission
deadline
now
because
I
don't
think
they
headed
up
until
now.
A
So
this
is
evolutionmeetings.org
and
again
this
we
have
two
things:
we're
planning
for
this
kill
the
winners
and
the
euler
paths
for
life
which
I've
talked
about
in
the
meetings
the
kill.
The
winners
is
something
that
krishna
proposed
in
a
previous
meeting
and
so
he's
working
on
that
this
flash
talk
has
been
completed.
The
paper
for
diva
learn
is
in
progress.
It's
just
kind
of
stuck.
I
haven't
had
a
lot
of
time
to
work
on
it.
We
might
think
about
waiting
until
we
get
some.
A
A
A
So
that's
and
that's
so
we'll
be
working
on
that
growth
form
and
the
theory
of
deep
learning.
That's
another
abstract
that
we
don't
really
have
a
home
for
right
now,
but
it
you
know,
there's
going
to
be
something
com
that
comes
up
that'll
fit
the
bill
for
that.
So
we
have
that
sort
of
on
the
back
burner.
But
this
is
the
document
for
this.
So
this
is
the
shared
document,
and
if
you
want
to
join
in
on
that,
you
can,
you
know,
contribute
in
some
way.
A
You
can,
you
know,
add
some
content
or
some
references
or
do
some
editing,
and
so
it's
a
pretty
it's
it's
a
kind
of
an
interesting
tape
and
take
on
deep
learning
and
how
we
might
use
deep
learning
to
model
developmental
biology
and
vice
versa.
We
can
use
developmental
biology
to
inform
deep
learning,
so
I've
noticed
that
there
have
been
a
couple
papers
that
have
been
kind
of
going
in
this
direction.
So
I
wanted
to
stake
out
some
territory
here.
A
A
A
This
is
just
kind
of
in
sort
of
pieces
right
now
we
have
the
document
that
we're
writing
up
and
we
have
a
presentation,
that's
been
made
and
we
have
ideas,
but
those
all
have
to
come
together
in
one
place
and
that
has
to
be
done
by
the
end
of
april.
So
we'll
be
ramping
that
up
in
the
next
couple
weeks,
there's
the
international
c
elegans
conference
which
is
coming
up.
I
don't
think
we're
gonna
submit
anything
to
that.
It's
it's.
D
A
Biologically
oriented,
but
if
you
want
to
attend
this,
it
does
cost.
I
think
a
couple
hundred
dollars,
maybe
well,
maybe
not
for
students.
I
don't
know
what
the
registration
fee
is,
but
this
is
the
c
elegans
conference
that
they
have
every
two
years,
and
so,
if
you
want
to
know
all
about
c
elegans,
you
can
attend
this
conference.
A
The
abstract
submission
and
deadline
is
march
25th,
but
the
registration
is
a
bit
later
than
that.
This
is
like
this
was
something
that
you
would
have
to
travel
to
normally.
So
that's
online
now,
so
it's
an
opportunity
to
do
this
without
traveling,
it's
usually
in
los
angeles.
This
last
year
was
supposed
to
be
in
scotland,
but
they
canceled
the
in-person
version
of
it.
So
so
that's
just
an
aside
steven
learned
paper.
Actually
we
have
this
here,
so
I
don't
know
why
this
is
a
duplicate,
but
make
a
note
of
this.
A
A
This
is
something
that
has
a
deadline
of
march
26th,
and
this
is
a
conference.
If
you're
interested
in
complex
networks,
you
might
want
to
attend,
there's
complement
and
then
there's
networks.
2021,
so
complement
is
one
conference
and
they
have
like
you
can
submit.
I
think,
a
two
to
four
page
abstracts
extended
abstract
and
like
an
eight
to
twelve
page
paper,
so
being
very
ambitious.
A
So
as
networks
2021
is
virtual,
compilnet
2021
is
virtual
and
if
you
know
anyone
interested
in
complex
networks,
so
this
is,
you
know
like
a
lot
of
the
complex
network
stuff
like
brain
networks,
and
you
know
social
networks
and
things
like
that.
They
usually
have
a
wide
variety
of
things
going
on
there
from
like
gene
expression,
networks
to
brain
networks,
to
social
networks,
to
other
really
interesting
types
of
networks
that
you
wouldn't
think
exist.
A
A
A
So
that's
that's
yeah.
For
that,
then
we
have
this
biosystem
special
issue,
so
we
have
periodicity
in
the
embryo,
and
I
know
that
oswal
and
jesse
are
both
on
this
list
of
authors.
So
if
you
want
to
look
over
I'm
getting
close
to
the
resubmission
point,
I
think
we're
getting
there.
So
if
you
want
to
look
it
over
and
add
things
in
you're
welcome
to
do
so,
I
would
you
know,
maybe
some
good
editing,
good
eyes
for
editing
some.
A
You
know
looking
at
what
we
have
and
thinking
through
things
I
think
that's
going
to
help
quite
a
bit,
so
okay
yeah
the
boring
billion,
so
the
boring
billion
is
actually
something
that
we're
coming
up
on
here.
So
darn
one
day's
passed,
boring
billion
is
the
next
one.
So
this
is
something
that
we
talked
about
a
couple
weeks
ago
when
I
showed
this
like.
I
saw
all
this
stuff
about
plate,
tectonics
and
the
evolution
of
life,
and
I
showed
this
graphic
of
like
a
tree
of
life.
A
That
goes
way
back
to
you
know
a
couple
billion
years
ago,
and
this
is
all
based
on
something
that
dick
and
myself
and
george
mikhailovsky
are
working
on
called
the
boring
billion,
and
I
think
those
are
well
is
interested
in
this
too.
This
was
going
to
be
a
paper
in
the
biosystem
special
issue,
but
it's
probably
now
going
to
be
a
book
contribution
of
some
type.
A
I
know
because
they
showed
me
some
stuff
but
like
this
is
a
sequence
analysis
to
be
clear:
what
bioinformatics
means,
because
we
have
the
the
data
about
the
tree
of
life
and
we
have
some
information
from
the
literature,
but
it's
very
you
know
sometimes
it's
very
sketchy
as
to
what
is
actually
going
on
this
time.
Like
you
know,
people
have
different
time
estimates
for
things,
and
so
it
would
help
if
we
could
get
some
sequence
data
and
analyze
it
and
I've
done
some
bioinformatics,
but
it
might
help
if
someone
else
would
want
it.
A
A
A
Yeah,
so
all
right,
yeah,
so
people
most
of
you
probably
don't
know
what
it
is.
So
the
boring
billion
is
this
period
where
you
have
sort
of
the
beginnings
of
life
and
then
for
like
a
billion
years.
Nothing
much
happens.
It's
pretty
simple!
You
know,
life
is
pretty
simple,
it's
maybe
unicellular
and
it's
just
hanging
out.
There
are
a
lot
of
theories
about
the
origins
of
life,
but
you
know
for
like
over
a
billion
years.
A
It
just
remains
very
simple
and
then
all
of
a
sudden,
you
get
this
explosion
of
diversity
where
you
get
multicellularity
and
you
get
different
kingdoms
like
you
get
animals
and
plants
and
protists
and
other
types
of
organisms
that
have
different
cell
types,
and
so
it
just
happens
all
at
once
and
there's.
I
guess,
there's
some
theories
about
like
the
earth's
you
know.
Atmosphere
like
you
know,
there's
their
oxygenation
events
that
sort
of
set
up
sort
of
the
end
of
the
boring
billions.
A
It's
a
very
it's
boring
because,
there's
not
you
know,
it's
just
life
is
basically
looks
all
the
same
or
you
know
maybe
there's
like
maybe
two
species
on
the
planet
and
doing
boring
things
they're
not
really
behaving
in
any.
You
know
meaningful
way.
I
mean
they're,
you
know
moving
around,
maybe
but
it's
it's
very,
very
simple
life,
and
then,
after
that,
you
get
this
explosion
of
different
kingdoms
of
life
and
different
ways
of
surviving.
A
So
that's
a
boring
billion
means
and,
and
we've
got
like,
we've
worked
out
a
lot
of
the
sort
of
I
mean
we
haven't
worked
it
out
on
our
own
we've
kind
of
like
assembled
a
lot
of
information
about.
What's
going
on
at
that
time,
so
you
know
the
contributions.
I
guess
we
have
a
manuscript
that's
being
worked
on.
We
have
some
graphics
and
we
have
maybe
some
data
analysis
that
needs
to
be
done,
especially
on
the
sequence
data.
A
So
if
you're
interested
in
the
sequence
data
analysis,
let
us
know-
and
we
can
we
can
get
on
onto
that
then
and
the
final
destination
for
this
is
now
to
be
announced.
It's
probably
going
to
be.
We
were
working
in
a
book
proposal
on
this
topic
a
long
time
ago,
but
I
don't
know
you
know,
there's
there
are
always
opportunities
to
put
put
it
somewhere.
So
it's
not
like
you
know,
we
won't
be
able
to
publish
it.
A
A
A
A
The
deadline
for
submitting
an
abstract
is
march
31st,
and
this
is
the
abstract.
Submission
portal
needs
to
be
1500
characters
which
is
small.
You
know
it's
like,
maybe
two
to
three
hundred
words
at
most
and
I'm
thinking
of
some
sort
of
devozu
epistemological
directories
mash
up
in
the
epistemological
directories.
Jesse
knows
about
this.
This
is
something
I'm
working
on
in
my
other
group.
It's
a
educational
tool
where
you
know
it
provides
tools
for
people
to
sort
of
do
self-guided
education
and
to
get
up
to
speed
in
different
fields.
A
Finally,
there's
this
a
nn's
bnn's
extended
abstract.
This
is
something
that
krishna
proposed
for
a
life
2021.
This
is
a
life,
is
coming
up.
It's
a
conference
on
artificial
life,
so
it's
computational
approaches
to
evolution
and
development,
and
this
extended
abstract
would
be
about
two
to
four
pages
in
their
template
and
it's
due
pretty
soon
so
we're
working
on
that
if
people
are
interested
in
looking
it
over
or
being
involved,
let
us
know,
but
this
is
a
pretty
short
turnaround.
So
I
don't
know
what
the
actual
opportunities
are
on
this
but
anyways.
A
Every
week,
of
course,
is
because
the
deadlines
are
always
kind
of
marching
towards
us.
So
that's
good.
Let's
see
what
else
is
in
the
chat
here.
Okay,
so
this
is
the
okay,
so
dick
says,
the
boring
billion
book
could
be
part
of
astrobiology
perspectives
on
life
of
the
universe
book
series.
So
this
is
a
book
series
that's
coming
out
and
actually
said
that
his
invite
is
not
working
and
jesse's
email.
So
jesse,
I
put
your
email
in
there.
A
A
We
need
to
kind
of
get
into
some
sort
of
argument,
and
maybe
you
know
the
a
life
conference
is
very
interested
in
sort
of
like
the
emergence
of
biological
systems,
so
we
might
focus
on
that
a
bit,
so
this
is
this
will
be
hopefully
by
this
time.
Next
week
the
deadline
will
have
passed
and
we'll
have
a
completed
extended
abstract
to
show.
A
So
this
is
the
evolution
presentation,
so
I
don't
have
that
much
to
say
in
that,
but
krishna
has
been
working
on
it.
So
finally,
we
have
our
paper
cue
here
I
was
going
to
talk
a
little
bit
about
some
of
the.
I
dug
up
some
things
on
some
cellular
automata
that
I
had
been
working
on
a
long
time
ago,
but
I'm
not
going
to
talk
about
that
today.
A
I
think
I
mentioned
surety
that
she
was
asking
about
different
models
that
we
might
use
for
evil
or
another
than
machine
learning
or
different
types
of
models.
We
might
use-
and
I
told
her-
we
have
a
couple
of
models
in
place
right
now,
but
in
we
can
always
add
machine
learning
models
on
to
what
we
have,
but
there
are
also
other
opportunities
to
do
other
types
of
models.
Our
group
has
been
interest
as
a
long-term
interest
to
think
in
cellular
automata.
A
We
also
are
interested
in
other
types
of
models
like
agent,
more
general
agent-based
models.
Things
like
that,
so
these
are
things
that
you
can,
if
you're
interested
in
contributing,
we
can
find
a
way
to
con.
You
know
put
it
into
this
model
zoo
and
we
you
know
it's
not
the
the
types
of
agent-based
models
and
cellular
automata
that
we
use
are
not
really
meant
to
do
like
data
analysis,
they're
more
meant
for
simulating
processes,
so
you
know
simulating
like
morphogenesis
with
a
cellular
automata.
A
A
A
I
think
we've
talked
about
19th
century
embryology
in
the
group
before
so
february:
16th
1834.
This
is
obviously
something
I
bookmarked
two
weeks
ago,
but
I'm
just
getting
to
it
now
birthday
of
german
naturalist
and
artist
ernst
takel.
So
this
is
ernest
taco
and
he
did
a
lot
of
stuff
with
embryos
and
and
kind
of
coming
up
with
ideas
about
embryology,
some
of
which
were
wrong,
but
some
of
which
were
pretty
spot-on
and
he
came
up
with
you
know.
What
is
it
a
capitulation
of
phylogeny?
A
The
idea
that
you
know
embryo's,
sort
of
development
from
sort
of
a
very
simple
embryo
to
you
know
to
an
adult,
actually
mimics
evolution,
and
so
he
came
up
with
the
concept
of
neotimi.
If
you've
heard
of
that-
and
it
says
these
are
ideas
that
you
know
you
come
up
with
and
they
over
time
they
get
proven
to
be
true
or
false.
You
know
they
get
falsified
or
they
get
confirmed
an
early
supporter
of
darwin.
He
studied
microfossils
and
deep
sea
sediments,
believing
in
the
unity
of
science
and
art.
A
There
is
even
a
reference
in
the
an
anime
ghost
in
the
shell,
which
is
this
here.
Anime,
of
course,
is
the
animation
style.
So
I
don't
know
what
the
reference
is,
but
that
was
the
that
was
what
they
were
highlighting,
and
then
this
is
a
thing
on
ernst
takel.
In
the
spirit
of
matter,
which
is
something
is
translated
from
german,
so
it's
a
bit
like
rough.
A
He
brought
a
novel
microscope
of
the
water
immersion
lens
that
could
be
used
to
look
at
living
microorganisms
and
a
young
scholar
wanted
to
spend
half
a
year
studying
jellyfish
in
florence,
also
mollusks
and
plankton
decades
later,
her
work
of
the
century
was
to
be
created.
In
the
end,
the
young
physician
natural
observer
was
prince
hakel,
who
was
born
february
16th
1834
in
potsdam,
germany,
the
request
of
his
father.
He
studied
medicine,
been
on
excursions
with
the
anatomist
johannes
mueller.
A
A
Had
only
been
discovered
a
few
years
earlier,
so
they
you
know
he
did
a
lot
of
these
sketches
and
so
there's
one
thing
about
hakel's
legacy
or
all
these
amazing
drawings
that
he
did
and,
of
course,
in
the
19th
century.
You
couldn't
take
a
lot
of
high
resolution
photographs,
so
you
did
a
lot
of
drawings,
and
so
this
is.
These
are
some
examples.
A
E
A
A
nice
article
again
this
is
a
sort
of
hegel's
pedigree.
Again
this
is
his
idea
of
evolution,
which
is
not
what
we
think
of
today.
You
know
this
is
sort
of
like
a
where
humans
are
at
the
pinnacle
and
there's
this
straight
tree
that
goes
up
from
base
here
yeah.
So
a
lot
of
these
ideas
were
sort
of
formative,
but
they
weren't
correct
and
the
way
we
understand
them
today
and
finally,
here's
this
reference
to
the
anime
character.
A
I
don't
know
much
about
this,
but
it's
you
know
you
don't
get
too
many
references
to
scientific
things
in
comics,
but
when
you
do
you
know
it's
interesting,
it's
a
an
aside.
So
I'll
talk
about
a
couple
papers
if
you
have
to
go
at
the
top
of
the
hour
here,
it's
fine,
but
I
will
mention
this
here
says
milton
tan
who's
at
the
university
of
illinois
he's
an
ichthyologist.
A
What
is
systematics?
Where
is
a
line,
and
so
that's
an
interesting
point,
because
we
think
about
evolution,
as
you
know,
maybe
like
classifying
traits
or
classifying
organisms,
but
we
also
have
things
like
population
structure,
so
like
traits
exist
in
different
places
in
the
species
range
or
things
like
that,
and
there
are
all
sorts
of
things
you
can
think
of
as
like
that
are
classification
that
aren't
necessarily
directly
tied
to
out
the
analysis
of
evolution.
A
So
you
know
there's
a
lot
of
a
lot
of
interesting
areas
there,
but
I
maybe
that's
above
over
some
people's
heads,
but
it's
a
interesting
question
we
might
follow
up
on
so
the
first
paper
I
want
to
talk
about
is
it's
a
new
article
from
james
briscoe's
group?
I
don't
know
you
know
many
people
aside
from
who
james
briscoe
is,
but
this
is
precision
of
tissue.
Patterning
is
controlled
by
dynamical
properties
of
gene
regulatory
networks,
and
this
is
brand
new.
A
But
that's
that's.
For
another
time.
The
abstract
reads:
during
development,
gene
regulatory
networks
allocate
cell
phase
by
partitioning
tissues
into
spatially,
organized
domains
of
gene
expression,
other
sharp
boundaries
that
delineate
these
expression
patterns
arise
despite
the
stochasticity
associated
with
gene
regulation
is
poorly
understood.
A
These
products,
then,
are
expressed
locally
in
the
environment
and
within
the
cell,
and
they
kind
of
determine
where
this
you
know
they
kind
of
are
expressed
spatially,
so
they're
expressed,
maybe
in
stripes
or
in
different
patches,
and
they
are
local.
You
know
so
there's
a
local
signal
and
you
get
these.
That's
how
you
end
up
with
patterning.
You
get
these
the
expression
of
genes
that
are
specific
to
certain
gene
products
that
then
are
spatially
segregated
and
you
end
up
with
these
patterns.
A
Gene
expression
is
not
deterministic,
there's
a
stochastic
component,
meaning
that
it
turns
on
and
off
sometimes
quite
unpredictably,
but
despite
that,
we
can
still
have
these
patterns
emerge.
That
look
highly
ordered.
That's
what
they're
trying
to
say-
and
you
have
these
very
precise
boundaries,
and
there
are
reasons
for
that
that
maybe
we
can
talk
about
in
another
week.
But
basically
you
have
you
know
boundaries
that
are
pretty
precise.
You
would
think
that
if
it's
just
this
general
process,
that
would
be
less
precise
than
it
is
so.
A
This
is
achieved
not
by
reducing
noise
in
individual
genes,
but
by
the
configuration
of
the
network
modulating
the
ability
of
stochastic
fluctuations
to
initiate
gene
expression
changes.
We
use
a
computational
screen
to
identify
network
properties
that
influence
boundary
precision
revealing
two
dynamical
mechanisms,
by
which
small
gene
circuits
attenuate
the
effect
of
noise.
In
order
to
increase
pattern
and
precision.
A
The
results
highlight
design
principles
for
gene
regulatory
networks
that
produce
these
precise
patterns.
So
the
paper
actually
is
about
looking
at
these
networks
using
a
computational
screen,
which
is
just
like
using
like
bioinformatics,
to
look
at
different
modes
of
gene
expression
and
then
kind
of
figuring
out
how
these
models
or
how
these
circuits
work.
A
So
they
actually
use
an
sde
model,
which
is
a
stochastic
differential
equation
to
simulate
a
pac-6
mutant,
which
is
where
they
knock
out
a
gene
in
this
mutant
in
pac-6,
I
think,
is
involved
in
eye
formation,
but
so
they
they're
able
to
use
these.
They
want
to
simulate
mutant
genotypes
and
their
effect
on
the
on
gene
expression.
So
a
stochastic
differential
equation
is
a
differential
equation
where
you
have
some
stochastic
element
in
it,
meaning
that
you
can.
A
You
have
some
factor
in
the
equation
that
allows
you
to
simulate
things
not
in
a
deterministic
way,
but
introduces
noise
and
systematic
noise
and
so
they're
able
to
use
these
in
these
gene
gene
regulatory
networks
and
they're
able
to
actually
model
this.
I
don't
know
if
they
have
any
good
figures
in
this
paper,
but
I
don't
know
where
the
figures
would
be.
Oh
at
the
end,
okay,
so
this
is
see
if
they
have
any
good
pictures
of
networks.
A
This
paper
is
mostly
the
biology
part.
This
is
this
shows
kind
of
the
boundaries
of
gene
expression.
So
you
have
these
different
stripes.
So
these
cells
are
expressing
this
gene
here
pack,
six
and
then
there's
only
two
down
here
and
then
and
kx2
down
at
the
bottom,
and
you
can
see
that
these
boundaries
are
pretty
sharp
and
you
know
there
are.
There
are
these
theories
about
gradients,
where
you
have
different
gradients
of
gene
expression
of
these
gene
products
that
collide
and
then,
where
they
kind
of
join
together.
They
kind
of
form.
A
A
This
is
figure
four,
so
they
kind
of
talk
about
the
configuration
of
gene
expression
fluctuations,
so
they're.
Looking
at
these,
these
dynamical
systems,
graphs,
where
they're
looking
at
transitions
between
different
states
in
the
wild
type
and
then
the
mutant,
and
then
this
is
a
sort
of
a
landscape
model
where
they're
looking
at
these
different
states
of
gene
expression.
A
So
they
do
a
lot
of
modeling,
it's
very
modeling
having
this
paper
and
then
they
actually
at
the
b.
At
the
end
of
this
paper,
they
have
a
glossary
of
dynamical
systems
terminology
because
this
was
published
in
development.
So
it's
going
to
be
very
difficult
for
some
people
to
follow.
If
you
don't
have
a
background
in
it.
A
So
if
you're
interested
in
dynamical
systems,
there's
a
nice
glossary
at
the
end
here,
so
you
have
a
phase
based
stochastic
systems,
deterministic
systems
and,
if
you're
interested
in
like
machine
learning,
you
may
have
heard
the
term
stochastic
before,
but
that's
actually
not
quite
the
way.
They're
using
it
here,
they're
talking
about
in
a
very
specific
way
to
systems
biology.
So
a
lot
of
this
you
know
a
lot
of
the
stochastic
components
of
in,
in,
like
gene
expression,
involve
fluctuations
of
concentration.
So
if
you
have
a
concentration
of
molecules,
those
concentrations
fluctuate.
A
A
A
Another
paper
is
this:
mechanotransduction
tuning
stem
cell
fate,
and
so
this
is
something
we
don't
usually
talk
about.
Stem
cells.
A
lot
in
this
group,
but
so
the
abstract
here
is.
It
is
a
general
concern
that
the
success
of
regenerative
medicine
based
applications
is
based
on
the
ability
to
recapitulate.
The
molecular
events
that
allow
stem
cells
to
repair
the
damaged
tissue,
slash
organ
so
to
this
end,
biomaterials
are
designed
to
display
properties
that,
in
a
precise
and
physiologic,
like
fashion,
the
germs
stem
cell
fate,
both
in
vitro
and
vivo.
A
The
rationale
is
that
stem
cells
are
highly
sensitive
to
forces
and
that
they
may
convert
mechanical
stimuli
into
a
chemical
response.
So
I
think
we've
talked
about
this,
where
you
have
you're
doing
things
to
cells,
mechanically,
constraining
them
and
other
things,
putting
them
on
different
substrates
and
that
actually
has
an
effect
on
the
chemical
response
on
the
gene
expression
response.
So
these
cells,
when
they're
they
experience
different
physical,
like
perturbations
or
physical
forces,
that
you
know
they.
A
People
have
done
experiments
where
they've
looked
at
different,
applying
different
physical
forces
to
these
cells
and
they're
able
to
get
them
to
express
genes
that
you
know
maybe
make
them
jump
to
a
different
state,
a
different
cell
state.
So
that's
the
basically
the
idea.
So
this
is
a
review
article
that
describes
the
novelties
on
stem
cells
and
biomaterial
interactions
with
a
more
focused
implication
of
the
mechanical
stimulation
in
mechanotransduction.
A
So
they're
interested
in
here
is
this
process
of
mechanotransduction,
so
you
have
mechanical
forces
acting
on
the
cells
and
it's
being
transduced
into
this.
You
know
to
the
cells
or
the
physiological
response,
which
is
to
express
certain
genes
in
response
to
those
forces,
and
so
it
sounds
actually
like
perception.
A
It
sounds
like
something
like
human
perception
where
we
take
an
information
and
we
produce
a
behavior
and
that's
sort
of
what
it
is.
But
you
know
this
is
where
you
know
people
start
talking
about
cellular
cognition,
they're
in
papers
written
on
that
topic.
Where
they
talk
about
how
cells
are
making
decisions,
cells
are
like
perceiving
things
in
their
environment.
This
is
sort
of
what
they're
talking
about.
A
So
this
paper
has
interest
in
regenerative
medicine
which
deals
with
tissues
and
organ
replacement,
tissue
engineering
and
stem
cells,
which
are
stem
cells
which
are
cells
that
don't
they
have
the
stem
fate,
but
they
can
differentiate
into
other
different
types
of
cells
and
so
they're
also
self-renewing.
So
they
don't
go
through
things
like
they
don't
go
through
cell
death,
so
they're
able
to
replicate
as
many
times
as
they
do.
A
There
is
no
limit
on
that
in
some
in
in
some
somatic
cells
like
in
your
skin
cells
or
your
heart
cells
or
your
muscle
cells,
they
live
for
a
finite
number
of
divisions
before
they
die.
So
these
are
things
that
people
aren't
been
interested
in
stem
cells
and
we
can
look
at
stem
cells
as
models
for
this,
because
stem
cells
are
particularly
susceptible
to
these
signals,
so
they
go.
A
So
you
can
take
a
skin
cell,
for
example,
put
in
factors
that
allow,
for
you
know
the
expression
of
genes
that
then
trigger
a
cascade
of
things
underneath
it
in
terms
of
the
gene
expression
network,
and
it
produces
this
stem
cell.
It
goes
becomes
a
stem
cell.
Basically,
it
sounds
magical
and
it's
not
something.
That's
an
exact
science,
but
people
done
a
lot
of
experiments
in
this
area.
A
A
So
they
basically
tell
the
promoter
of
a
gene
to
express,
because
the
conditions
are
such
that
they
need
to
express-
and
these
are
things
that
happen
normally
in
during
times
of
stress
or
maybe
during
times
of
development
when
they
need
to
be
expressed.
But
you
can
actually
mimic
these
experimentally,
and
so
you
have
all
these
things
happening
in
the
cytoplasm.
A
A
A
So
this
is
a
paper,
so
the
abstract
here
is
they're.
Talking
about
something
called
network
alignment
which
is
network
alignment
can
serve,
can
transfer
functional
knowledge
between
species,
conserved,
biological
network
regions,
traditional
n,
a
assumes
that
it
is
topological
similarity
which
is
a
matching
between
network.
You
know
the
shape
of
the
networks
between
regions
that
corresponds
to
a
region's
function,
functional
relatedness.
A
So
what
they're
doing
is
they're
looking
at
the
network's
topology
and
they're,
looking
at
the
relatedness
of
two
different
topologies,
so
it's
kind
of
like
the
relatedness
of
two
different
species
or
two
different
species
trees.
You
know
it's
a
similarity,
but
it's
really
not
they're
using
different
types
of
criterion.
For
that.
B
A
A
On
different
aspects
of
the
of
the
proteins,
so
this
is
you
know
this:
these
are
two
different
aspects
of
of
protein
action,
and
so
this
is
like.
One
thing
is
how
you
represent
it
in
a
graph.
The
other
is
the
actual
functional
relatedness
of
the
proteins
and
this
functional
relatedness
of
the
proteins
isn't
represented
in
a
graph,
so
you're
picking
up
things
in
the
graph
representation
that
are
a
bit
different
than
the
things
you
kind
of
know
about
the
proteins
functionally.
A
So
tara
used
topological
information
within
each
network,
but
not
sequence,
information
between
proteins
across
networks.
Yet
tara
yielded
higher
protein,
functional
prediction,
accuracy
than
existing
n,
a
methods,
even
those
usable,
topological
and
sequence
information,
and
so
this
is
something
basically
what
they're
doing
here
is
they're.
Looking
at
combining
different
techniques
and
looking
at
how
you
know,
those
different
techniques
can
be
combined
to
get
a
better
result
and
so
conclusions
as
such.
Combining
research
knowledge
from
different
domains
is
promising.
A
Overall,
improvements
in
protein,
functional
prediction
have
biomedical
implications,
for
example,
allowing
researchers
to
better
understand
how
cancer
progresses
or
how
humans
age,
and
so
this
is
that's
really
the
reason
why
they're
doing
a
lot
of
this
sort
of
stuff.
So
if
we
look
through
the
paper,
we
can
see
that
they
kind
of
describe
their
met,
sort
of
what
they're
doing
here.
They
review
these
n
a
methods,
and
then
they
produ
propose
this
better
method.
They
show
what
these
protein
protein
interaction
networks
look
like,
so
these
are
based
on
function.
A
These
are
just
based
on
maybe
like
proximity,
or
you
know,
coincidental
interactions
that
they
pick
up.
So
these
are
not
exactly
the
same
as
function
so
you're.
Actually
looking
at
these
interaction
networks,
which
tell
you
a
lot
about
the
global
state
of
proteins
in
a
cell,
like
you
know,
if
you
want
to
know
like
what
proteins
are
associated
with
one
another,
if
you
look
at
a
sample-
and
you
say
these
proteins
are
in
the
same
place-
they're
doing
something-
we
don't
know
what,
then
you
can
say.
A
Okay,
this
is
what
our
network
looks
like,
but
it
doesn't
tell
you
anything
specifically
about
function.
You
actually
have
to
go
in
and
do
the
functional
essay
separately,
but
then
that
doesn't
fit
into
this
global
view
of
what
you're
doing
so.
This
is
what
they're
trying
to
do
is
to
combine
these
two
approaches,
this
sort
of
holistic
network
approach
and
then
taking
what
we
know
about
the
functional
relationships
between
different
proteins
and
putting
them
together
in
a
single
model.
A
So
this
is,
you
know
this
is
classic
bioinformatics
stuff.
They
have
a
pipeline
where
they
actually
look
at
like
different
protein
protein
interaction
networks.
They
do
feature
extraction,
they
do
classification,
they
do
alignments
and
then
they
come
up
with
this
prediction.
That's
basically
the
way
they
do,
what
most
bioinformatics
these
a
lot
of
go
terms.
To
look
at
like
function,
which
go
terms,
are
descriptions
of
function
that
people
have
found
in
the
literature
and
they
have
a
sort
of
a
work.
A
A
So
that's
I'm
not
going
to
go
any
more
into
the
paper,
but
I
think
if
you're
interested
in
it,
you
can
read
more.
I
just
wanted
to
give
you
an
example
of
what
what
a
typical
bioinformatics
study
looks
like.
So,
let's
see
we
have
a
lot
of
things
going
on
in
the
chat
here,
so
we
have
actually
says
I
have
to
leave
now
we'll
meet
you
all
again
next
week.
Well,
thank
you
actually
for
attending
the
meeting.
Hopefully
some
progress
on
my
side.
A
So
next
week,
akshay
and
oswal,
I
think,
are
interested
in
presenting
on
some
work.
They've
been
doing
on
the
basil
area.
So
I
look
forward
to
that.
Let's
see
we
have
another
couple
of
comments
here.
If
I
can
get
back
up
to
the
top
okay
susan
says
the
gene
networks
probably
have
something
to
do
with
the
mechanics
as
in
pressure
and
tension.
So
I
think
that
was
from
the
first
paper
then
nick
says
differentiation.
A
Waves
may
control
these
networks
and
then
susan
says
waves
are
good
at
producing
tension
and
compression
dick
says
I
have
a
suggested.
I
have
suggested
we
list
all
papers
discussed
here
and
cited
an
endnote
basic,
which
is
free,
so
let's
yeah,
let's
keep
up
on
the
endnote.
I
know
jesse
is
interested
in
the
zotero
for
the
other
group,
but
I
told
him
that
we
were
making.
We
were
doing
an
endnote
library
for
these
papers,
so
I
need
to
go
back
and
pull
out
those
references
too.
A
That
also
says
stem
cell
relationship
to
differentiation
waves,
and
he
has
this
paper
here
on
the
diseased
breast
lobe
in
the
context
of
x,
chromosome,
inactivation
and
differentiation
waves,
and
then
this
other
paper
on
reverse
engineering,
the
mechanical,
more
molecular
pathways
and
stem
cell
morphogenesis,
and
the
stealth
cell
states
boiler
and
differentiation
wave
working
model
for
embryonic
stem
cell
development.
So
those
are
all
these.
Actually,
these
last
two
I
was
thinking
about
when
I
was
reviewing
the
paper
on
the
different
forces,
the
meccano
transduction
and
then
the
stem
cell
paper.
A
So
that's
a
very
good
set
of
papers
to
read
if
you're
interested
in
what
some
of
those
things
might
be
doing
in
the
cell
and
especially
in
terms
of
differentiation.
My
knot
says
interesting
paper.
Thank
you,
my
knock
so
as
well,
sir,
I
and
thirun
are
working
on
basil
area.
I
haven't
talked
with
ashley,
yet,
oh
okay,
so
sorry
thiruan
was
the
person
who
was
working
on
ambassadoria
and
ashay
was
working
on
the
yeah.
I
I'm
sorry
I
I
forgot
but
anyways.
I
look
forward
to
the
presentation
next
week
on
that.
A
I
know
that
you've
been
previewing.
Some
of
that
in
the
slack,
so
keep
up
the
good
work
and
again,
if,
if
anyone's
interested
in,
if
anyone
has
a
paper,
they
want
me
to
present
or
they
want
to
present.
We
can
do
that
and
just
send
it
to
me
or
before
the
meeting
we
can
pull
it
up
or
if
you're
interested
in
presenting
on
some
work
that
you're
doing.
Let
me
know-
and
I
can
put
you
on
the
schedule
so
does
anyone
else
have
any
questions
before
we
go
right
comments.
A
A
A
A
A
A
A
You
know
come
to
our
next
meeting
and
we
have
a
youtube
channel,
which
is
also
you
know
it's
the
divorm
youtube
channel
so
which
gives
you
a
lot
of
stuff
information
about
past
meetings.
So,
if
you're
interested,
you
know,
browse
that
and
see,
if
maybe
there's
something
you'd
be
interested
in
doing
or
just
you
know,
just
seeing
what
we've
been
talking
about?
Okay,
that
sounds
good.