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From YouTube: DevoWorm (2021, Meeting 28): GSoC #9, Math of DevoWorm, Early Response to Environment and Emergence
Description
Update on Summer of Code activities (Mainak Deb, Improving DevoLearn), Review of submissions document and Mathematics of DevoWorm document. Immediate Early Genes and Activity-dependence in embryos and the brain, Early Embryos and the Emergence of Embryonic Development. Attendees: Bradly Alicea, Krishna Katyal, Susan Crawford-Young, Akshay Nair, and Mainak Deb.
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Graduate
school,
it's
not
like
grades
are,
like
you
know,
they're
just
kind
of
like
they
have
to
give
you
something
they're,
not
really
that
worried
about
it.
I
mean,
if
you're
competing
for,
like
a
super
high
profile
job.
Maybe,
but
I
don't
even
think
then
it's
more
like
publications
and
things
like
that.
So.
C
C
He
just
does
weekly
entries
for
the
program
that
he's
in,
and
this
is
the
work
done
this
week
july,
26
to
august
first,
so
he's
converting
the
lineage
population
model,
which
is
the
thing
that
they've
been
using
for
in
evil,
learn
to
build.
Let's
see,
if
he's
here
nice
here
good.
This
is
the
looking
over
your
blog
post
here.
My
doc
looks
pretty
good.
D
D
D
D
D
So
these
these
files
could
be
found
in
the
github
repository
that
have
linked
over
here,
just
open
up
repository
yeah.
So
this
is
the
profile,
and
this
is
the
setup
file
that
was
required
and
the
requirements.txt
file
is
also
currently
here.
Working
on
this
official
repository
and
I'll
be
I'll,
be
actually
moving,
everything
that
is
here
to
the
official.
It
was
very,
very
quickly
so
yeah,
so
the
web
app
is
actually
it's
actually
online.
D
Now
I
okay,
so
so,
can
you
see
the
webinar
yeah
yeah
yeah,
so
so
this
is
the
home
page,
it's
pretty
basic.
As
of
now
it
just
contains
a
couple
of
some
some
of
the
animations
that
shows
what
the
library
could
do
and
at
this
point
I
see
this
as
a
placeholder
and
I'll
be
adding
more
content
very
soon
to
this
page
to
the
home
page
very
soon,
so
I'll
go
ahead
and
show
you
the
models.
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The
preference
of
the
user
and
the
next
the
next
goal
would
be
to
have
some
sort
of
to
have
some
sort
of
a
folder
uploading
mechanism.
Uploading
mechanism,
using
which
the
user
would
be
able
to
upload
many
inputs
at
once
and
would
be
able
to
get
the
options.
Also
as
a
zip
file,
which
you
would
be
able
to.
D
D
Yeah
previously,
I
was
using
this
other
library
called
radio
which,
using
which
I
had
made
some
progress.
But
I
I
did
not
decide
to
go
ahead
with
it
during
that
library,
because
I
could
not
find
the
way
to
host
three
different
models
in
one
web,
app
using
the
radio
library
and
then
using
that
library
would
have
led
to
three
different
web
links
for
the
three
modules
which
is
not
at
all
convenient.
D
D
I'll
be
adding
content
regarding
the
models
in
the
weather,
specifically
the
home
page
and
the
and
the
any
specific
module
pages
I'll,
be
adding
a
bit
of
markdown
text
or
flow
charts,
which
would
show
how
the
model
was
trained
and
what
sort
of
data
was
it
trained
on
which?
What
is
the
architecture
of
the
network
to
give
the
users
a
better
idea
of?
What's
going
on?
D
Which
I'll
be
doing
very
soon
and
then
moving
from
travis
ci
to
detail?
Rations
has
been
one
of
my
goals
since
the
past
week
itself,
so
I'll
have
to
I'll
have
to
get
that
done
too
as
soon
as
possible,
and
then,
by
the
end
of
a
google
server,
I
have
plans
to
release
a
new
level
and
russian,
which
will
contain
all
of
the
upgrades
and
all
of
the
new
features
that
have
been
integrated
in
to
the
library
and
I'll
also
have
to
upgrade
the
level
of
startup.
C
C
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D
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C
C
Right
or
some
c
elegans,
it's
it's
fairly
easy
to
learn
that
because
it's
like
the
cells
are
deterministic.
So
if
you're
at
a
certain
stage
of
development,
you
know
kind
of
what
cells
should
be
there,
so
it's
kind
of
like
it'll
know,
of
course
I
don't
know
do
you?
Would
you
put
any
sort
of
like
do
you
train
the
neural
network,
or
does
it
just
kind
of
see
the
image
and
know
that
it's
c
elegans
or
because
I
know
you've
trained
it
on
c
elegans,
but
maybe
not
any
other
type
of
embryo.
D
A
D
B
C
Well,
they
have
a
lot
of
packages
like
available,
for
you
know
general
use
like
if,
if
you
go
like,
I
think,
well,
there's
pi
torch
and
then
there's
tensorflow
and
people
argue
the
pros
and
cons
of
these,
for
you
know
until
they're
blue
in
the
face,
but
you
know
pick
the
one
you're
most
comfortable
with.
I
guess,
if
you're
doing
something
like
you
know,
I
don't
know
you're,
probably
not
doing
it
for
like
a
publication.
Maybe
you
are
that
might.
C
C
C
B
A
C
B
C
C
C
C
C
D
A
C
E
E
E
C
C
C
You
know
for
reviewing
methods
or
seeing
what
they
have
to
say
in
their
reviews,
because
they'll
get
experts
in
the
field
and
they'll
give
lectures
and
those
lectures
will
be
available
online.
So
you
can
follow
along
as
a
what
they
call
an
observer
track
and
the
observer
track.
You
know
it
might
have
some
useful
information
for
people,
so
I
will
do
that
next
week,
though
I'm
going
to
share
my
screen
here.
C
So
let
me
go
over
the
submissions
really
quick.
I
don't
think
there's
anything
in
this
document.
That's
particularly
time
sensitive.
We
still
have
our
our
you
know
things.
We've
been
working
on
for
weeks,
the
non-neuronal
cognition
the
diva
learned
papers,
or
you
know,
on
various
stages
of
whole.
Some
of
these
other
projects,
the
mathematics
of
diva
worm,
I'm
going
to
talk
about
today,
a
little
bit
in
depth
and
then
the
neurops
workshop.
C
But
if
you
are
on
the
lookout
for
some
of
the
workshops,
you
might
be
able
to
contribute
to
some
of
the
workshops
that
they
have
it's
not
as
prestigious
as
the
main
conference.
But
you
might
learn
some
interesting
things
and
you
may
be
able
to
get
involved
in
this.
So
let
me
go
through
some
of
the
workshops
that
they
have
accepted
for
this
year
and
we'll
see
maybe
there's
a
good
fit
for
some
of
our
work.
Or
you
know
you
might
find
something
really
interesting.
C
So
this
is.
These
are
like
the
each
of
these
workshops
will
eventually
have
a
website,
so
this
doesn't
have
the
website
with
the
submission
details
for
submissions,
but
this
basically
is
a
list
of
workshops
that
have
been
accepted,
so
you
might
be
interested
in
one
of
these
workshops,
and
this
gives
you
a
heads
up
as
to
what's
going
on,
so
they
have
a
lot
of
workshops
this
year
they
have
busy
and
deep
learning.
C
They
have
things
on
education
on
new
frontiers,
so
this
is
on
federated
learning.
There's
this
one,
medical
imaging
meets
nurips,
and
this
is
a
workshop
on
medical,
imaging
and
microscopy
imaging.
I
imagine
there's
a
lot
of
that.
There's,
probably
a
lot
of
medical
images
like
mri
images,
and
things
like
that.
So
you
know
that's
one
sort
of
related
area.
C
Let's
see
there
are
things
like
human
and
machine
decisions,
something
called
cooperative
ai.
This
is
where
they
look
at
like
models
of
cooperation,
so
I've
noticed
that
they're
getting
kind
of
they're
going
in
the
direction
of
artificial
life
in
some
cases.
So
a
lot
of
these,
some
of
these
workshops
are
focusing
on
things
like
reinforcement,
learning,
human,
ai
interaction,
game
theory.
C
So
this
is
stuff
that
you
might
find
like.
Maybe
like
an
artificial
life
conference
or
even
the
cognitive
science
conference
that
was
held
recently,
then
you
also
have
some
other
like
natural
language
and
speech
processing,
I'm
not
sure
if
there
are
any
other
ones
that
really
are
relevant
to
this
group.
There's
deep
reinforcement
learning
so,
if
you're
into
reinforcement,
learning
that
might
be
interesting
to
attend
or
find
out
more
about.
C
Ai
for
science,
mind
the
gaps.
This
is
a
workshop
where
they
have
that
it's
sort
of,
like
you,
know,
computational
biology,
chemistry,
material,
science,
so
they're
looking
at
you
know
these
critical
gaps
that
ex
exist
with
like
sort
of
applying
algorithms
to
science,
and
so,
despite
this
promise,
several
critical
gaps,
stifle
algorithmic
and
scientific
innovation
and
ai
for
science,
unrealistic,
methodological
some
assumptions
or
directions,
overlook
scientific
questions,
limited
exploration
at
the
intersections
of
multiple
disciplines,
the
science
of
science
and
responsible
use
and
development
of
ai
for
science.
C
C
You
have
meta
learning,
that's
always
an
interesting
topic:
offline
reinforcement,
learning
like
things
like
the
symbiosis
of
deep
learning
and
differential
equations.
What
else
just
a
lot
of
different
topics?
You
know
kind
of
exploring
some
niche
issues
in
the
field,
so
image
net
past
present
and
future.
So
this
is
since
it's
released
in
2010
image
net
has
been
instrumental
in
the
development
of
deep
learning
architectures
for
computer
vision.
So
a
lot
of
the
microscopy
images
are
sort
of
you
know
relies
on
this
computer
vision.
C
Model
of
you
know:
analyzing
images
using
feature
space,
so
this
is
sort
of
a
focus
on
the
imagenet
architecture
and
kind
of
a
reflection
on
that.
So
there
are
a
lot
of
different
things
you
can
look
at
you
know
I
would
look
at
that
link
and
see.
What's
there,
it
kind
of
gives
you
an
idea
of
the
state
of
the
field.
C
The
other
thing
I'd
like
to
talk
about
is
this
mathematics
of
diva
worms.
So
this
is
this
thing
this
poster
we're
working
on
with
different
equations
and
different
models
that
we
might
use
in
that
that
sort
of
we
hit
on
over.
You
know
week
after
week
in
our
group,
so
you
know
we
have
tools
like
the
von
neumann
neighborhood,
which
are
essential
for
understanding
cellular
automata.
C
We
have
these
models
of
the
embryo
and
these
like
multi-parameter
models.
We
have
things
like
neural
networks
and
complex
networks
and
lineage
trees,
which
are
all
different
versions
of
you
know
with
something
that
has
nodes
and
links
between
them.
So
you
know
we
have
neural
networks,
of
course,
they're
very
different
from
complex
networks
and
lineage
trees,
but
they
all
are
used
to
represent
this
developmental
function
at
some
level,
and
then
we
have
some
other
new,
newer
ones
that
I
put
in
here
just
last
week.
C
So
last
week
I
talked
about
creating
a
differentiation
tree
with
a
computational
agent,
and
so
this
is
an
example
here
of
this
agent,
that's
being
developed
in
actually
that
that
should
be
there.
But
this
is
an
example
of
an
agent
that's
being
developed
from
like
this
three-layered
embryo
and
it's
being
developed
into
what
they
call
a
breitenberg
vehicle,
and
so
this
is
a
differentiation
tree
of
that
process.
C
C
C
Yeah,
so
let
me
go
to
the
paper,
so
I
know
susan
was
asking
about
the
papers
before
sort
of
at
the
beginning
of
the
meeting,
and
I
told
her
that
we
are
keeping
things
in
these
folders
and
they're
open.
I
don't
send
the
folder
links
out
as
much
as
I
should,
but
to
let
you
know
that
we
have
these
folders
and
I
sort
of
been
organizing
them
a
little
bit
better
with
different
subtopics.
C
So
just
to
let
you
know
that-
and
this
is
an
example-
the
subtopic
structure
here.
So
one
of
the
things
we've
been
doing
is
talking
about
what
is
environment.
So
this
is
the
fourth
installment
of
this,
and
so
this
goes
back
to
a
question
that
I
had
or
a
conversation
I
had
with
dick
and
I've
talked
about
this
before
you
know
about
environment
and
he
asked
what
is
it?
C
What
is
its
relevance
and
how
do
you
define
it,
and
it
was
sort
of
a
rhetorical
question,
because
we
kind
of
know
what
it
is,
but
we
kind
of
don't
know
what
it
is.
There
is
a
lot
to
sort
of
quantify
and
really
define.
So
you
know,
we've
been
talking
about
these
points.
That
environment
is
permissive,
it
allows
things
to
be
transduced
into
the
biological
system.
It
allows
change
to
happen
in
development
and
environment
is
also
instructive.
C
C
So
you
know
this
could
be
actually
stimulating
a
single
cell.
It
could
be
stimulating
a
nervous
system
where
you
get
like
a
sensory
input,
and
so
this-
and
sometimes
it's
also
with
respect
to
stress
if
the
organism
experiences
some
stressor,
it
can
activate
these
immediate
early
genes,
in
this
case,
they're
actually
uniquely
involved
in
various
aspects
of
synapse
development.
C
And,
of
course
we
know
that
synapses
are
notoriously
or
maybe
surpri,
maybe
not
surprisingly,
plastic-
that
they
change.
Sometimes
they
are
maintained
in
development.
Sometimes
they
change
quite
a
bit
in
development,
and
so
this
is
a
good
place
to
look
at
the
the
effects
of
these
immediate
early
genes.
C
In
this
review,
we
summarize
recent
studies
of
a
subset
of
neuronal
iegs
in
regulating
synapse
formation,
transmission
and
plasticity.
We
also
discuss
how
the
dysregulation
or
how
these
things
are.
Not
you
know
not
regulated
in
the
same
way
of
neuronal.
Ieg's
is
associated
with
the
onset
of
various
brain
disorders
and
pinpoint
key
outstanding
questions
that
should
be
addressed
in
the
field.
C
They've
also
identified
a
number
of
transcription
factors
that
are
related
to
the
expression
of
these
genes,
so
these
are
things
when
the
gene
is
trans.
When
it's
transcribed,
it
leaves
these
transcription
factors
in
the
cell,
and
this
is
something
that
drives
other
processes
like
protein
form.
You
know
protein
construction
and
other
things
that
were
could
regulate
other
genes,
they're
very
diverse,
but
basically,
if
you
find
transcription
factors
of
these
genes,
you
know
it's
being
expressed.
C
C
C
Okay,
we
first
discussed
earlier
studies
that
have
illuminated
the
role
of
cis
acting
elements,
which
are
elements
that
are
near
the
gene
itself
that
regulate
the
gene
within
the
promoters
of
immediate
early
genes.
So
these
are
elements
that
are
within
the
promoter
region,
and
you
know
if
you're
not
familiar
with
gene
expression.
That's
fine!
Just
let
it
know
that
this
is
one
version
of
what
you
know
of
types
of
elements
you
can
have.
C
Then
they
talk
about
this,
how
this
has
an
important
role
for
epigenetic
and
what
they
call
topological
mechanisms.
So
this
is
where
you
have
both
sort
of
regulating
how
genes
are
expressed
and
then
these
things
where
the
rnas
are
folding
and
taking
on
different
shapes
and
it
actually
affects
that
which,
in
turn
that
affects
how
genes
are
how
proteins
are
built?
How
the
rnas
are
are
translated
and
things
like
that.
C
So
then.
The
third
paper,
then,
is
this
role
of
immediate
early
genes
and
synaptic
plasticity
and
neuronal
assemblies.
So
this
is
where
it
actually
affects
the
memory
trace.
So
this
is
where
it's
affecting
memory
of
the
organism-
and
this
is
again
you
know
they
talked
about
the
immediate
early
genes,
egr1,
cfos
and
arc,
and
it's
rapidly
and
selected
selectively,
upregulated
and
subsets
of
neurons
or
specific
brain
regions
associated
with
learning
and
memory
formation.
So
now
we're
talking
about
learning
and
memory
formation,
but
we're
talking
about
the
molecular
level.
C
So
these
different
iegs
are
widely
have
been
widely
used
as
a
molecular
marker
for
neurons
that
undergo
plastic
changes.
So
when
they're
looking
at
long-term
memory,
one
of
the
things
they
can
do
say
in
a
mouse
model
is
look
at
the
expression
of
these
genes
and
it
tells
them
something
about
what
you
know.
C
C
So
that's
that
part
of
the
environment.
So,
in
this
environment
theme,
we've
gone
from
like
looking
at
stimulus
to
looking
at
what
happens
within
the
cells
within
the
nervous
system,
and-
and
this
tells
you
something
about
environment,
how
it's
affecting
both
the
organism
and
development
and
more
generally,
so
I
hope
that,
like,
if
you
go
back
to
the
youtube
videos,
you'll
see
that
we,
you
know.
C
I
hope
we
can
stitch
that
together
a
little
bit,
I'm
going
to
go
back
and
look
at
them
and
see
if
we
can
like
come
up
with
some
sort
of
summary
statement
on
you
know
what
what
we're
talking
about
here
and
what
are
the
major
themes.
I
think
that's
important,
though,
to
talk
about
at
different
levels
of
organization,
so
from
like
the
organismal
level,
where
you
have
sensory
inputs
to
this
molecular
level
and
the
molecular
level
may
seem
unclear
as
to
its
relevance.
C
If,
if
we're
looking
at
microscopy
images,
we're
going
to
see
these,
we
can
actually
do
some
really
interesting
things
with
some
of
these
markers,
because
they'll
often
use
a
fluorescent
marker
to
you
know
mark
some
of
the
expression
of
some
of
these
genes,
and
you
can,
you
know,
generate
or
work
from
a
data
set
where
you
have
these
things
put
together
in
time,
so
you
can
actually
look
at
the
dynamics.
So
it's
really
there's
some
interesting
stuff
that
might
happen
in
the
future
on
that.
C
So
this
one
is
interest.
This
is
early
embryos
and
emergent
embryos.
So
this
is
a
collection
of
papers
here,
where
we
have
a
number
of
different
model
organisms
and
it's
sort
of
about
what's
going
on
in
the
early
embryo
and
things
that
are
emerging,
so
they're
different
things
like
the
emergence
of
an
embryo
itself,
the
emergence
of
behavior,
so
the
first
one.
Well
actually
now
I'll
start
here
this
one
here,
because.
C
C
C
Yeah
all
right,
let
me
start
over
here
with
this.
Okay.
Here
we
go
so
this
is
actually
one
of
the
first
embryos
that
were
found.
We
talked
about
this
before
we
talked
about
one
of
the
first
embryos
that
were
found.
This
is,
I
think,
a
different
embryo.
This
is
a
different
assemblage
where
they
kind
of
talk
about
the
first
embryos
and
plants.
C
This
is
now
the
first
embryo
I
believe,
just
kind
of
like
showing
some
of
these
processes
that
you
see
in
modern
embryos,
but
this
is
609
million
years
old,
so
this
is
very
very
early.
This
is
before
the
cambrian
explosion,
where
you
get
a
lot
of
multicellularity,
so
this
is
like
very
early
multicellularity.
C
This
is
so
canvas.
Fiera
was
an
enigmatic
component
of
the
609
million
year
old
wing
gum
biota
of
south
china.
This
study
uses
an
x-ray
tomography
to
characterize
cellular
structure
and
development,
so
they're,
taking
these
from
fossils
and
they're,
actually
looking
at
some
of
the
things
that
have
been
preserved.
So,
first
of
all,
you
have
this
thing,
that's
being
preserved.
I
don't
know
if
you
want
to
call
it
an
embryo,
but
it's
definitely
some
sort
of
collection
of
cells
that
exhibit
some
of
these
like
embryonic
features.
C
So
you
have
these
things
like
this.
This
organism
develops
within
an
envelope
by
cell
division,
aggression,
detachment
and
polar
aggregation
in
a
manner
analogous
to
gastrulation.
Now
it's
not
gastrulation,
as
we
know
it
today,
maybe,
but
it
has
this
sort
of
set
of
features
together
with
evidence
of
functional
cell
adhesion
and
development
within
an
envelope.
This
is
suggestive
of
a
whole
zone
affinity,
so
this
is
suggestive
that
that
it's
similar
to
our
development
today,
animal
development
and
plant
development-
I
guess
too
it
has
so
this
is
the
darshan.
C
This
is
in
this
del
shanto
formation.
So
this
is
again
we
go
back
to
this.
We
talked
about.
We've
talked
about
this
in
weeks
past,
where
they
have
these.
It's
a
rich
multi
microfossil
assemblage
that
preserves
biological
structure
to
a
subcellular
level
of
fidelity
and
encompasses
a
range
of
developmental
stages.
C
However,
the
animal
embryo
interpretation
of
the
main
components
of
the
biota
has
been
the
subject
of
controversy.
Here
we
describe
the
development
of
quevus
fiera,
which
varies
in
morphology
from
lensoid
to
hollow
spheroid
cage.
So
this
is
the
cage
up
here
and
they
have
these
different
features
of
it.
C
So
the
idea
is
that
in
this
ediacaran
period,
which
is
around
this
time
of
earth
history
during
which
molecular
clocks
estimate,
the
fundamental
animal
lineage
is
to
have
diverge
so
they're
using
like
what
they
call
a
mutational
clock
to
calculate
back
to
a
common
ancestor,
and
the
calculations
suggest
that
this
is
about
the
time
that
this
happens.
So
this
is
evidence
for
this,
maybe
being
a
common
ancestor
to
our
embryos
that
we
have
today.
A
C
C
C
So
this
is,
you
know
it's
pretty
unclear
as
to
kind
of
in
in
paleontology
in
general,
you
can't
really
get
good
species
estimates
they're,
always
fighting
over
it,
and
sometimes
you
know
if
you
find
different
parts
of
the
same
organism,
you
can't
say
for
sure
whether
it
was
part
of
one
organism
or
multiple
organisms.
So
it's
never
really
clear.
Nevertheless,
here's
some
nice
microscopy
images
of
these
structures,
so
these
are
the
structures
here,
they're
talking
about
and
they're
looking
at
some
of
the
formations
that
they
make
within
the
shell.
C
C
And
you
have
spores
and
other
things
here.
So
you
can
see
it
just
covers
as
vast
distance
across
the
tree
of
life,
and
they
all
have
this
sort
of
common
ancestor,
which
is
basically
this
thing
with
a
shell
with
some
cells
inside
and
they
have
lipids
and
they
have
some
ass
and
they
make
these
things.
There's
an
experimental
model
that
they
use
that
are
like
these
little
lipid
spheres
that
they
can
make,
and
they
actually
use
these
to
look
at
different
aspects
of
pattern.
C
Formation
and
of
morphogenesis-
and
I
can't
remember
too
much
about
them
right
now,
but
they
use
them
actually
in
in
biomedicine,
to
look
at
like
they're,
they're
sort
of
looking
at
like
how
cells
form
and
embryos
form.
So
this
is
interes
an
interesting
parallel
and
I'll
try
to
get
a
paper
on
this
thing,
I'm
talking
about
next
week
but
yeah.
This
is
this
is
kind
of
reminiscent
of
that,
it's
basically
a
very
basic
model
of
a
cell
or
of
an
embryo,
or
actually
a
multicellular
structure,
yeah,
so
so
yeah.
C
C
So
this
goes
now
to
frogs
or
xenopus
embryos.
So
now
we're
in
the
modern
era
and
we're
looking
at
these
xenopus
lavis
embryos,
which
is
a
common
model
organism
that
shows
that
the
very
early
brain
is
important
functions
long
before
behavior,
and
we
know
that
from
our
group.
We
know
that
there's
there
are
these
different
movements
that
the
cells
make
and
they're
doing
things
to
engage
in
pattern
formation.
C
So
this
is
like
things
that
would
kill
the
embryo
or
some
developing
tissues
before
they
get
a
start.
They
can,
you
know,
monitor
the
embryo
for
these
these
things.
These
activities
of
the
early
brain
can
be
partially
compensated
for
in
a
brainless
embryo
by
experimental
modulation
of
neurotransmitter
and
ion
channel
signaling.
C
So
this
is
michael
evans
group
and,
of
course
they
do
this
stuff
with
bioelectricity,
and
so
that's
one
of
their
interests,
and
so
you
can
do
things
really
earl
to
to
the
embryo
really
early
on
and
all
embryo
cells
have
this
sort
of
bioelectric
potential.
So
this
bioelectric
potential
is
acting
to
regulate
the
embryo.
Well
before
it's
generating
any
sort
of
behaviors.
C
Here
we
discussed
the
major
finding
of
this
paper
in
the
broader
context
of
developmental
physiology,
neuroscience
and
biomedicine.
The
novel
functional
in
the
embryonic
brain
has
significant
implications,
especially
for
understanding
developmental
toxicology
in
the
context
of
pharmaceutical
reagents,
so
they're
just
really
interested
here.
Looking
at
what
the
cells
of
the
embryo,
maybe
the
emerging
brain
does
before
it
starts
to
form
synapses
with
the
muscle
and
starts
driving
muscle
activity.
C
So
what
will
happen?
Is
you'll
get
this
emerging
nervous
system.
You
get
these
neurons
that
emerge.
You
start
to
get
connections,
you
get
these,
what
they
call
gap,
junction
connections
and
then
this
this
brain
starts
to
form
later
you'll
get
synaptic
connections
and
then
you'll
get
some
electrical
activity
in
the
network.
C
Now,
in
parallel,
you
have
the
muscles
forming
and
the
muscles
will
start
to
twitch
sort
of
autonomously
before
they're
wired
to
the
brain,
so
these
axons
will
go
out
to
the
periphery,
make
a
connection
with
the
muscle,
and
once
that
happens,
then
the
muscle
is
actually
controlled
by
the
brain.
But
before.
C
So
this
is
really
an
interesting
piece
of
work
because
it
suggests
that
there's
this,
this
very
early
brain,
is
actually
doing
more
to
regulate
morphogenesis
and
patterning
of
tissues
than
it
is
driving
behavior,
and
so
it's
actually
interacting
with
its
periphery,
which
is
something
that
happens
when
the
connections
come
into
play
and
it's
connecting
to
things
like
muscle
and
it's
driving
behavior.
It
can
also
regulate
that
periphery
earlier
and
so
that
that's
kind
of
the
point
of
this
paper
and
I'm
not
going
to
get
too
much
more
deeply
into
that.
C
But
I
did
want
to
talk
to
about
two
other
papers
here,
this
mouse
embryonic
stem
cell
paper.
So
this
is
where
you
have
these
muscle
embryonic
stem
cells
itself
organized
into
trunk,
like
structures
with
neural,
tube
and
somites,
and
this
is
a
paper
on
this
I'll.
Just
look
at
this
figure
here,
where
you
have
this
trunk-like
structure.
So
what
they're
doing
here
is
they're
forming
these
embryos,
and
we've
talked
about
embryoids
and
different
structures
like
this
before,
where
you
have.
C
When
they're
put
onto
this
three-dimensional
structure,
they're
doing
a
lot
of
things
to
to
form
some
sort
of
shape,
and
so
you
can
actually
do
things
when
they're
on
this
scaffold,
you
can
expose
them
to
different
chemicals,
and
these
chemicals
are
things
genes
that
are
expressed
in
the
cells
but
you're,
giving
them
more
of
it.
You're
modulating
using
these
chemicals
you're
getting
you
can
look
at
like
single
cell
already
seek,
so
you
can
actually
take
the
cells
each
cell.
C
C
This
is
actually
a
cellular
automata
where
we
can
reach
in
and
see
what
the
metabolic
activity
is,
what
the
gene
expression
activity
is-
and
this
is
in
a
context
of
some
geometry,
so
we
know
that
they're
interacting
with
their
neighbors
and
it's
influencing
what
they're,
expressing
maybe
it's
expressing
genes
that
allow
it
to
differentiate
into
a
a
certain
type
of
cell
other
than
a
stem
cell.
Maybe
it's
suppressing
its
potential.
C
We
don't
really
know,
but
we
can
look
at
that
using
this
method,
so
we
have
this
tls,
which
is
the
trunk-like
structure,
and
it's
supposed
to
resemble
an
embryo.
So
we
can
map
between
this
tls
and
an
embryo,
and
we
can
look
at
what
maybe
what's
going
on
with
respect
to
the
3d
structure
of
an
embryo.
C
This
allows
us
to
build
this
trunk-like
morphogenesis
from
these
cells.
You
build
this
scaffold,
you
embed
the
cells
in
it
on
the
surface
and
it
will
form
these
complex
structures.
So
this
is
a
picture
of
the
gastruloid,
and
this
is
the
term-like
structure
which
resembles
this
gastroloid
is
like
not
shaped
by
a
by
a
scaffold.
C
Then
they
show
this
in
comparison
to
the
e
9.5
embryo,
and
this
is
like
a
certain
stage
of
morphogenesis
in
the
embryo
in
a
in
situ
embryo,
and
they
have
other
images
here
so
generation
of
trump,
like
structures
with
somites
in
a
neural
tube.
So
you
can
actually
take
these
structures
and
form
things
like
some
whites
and
a
neural
tube
along
them.
So
you
can
actually
direct
the
differentiation
of
cells
in
certain
ways
using
gastroloids
okay.
So
that's
that
paper
and
finally,
we
have
this
paper
on
axial
elongation
of
caudalized
human
organoids.
C
This
is
another
organoid
paper,
but
it's
focused
on
neural
tube
development,
and
so
the
abstract
here
is
the
axial
elongation
of
the
neural
tube
is
crucial
during
mammalian
embryogenesis
for
anterior
posterior
body
axis
establishments.
This
is
head
to
tail
axial
organization
and
subsequent
spinal
cord
development.
C
So
we
need
to
have
this
early
on
in
order
to
have
a
functional
embryo,
but
these
processes
cannot
be
interrogated
directly
in
humans
as
they
occur.
Post
implantation
here,
we
report
an
organoid
model
of
neural
tube
extension
derived
from
again
human
pluripotent
stem
cells.
So
this
is
a
stem
cell
that
you
make
from
differentiated
cells,
but
they
behave
like
stem
cells.
These
aggregates
are
called
what
they
say:
caudalized
with
the
antagonism
enabling
them
to
recapitulate
aspects
of
morphological
and
temporal
gene
expression.
C
So
this
is
just
a
construct
they're
putting
into
the
cell
and
they're
getting
or
into
the
structure
so
that
there's
a
there
are
these
signals
that
they
use?
One
of
them
is
wind
signaling,
and
it's
supposed
to
shape
the
this
sort
of
it
gives
it
an
orientation,
so
they
basically,
this
went
agonism
acts
to
orient
the
cells
in
in
one
direction
or
another
and
they're
able
to
cautize
it,
meaning
they
put
it
towards
the
tail
end
of
the
structure
and
they're
able
to
get
the
expression
that
they
desire.
A
A
C
I
I
didn't
know
that
I
didn't
know
that.
Well,
I've
heard
of
some
of
these
things
where
you
know
there
were
it's
actually
like.
Like
you
know,
you
have
physics
that
act
in
the
same
way
where
there's
like
this
physical
effect
that
yeah
a
lot
of
these
exp,
these
experiments
they're
using
a
lot
of
these
signaling
mechanisms,
but
when
they
take
the
cells
out
of
the
embryo
context,
it's
interesting
but
you're,
saying
that
there's
like
a
physical
thing
that
they
should
be
investigating
in
these
models
as
well.
A
A
C
So
this
is
this
basically
goes
through
the
molecular
aspects
of
this
critical
threshold
of
antagonism,
stimulate
stimulated
single
axial
extensions,
while
maintaining
multiple
cell
lineages,
the
organoids
displayed
regionalized
anterior
to
posterior
hox,
gene
expression
with
hindbrain
regions,
spatially
distinct
from
brachial
and
thoracic
regions.
So
these
are.
C
So
I
think
that
and
then
that
has
relevance
to
nervous
system
development.
So
I
think
that
that
group
of
papers
maybe
was
a
little
bit
spread
out,
but
I
think
there's
a
definitely
a
theme
there
and
I
wanted
to
go
through
that.
I
wanted
to
kind
of
tie
together
some
of
the
stuff
we've
been
doing
with
with
like
ancient
embryos
and
kind
of
modern
embryos
and
things
like
that
and
behavior
as
well.
So,
let's
see
we
have
some
things
in
the
chat.
Oh,
you
wanted
to
say
something.
Susan.
A
C
A
C
A
C
A
A
C
Yeah
I've
heard
of
that,
and
so
that's
something
that
yeah
we
haven't.
You
know
we
sometimes
you
talk
about
the
physics.
Sometimes
we
talk
about
the
chemistry
and
it's
kind
of
interesting,
how
they
intersect
and
there's
also,
of
course,
soft
active
materials,
which
is,
I
think,
something
you're
probably
into
as
well
and
they've
been
able
to
take
soft
active
materials,
things
like
gels
and
and
sand,
and
things
like
that
they've
been
able
to
actually
observe
a
lot
of
things
that
look
like
they're
sort
of
like
biological
pattern
formation.
C
A
C
Yeah
yeah,
if
we
go
back
even
to
like
the
early
embryos,
and
we
don't
really
know
what
early
I
mean
early
earth.
History
was
a
bit
different
than
now.
You
had
like
ice
ball,
earth,
snowball,
earth
and
other,
like
environmental
things
going
on
at
the
time.
So
it
really
might
be
interesting
to
see
like
these
early
embryos.
What
were
they,
what
kinds
of
physical
forces
were
they
experiencing?
Were
they
very
similar
to
what
we
have
now
or
because
you
know
embryos
sort
of
evolved
in
this
or
we
can
see?
A
C
And
that's
that's
of
course,
yeah
earth
history.
You
had
a
lot
of
periods
where
you
had
fluctuations
in
co2
and
temperature,
and
you
know
it's
like.
We
didn't,
of
course
start
with
mammalian
embryos,
but
you
know
we
ended
up
with.
They
had
to
have
similar
conditions
and
in
the
so
you
know
it's
like
we
had
eggs
and
then
we
had
embryo
we
moved
to,
like
you
know:
mammalian
embryos
you'd
have
that
as
like
an
option
but
yeah.
What
were
the
conditions
that
they
needed
to
have.