►
Description
DevoWorm meeting: May 4, 2020. Attendees: Richard Gordon, Ujjwal Singh, Bradly Alicea, Susan Crawford-Young, and Steve McGrew. Look for the "May the Fourth" easter egg at 17:47!
A
E
D
A
A
A
Well,
I
mean
I,
guess
it
depends
on
wonderful
new
ones.
Our
paper
on
that
about
why
you
would
have
and
then
not
just
like
free
symmetry,
where
you
have
all
different
varieties
and
it
seems
like
you
have
a
couple.
You
know
you're
a
five
fold
fourfold.
Maybe
fool!
You
don't
really
see
threefold
very
much,
but
it's
gonna.
D
E
G
A
E
H
A
J
K
H
E
H
B
L
A
H
H
A
So
the
first
thing
I
wanted
to
mention
here
is
that
today
is
May
the
4th,
so
it's
May
4th
be
with
you
it's
Star
Wars
day,
so
people
like
to
put
things
online
and
I
found
something
kind
of
interesting,
if
maybe
not
useful,
but
interesting,
Star,
Wars
and
spreadsheets.
So
this
is
are
people
who
are
in
Excel
spreadsheets
and
in
this
case
it's
different
images
from
Star
Wars.
A
So
each
of
these
it's
like
pixel
art,
but
each
of
those
pixels
is
a
style
in
Excel
and
they've
changed
the
value
to
some
color
and
so
they've
been
able
to
make
these
different
graphics.
If
that
type
of
methodology-
and
then
this
is
an
API
for
generating,
like
the
database
for
different
things
and
the
Star
Wars
universe
and
that's
executed
in
Excel
anyways,
it's
for
actually
in
Google
sheets
anyways
may
the
force
be
with
you
so
so
this
is
the
time
from
giving
it
to
this
group
on
Wednesday.
It's
a
group,
that's
online.
A
They
can
start
up
these
online
talks
since
everyone's
been
warranted
and
they've
had
some
pretty
good
talks
and
I
have
them
on
YouTube
I.
Think
I
sent
on
a
link
in
the
weekly
email
they
they
have
like
my
two
talks
a
week,
that's
an
hour,
long
and
Wednesdays,
and
so
this
one
is
the
they
call
it.
The
virtual
worm,
so
I
feel
kind
of
at
home
with
that,
given
what
I'm
presenting
on
here.
So
this
is
a
developing
virtual
world.
So
this
is
a
dad,
a
theoretical
synthesis.
A
This
is
basically
a
lot
of
stuff
we've
been
doing
in
the
group
here.
So
the
first
slide
is
talking
about
the
evil.
One
group
talking
about
the
linkages
with
open
worm
and
with
using
debt
secondary
and
tertiary
data,
and
then
the
actual
people
doing
the
work
or
the
topics
that
we
discussed
and
then
talking
about
the
sources
of
data.
So
people
will
be
interested
in
where
the
data
come
from.
So
there's
mostly
microscopy
data,
maybe
some
like
it
or
data.
A
A
So
what
that
refers
to
is
this
idea
that
you
can
take
a
bivariate
graph
say:
draw
an
elephant
on
it,
given
the
right
equation,
and
then
you
can
actually
make
it
animate
it
with
additional
equations.
The
point
of
this
parable
is
that
you
can
simulate
something
that
looks
like
an
elephant,
but
it
isn't.
You
know
it
may
not
be
reflected
something
real
in
the
world.
So
you
know
you
have
this
problem
in
modeling,
where
you
can
model
something
that
looks
pretty
good,
but
it's
actually
not
what
you
want.
A
If
you
just
you
know,
you
can
recreate
a
lot
of
things
with
just
a
few
parameters
which
tree
stands.
In
contrast
to
all
of
these
boring
stuff
we've
talked
about,
which
is
where
they're
using
you
know
millions
and
millions
of
parameters
to
get
answers
that
reflect
the
real
world
and
then
to
remind
people
again
that
you
want
to
avoid
spherical
cow.
A
So
this
is
something
that
takes
talked
about
and
I
have
an
animation
here
about
how
going
from
the
sparrow
to
a
cow,
and
so
that's
a
reminder
that
we're
working
like
from
an
embryo
to
a
asymmetric
organism
and
again
throw
them
into
the
stop
and
so
I'm
gonna
kind
of
go
into
some
recent
attempts
at
synthetic
embryology.
So
people
have
actually
attempted
to
model
embryos.
Synthetically
I'm
reviewing
this,
because
I
want
to
give
people
an
idea
of
sort
of
what
it
means
to
be
sort
of
artificial.
A
As
a
you
know,
what
an
artificial
embryo
might
look
like.
So
there's
one
there's
they're
examples
from
biology
where,
if
culture
and
artificial
human
embryos
set
up
for
generator
and
organize
ourselves,
they
call
these
things
synthetic
chemicals,
and
so
they
can
actually
observe
a
lot
of
different
aspects
of
marketed
ilg
learning
systems.
So
there's
a
paper
here
from
nature
on
that
topic.
A
This
is
actually
from
a
paper
that
that
Steve
brought
up
to
my
attention,
and
this
is
something
called
the
digital
embryo.
So
this
is
something
where
you
have
a
an
organism
that
has
this
sort
of
development
like
C
elegans.
It's
a
mosaic
development
and
they're
able
to
track
the
cells
in
early
development
there
through
the
single
cell,
sequencing
and
they're
able
to
map
those
that
data
to
virtual
model
of
the
embryo,
so
here's
the
embryo
and
they
can
have
molecular
yeah,
so
we're
we're
kind
of
doing
something
like
that
in
a
way.
A
A
A
Physics,
I
guess
the
phenotype.
Actually
they
all
sort
of
involve
physics,
but
maybe
the
phenotype
and
behavior
are
most
physical.
Yes,
then,
actually
I
was
gonna
point
out,
so
you
have.
You
can
represent
the
entire
thing
from
genetics
to
behavior,
because
you
don't
really
need
to
do
that.
You
just
need
to
represent
a
subset
of
those
and
you
can
get
a
pretty
good
approximation.
A
The
question
is:
what
next
should
you
choose
and
so
then
I
said
they
claim
that
well
we're
focusing
mostly
a
phenotypic
and
behavioral
representation,
but
and
those
are
most
the
most
physical
as
opposed
to
molecular
genetic.
That's
going
to
be
physical
as
well,
depending
on,
if
you're,
looking
at
like
distributions
of
molecules
in
space
or
or
whatever.
A
So
that's
we're,
gonna
focus
on
these
two
levels
of
representation,
and
so
then
I
get
into
the
five
dimensional
data
structure,
which
is
a
basically
the
way
we
characterize
the
money
extreme.
So
we
have
three
dimensions
of
space,
so
we
have
these
cell
centuries
in
space
which
are
generated
by
self
tracking
and
then
those
are.
Those
are
boiled
down
to
a
series
of
cells
in
the
embryo.
So
this
is
like
the
bunch
of
replicates
and
you
do
an
averaging
percieve
smoothing
procedure
to
find
the
cell
positions.
A
But
then
you
also
have
two
additional
parameters
and
those
are
lineage
time,
which
is
this
T
value,
which
goes
from
single
cells
or
multiple
cells
through
cell
divisions
and
this
I,
which
can
represent
the
number
of
things.
If
it
be
along
the
anterior-posterior
axis,
it
could
be
a
physician
in
lineage
tree
they're,
a
bunch
of
things
it
could
be,
but
that's
another
v,
/
ammeter,
and
so
in
this
case
we
have
call
context,
but
you
know
I,
guess
it's
a
pretty
general
parameter.
A
This
is
an
example,
a
2d
array
of
cartoon
cells,
so
you
have
3d
space
and
then
you
have
time
and
content,
and
so
that's
that's
the
basic
idea
of
how
you
approach
this.
So
there's
a
paper
that
was
done.
Of
course,
this
is
from
dick
and
I
on
Aaron
C
elegans,
Acciona
intestinalis,
and
this
is
the
tuna
cat
or
sea
squirt,
and
this
was
actually
one
of
the.
This
was
actually
the
the
model
that
I
showed
of
the
Morpho
seat
was
the
ciona.
A
It
was
a
tunic
in
just
like
this,
so
all
we
developed
if
you
have
this
ball
of
cells
and
it's
pretty
deterministic
in
terms
of
its
cell
divisions,
and
then
it
turns
into
this
thing
here
which
has
a
tail
and
head
and
then
ultimately
it
becomes.
This
looks
almost
like
a
closet
on
the
bottom
of
the
ocean.
Doesn't
it
go,
but
it
is
through
several.
A
But
this
is
really
comparing
the
earlier
two
organisms
so
and
then
these
two
organisms
are
right
here.
This
is
the
adult
tunicates,
and
this
is
the
adult
C
elegans
and
each
of
those
organisms
are
both
a
mosaic
development,
which
is
this
idea
that
developmental
cells
of
a
predetermined
fate,
and
so
they
exist,
C,
elegans
and
CNN
and
other
tunicates
there,
mostly
of
this
type,
although
they
do
exhibit
some
aspects
of
like
a
regular
typical,
which
are
represented
also
by
fishes
in
mammals.
L
A
Fishes
and
mammals
are
much
more
regulative
and
the
difference,
of
course,
is
that
the
cells,
the
cell
fate,
is
predetermined
in
mosaic
development.
So
you
start
with
the
single
cell
and
you
go
to
multiple
cells
and
the
fate
can
be
traced
through
this
lineage
tree
and
it's
always
the
same.
Every
time
you
every
lineage
tree
you'll
encounter
every
individual.
You
come
it's
the
same
as
opposed
to
regular
development.
Where,
when
you
get
this
sort
of
division
of
cells
in
an
embryo,
every
cell
can
be
different.
A
A
A
So
alright,
so
this
is
an
example.
One
of
the
analysis
we
did.
This
is
cell
volume
versus
division
time
in
minutes,
and
this
is
where,
of
course,
is
comparing
the
two
organisms.
Yet
you
know
within
a
hundred
cells
of
the
single
cell
state,
so
we
have
a
bunch
of
cells
and
basically
we're
showing
that
there's
a
decrease
in
cell
volume
before
division
time
in
minutes,
although
siano
shows
some
interesting
patterns,
it's
a
little
bit
different,
imperfect
and
some
of
the
reasons
for
that
have
to
do
with
the
way
the
ciona
embryo
divides.
A
It's
also
notable
that
the
ciona
embryo
is
a
four-fold
symmetry,
so
the
way
the
ciona
and
way
it
works
is
it
or
founder
cells,
and
then
each
of
those
four
cells
represent
sort
of
a
pole
symmetry.
So
those
four
parts
sort
of
divide,
there's
somewhat
symmetrical
to
each
other,
but
there's
some
differences
in
the
lineage
tree.
So
that's
why
it's
perhaps
you
see
this
difference,
but
you
can
also
discover
new
relationships
and
developments
all
ages.
So
this
is
something
we
discovered
this
paper.
A
Basically,
most
of
them
are
not
I'm,
not
beam
on
a
B
range,
so
the
not
maybe
sub
lineages
in
the
posterior
for
every
embryo,
and
these
are
cells
that
are
going
to
go
on
to
form
some
of
the
organs
like
muscle
and
gut,
as
opposed
to
the
a
beasts
lineage
there's
one
year,
that's
a
var,
but
you
can
see
it's.
You
know
it's
maybe
not
close
to
this
line.
A
So
there's
something
going
on
here
that
might
be
of
interest
further
investigation
and
also
in
you
see
the
same
thing
kind
of
in
seonah,
where
you
have
these
cells
that
are
outliers
out
in
this
part
of
the
distribution.
So
there
are
longer
lived
cells,
their
cell
volume.
This
is
log
cell
volume,
they're
longer
lives
in
general,
so
you
have
this
tale
of
longer.
A
Why
I
took
that
digression
into
this
business
about
synthetic
embryos?
And
so
we
have
this
type
of
data
like
I,
showed
you
the
centroid
from
C
elegans,
but
we
also
have
it
for
B
fruit
fly
Drosophila
and
for
zebrafish,
and
so
these
are
examples
of
this
embryos
for
each
days.
These
are
the
types
of
reference
point
representations
we
can
build,
and
so
we
have
data
for
a
number
of
time
things
these
that
are
taken
from
SS
PD,
which
is
a
database
we're
not
a
Reich
and
in
Japan
it's
a
Ken
Hall.
A
It
was
actually
been
part
of
our
dream
on
and
off
and
they
have
a
lot
of
different
data
sets
with
cell
tracking
down.
So
we
can
do
this
in
a
number
of
potentially
in
a
number
of
organisms,
but
we
do
partners
that
come
in
and
building
this
representation
of
a
meta
embryo
and
so
one
of
the
other
things
you've
done
in
the
group.
Look
at
the
distribution
of
major
events,
so
we
have
I
created
this
map
for
a
paper.
A
It's
kind
of
mapped
out
a
lot
of
the
main
events
can
the
embryo
going
from
fertilization
to
the
connectome
through
behavioral
milestones,
and
you
can
see
that
it
goes
to
hatch,
and
this
kind
of
gives
us
a
baseline
for
kind
of
what
the
cells
are
doing
is
they're
dividing
in
differentiating.
So
we've
done
things
along
this
timeline
in
terms
of
characterizing
the
embryo
quantitatively,
so
we
looked
at
cell
like
for
the
embryo
networks,
so
we've
looked
at
the
proximity
of
cells
related
to
sort
of
the
shape
of
the
embryo
takes
over
time.
A
So,
as
we
can
characterize
these,
you
can
get
a
handle
on.
You
know
how
we
might
characterize
an
embryo,
we're
howling,
my
characterize
developing
as
sort
of
this
abstract,
more
abstract
thing,
and
we
can
also
look
at
the
distribution
of
cells
as
they
energy.
So
this
is.
These
are
all
the
families
of
cells
and
their
release
of
time
in
their
number.
So
you
have
different
families
that
emerge
in
time.
A
There
know
their
distribution
over
time,
so
you
can
see,
there's
some
families
that,
like
hypodermis
cells,
which
proliferate
early
and
intestinal
cells,
and
then
there
cells
that
are
more
specialized,
that
different
proliferate
late
and
you
can
even
compare
these
distributions
and
histograms
or
good
look
at
different
classes
of
neurons
interneuron.
We
have
those
data
available
and
then,
finally,
you
can
simulate
a
timing
of
divisions
and
differentiations
using
a
mathematical
model.
So
this
is
something
that
I've
been
working
on
with
someone
who
doesn't
come
to
the
group
meetings,
but
we've
been
thinking
about
this.
A
See
I
don't
have
to
speed
this
up
like
when
I
give
it
because
there's
too
much
in
here,
but
I'm
gonna,
probably
trim
some
of
it.
So
then
I
talked
about
this
information
isometry
method
says,
is
a
method
that
we
worked
on
a
couple
years
ago,
where
given
Q
regulative
running
or
developmental
Sony,
it
is
based
on
different
criteria.
So
if
you
have
two
trees
that
are
based
in
different
criteria,
how
do
we
assess
the
information
of
the
developmental
process?
A
And
so
this
is
a
reminder
that
developmental
cell
lineages
contain
multiple
sources
of
information,
and
then
you
can
compare
levels
lineage
tree
topologies
using
visualization
and
information.
So
that's
why
we
look.
We
look
at
these
two
trees
by
comparing
them
using
Hamming
distance,
which
is
like
an
absolute
distance
here
between
the
trees.
So
this
is
an
example
of
we're
comparing
two
trees,
we're
putting
the
values
on
this
tree.
A
So
this
in
this
case,
our
positions
are
one
one
position
away
for
each
of
the
two
cell
cases
and
maybe
three
from
zero
to
three
positions,
away,
actually
one
to
four
positions
away
for
each
of
these.
So
these
are
the
binary
again
identities
of
the
villains
at
the
end
of
this
tree,
and
these
are
the
distances
you
can
see
how
that
plays
out
in
the
tree,
but
we
can
also
plot
this
on
a
bivariate
graph.
A
So
we
can
use
this,
which
is
chart
to
show
how
you
can
draw
lines
between,
say,
like
this
one
cell
node
and
the
to
settle
nodes
here,
and
we
can
draw
arrows
to
show
the
relationships
and
then
these
dots
are
freezing
distances.
So
we
can
look
at
the
distance
becoming
two
trees
in
that
way,
and
so
this
is
a
bivariate.
Practically
all
the
nodes
are
arrayed
in
this
type
of
isometric
raft
organization,
and
each
of
these
dots
as
colors
represents
a
different
Hamming
distance.
A
So
you
can
see
there's
variety
across
the
terminal
nodes
of
the
tree.
In
this
case
this
is
a
120
or
180
so
or
something
like
that,
because
128,
so
it
was
evenly
divided.
So
you
have
different
sub
trees.
It
looks
like
a
different
Hamming
to
it's
almost
as
if
you're
tutoring,
that
you're
using
are
swapping
subtrees
and
they're.
You
know
they're,
so
you
have
these
distances
of.
A
You
know
a
Hamming
distance
of
five
and
Hamming
distance
of
three
and
so
forth,
so
it
gives
people
a
sense
of
like
if
you're,
using
two
different
criterion,
the
goal
of
lineage
tree.
What
the
difference
is
between
you
and
then
this
is
an
example
of
sort
of
benchmarking,
this
against
random
trees.
So
if
I
just
generate
a
bunch
of
trees,
randomly
and
I
compared
to
C
elegans
C
elegans
has
a
higher
level
a
higher
amount
of
information.
A
If
I
compare
C
L
in
the
street
with
any
set
of
random
trainees,
C
elegans
tree
is
much
more
information
in
it
than
random
attorneys,
and
so
you
can
see
this
is
sort
of.
If
you
think
about
just
random.
This
is
you
know
random
signal,
and
then
this
is
actually
information
in
the
biological
tree.
So
maybe
there's
another
application.
A
Food
is
looking
at
sort
of
the
information
in
that
developmental
process
and
then,
of
course,
you
can
look
at
subtree,
so
you
can
look
at
the
different
sub
trees
by
themselves
to
see
what
the
Hamming
distances
are
between
them,
and
so
there
are
a
lot
of
options
for
analysis,
and
so,
but
course
this
only
works
for
bilateral
symmetry.
Very
well
so
for
cases
where
the
four
feet
symmetry.
A
Maybe
we've
made
think
of
a
more
advanced
solution
to
this,
and
this
is
one
of
the
things
that
is
being
not
going
very
far
in
until
recently,
but
is
a
potential
new
direction
for
us,
which
is
using
these
two
symmetric
networks
and
better
measures
of
information.
So
things
like
mutual
information,
where
you
represent
the
symmetry
on
these
trees
or
on
these
networks
and
then
use
a
different
type
of
information
theory
to
get
the
information
on,
and
so
then
finally
I'm
going
to
talk
about
this.
This
is
some
empirical
science
that
was
done.
A
A
Well,
we
have
weighed
wild
types
and
we
lead
ins,
and
this
graph
represents
front
of
the
measurement
of
the
worm
without
starvation
and
the
measurement
of
the
world
with
starvation.
And
so
the
idea
was
that
the
worms
were
starved
in
a
certain
period
of
larval
development,
also
one,
and
if
you
starve
them
in
l1,
they
can
survive
for
long
periods
of
time
without
food,
but
it
has
a
consequence
on
their
future
development.
A
And
so
that's
our
trying
to
get
in
this
set
of
analyses
is
that
you
starve
them
in
development
larvae
develop
how
many
offspring
do
they
produce
in
over
five
days,
which
is
their
length
of
their
reproductive
life.
So
we
measured
for
five
days.
So
this
is
an
example
where
you
have
sort
of
this
control,
which
is
where
you
have
the
l1
worm,
which
is
a
normal
worm,
and
then
you
just
let
it
go
to
the
adults
and
you
let
it
reproduce.
And
then
you
end
up
with
a
curve
of
the
river
which
is
by
contrast.
A
This
orange
curve
is
the
curve
that
happens
when
you
take
an
l1
and
you
starve
it,
you
put
it
in
you,
put
it
in
media
like
a
buffer
media.
You
wouldn't
sit
in
this
for
about
five
days
and
then
you
replate
them
and
you
let
them
grow
or
don't
like
me.
What
can
you
measure
them
in
the
offspring
and
so,
as
you
can
see,
there's
a
decrease
both
in
the
number
of
peak
number
of
offspring
and
the
timing
of
their
reproduction.
A
So
this
is
the
difference
in
n2,
which
is
the
wild
type
of
so
there's
no
genetic
mutation.
Here,
no
no
define
mutation,
and
this
is
one
of
the
things
you
can
do
with
these
data.
So
there's
a
difference
here
of
clearly
well.
What
we
want
to
know
is
like
what
are
the
differences,
a
difference
between
different
time
points,
and
so
in
this
case
we
can
look
at
the
area
under
the
curve,
or
rather
a
differential.
A
G
A
Where
we
have
one
curve,
that's
bigger
than
the
other,
we
don't
know
what
how
much
it
is
so,
but
we
can
use
this
differential.
You
see
measurement,
we
can
use
this.
We
can
represent
both
of
these
distributions
in
a
geometric
space
and
then
calculate
the
size
of
this
overlap
or
the
size
of
the
overlap
and
then
the
size
of
the
curve.
That's
bigger
and
subtract
the
overlap.
You
look
at
these
numbers,
so
this
is
so
early
on.
The
unstart
wild-type
is
producing
a
lot
more.
A
It
has
a
peak
at
about
two
days
and
then
it
declines
to
a
very
few
offspring
later
in
its
life
and
so
there's
a
positive
amount
of
reproduction
for
the
one.
That's
not
starved,
but
the
one
that's
starved.
It's
getting
a
little
bit
later
started,
so
it
actually
doesn't
do
much
reproduction
until
day,
three
or
then
it's
reproducing
more
than
the
one
that
wasn't,
and
so
we
have
this
measurement
here.
So
we
have
these
numbers
to
attach
to
this
process
and
then
pick
them
up
again
at
the
different
difference.
A
So
we
have
three
different
components
here
we
can
look
at
and
then
the
lot
of
data
and
this
by
reference
paper.
If
you
want
to
know
more,
but
there
are
three
different
things
so
there's
a
different
shift
in
askew
and
the
distributions.
So
this
is
a
shift
in
the
Petrie
production,
two-layer
point
of
adulthood.
So
when
you
starve
them
get
the
skew
in
this
machine,
it
pushes
it
back
when
they're
the
peak
the
peak
reproductive
period,
you
also
get
away
so
there's
a
delay
and
sort
of
were
a
little
bit
a
reproduction
of
her.
A
So
you
can
see
this
these
points
one
and
deserve
sampled
at
every
daily.
Twenty
so
day,
one
there's
no
production
at
all
day,
there's
very
little
mini
business.
You
will
write
to
the
peak
at
day
three,
so
you
can
refer
this
as
a
delay
in
reproduction,
so
you're
not
putting
their
eggs
out,
for
you
know
another
day
after
they've
been
started,
and
you
can
also
let
it
learn
also
look
at
the
kurtosis,
which
is,
if
you
look
at
the
paint
reproduction,
what's
the
change.
A
So
in
this
case
you
have
a
reduction
of
HP
and
that's
in
statistics.
They
call
kurtosis.
We
can
call
it
kurtosis.
It
just
means
that
the
e
the
peak
value
is
long.
So
that's
all
what
we
do
well
types,
but
what
about
more
other
defined.
So
in
this
case
you
look
different,
define
users
and
they're
all
this
AAK
one
and
two
variety.
So
a
Kay's
of
team
met
that
controls
metabolism,
and
so,
if
you
start
in
the
literature,
they
suggest
a
few
star
baby
names.
They
become
like
the
have
like
different.
A
They
have
deficits
in
terms
of
reproduction
and
other
things,
but
sometimes
they
also
have
ones
that
were
defects.
And
so
this
is
the
these
are
the
curves
for
a
k1
and
k2
and
then
a
double
mean
that
was
created
for
this
purpose,
and
you
can
see
that
for
a
k1
there's
a
you
know
not
very
much
delay
at
all.
There's
no
delay,
but
there's
a
kurtosis
and
no
skew
for
the
double
meaning.
A
There's
a
little
bit
of
positive
student
for
the
one
that
was
starved,
there's
no
delay
and
there's
a
little
bit
of
kurtosis,
but
for
a
k2.
For
some
reason,
none
of
these
rules
apply.
It
seems
like
the
ones
that
are
starved
kind
of
just
wait
until
like
a
four
or
five
to
jump,
all
their
eggs
and
reproduce.
So
this
is
what
they're
doing
here
is
perfect,
and
so
you
can
see
them
comparatively.
A
A
The
a
k1m
w,
so
it's
really
interesting,
but
this
aap
not
really
sure.
What's
going
on
there
because
clear,
maybe
this
day
a
k2
is
doing
some
sort
of
compensatory
mechanism.
Nay,
a
k1
is
alone
is
basically
getting
fit
with
a
penalty
for
being
starved,
and
so
we
have
another
question
here.
So
this
is
the
examples.
A
So
then
this
is
the
last
slide.
Thanks
to
our
contributors,
unable
mention
like
all
the
contributors
that
we
have.
These
are
not
all
the
contributors,
but
this
is
are
sort
of
all.
People
want
to
think
they
contributed
a
significant
amount.
You're
welcome
to
become
a
contributor.
We
have
our
weekly
meetings
on
YouTube.
We
have
a
lot
of
sponsors
here
that
have
contributed
to
our
efforts,
overview
of
organizational
sponsors,
and
so
then
that's
it.
A
That
would
be
the
whole
talk
and
then
see
we
had
a
question:
how
does
this
all
line
up
with
energy
to
your
unit?
So
the
robot,
the
mutants,
are
the
starvation
examples.
So
starvation
is
like
what
happens
in
C
elegans
larval
development?
Is
they
have
these
protective
things
for
energy
so
when
they
get
starved
they're
using
energy
they're,
putting
energy
in
don't
like
surviving
the
starvation,
and
so
when
they
do
that
they
it's?
You
know.
It
comes
at
a
cost.
A
I
think
later
in
life,
not
really
clear
how
this
works,
but
there's
a
molecular
pathway
that
controls
this.
So
when
you
start
of
a
it's
hitting
the
ember,
it's
hitting
the
larval
worm
in
in
a
certain
way.
So
if
you
have
this
a
a
k1
mutation,
yet
basically
Chris,
you
know
reduces
your
ability
to
respond
to
that
stress
and
development
later
in
life.
It's
a
lot
of
the
worms
die
or
they
don't
reproduce
very
much,
but
there
can
also
be
a
compensatory
effect.
A
H
A
Yeah,
oh
yeah
yeah,
so
they
actually
have
those
in
C
elegans.
They
do
epigenetic
type
epigenetic
inheritance.
We
actually
talked
about
that
in
the
group
there
several
weeks
ago,
but
this
is
like
a
little
bit.
Different
I
mean
they
look
at
like
you
know,
for
multiple
generally
effects
and
multiple
generations,
maybe
three
generations,
and
actually
in
C
elegans
they've,
established
that
that's
a
thing
that
that's
a
very
strong
effect,
but
only
for
a
couple
generations.
H
H
H
Let's
see
it's
more
squares,
rectangles,
Pentagon's,
hexagons,
okay
and
when
I
looked
at
them
and
I
realized
that
archaea,
which
are
prokaryotes
they're
about
40%
of
the
prokaryotes
on
earth,
often
are
also
polluted.
Let's
see
is
it
is
a
triangle
under
your
square,
one,
here's
what
they
do.
There's
a
contaminant
law,
okay
and
also
diatoms,
are
also
often
very
regular,
polygons,
there's
a
triangular
diatom,
so
these
are
eukaryotes
now,
of
course,
and
you
can
see
some
similarities,
there's
a
squirrel
and
things
when
this
sort
of
pentagonal
he's
one
at
600.
Okay,.
H
With
the
idea,
which
is
not
popular,
called
archaea,
first
there's
the
first
living
organisms
on
earth
or
archaea,
okay,
and
we're
trying
to
figure
out
to
eat
two
things
here,
and
that
is
what
is
the
if
one
assume
that
mice
started
his
oil
droplets,
which
became
alive
later
on
in
the
form
of
label
archaea
brought
my
two
conditions
me:
the
partners
to
what's
happened.
Okay
now
what
I've
been
reading?
Literature
on
the
origin
of
life
and
most
people
worry
about
the
chemistry
and
there's
nothing
shape,
they
just
assume
a
spherical
droplet,
and
that's
it.
H
H
So
the
question
is:
if
we
assume
that
life
started
out
as
a
as
a
polygamy
flat
shape
on
earth,
you
know
this
could
possibly
have
started
with
these
shape.
Droplets,
which
are
kind
of
weird
I,
mean
you
know
we're
talking:
oil,
droplet
and
water.
Here's
a
sphere-
and
this
is
janely
true,
but
if
you
pull
it
slowly,
you
get
this
flat
part
of
it.
Okay,
so
I
mean
this
is
what
I'm
working
on.
H
H
You
get
the
picture
here:
okay,
the
surface
of
archaea
and
bacteria
by
the
way,
and
also
little
coronavirus
and
these
s
layer,
proteins
which
are
stuck
in
the
membranes
and
for
they
considered
to
form
an
extra
layer
outside
the
cell.
Okay,
these
different
between
different
species.
But
they
come
in
various
li
for
you,
patience
and
a
little
bit's.
Translating
that
if
you
look
at
and
see,
if
you
look
at
the
okay,
the
kind
of
various
the
the
Escalade
proteins
can
have
various
the
various
configurations.
H
G
H
H
H
Okay,
so
the
notion
I
came
up
with
is
that
these
are
energy
configurations,
reflect
two
sided
polygons
and
the
energy
is
deep
or
tional
to
the
area.
If
we
assign
an
area
to
the
self-
and
we
sign
a
creator
and
cut
number
of
corners
I
can
this
formula
for
the
total
energy?
Oh
we're
a
sec,
our
free
parameters,
okay,
and
what
I've
been
doing
is.
H
But
it's
the
example
of
one
of
these
simulations,
a
plot,
the
energy
versus
the
number
of
sides
in
these
polygons,
and
they
sometimes
form
of
each
banana.
So
for
this
particular
set
of
values,
I
would
predict
you
get
mostly
six-sided.
Some
507
made
etcetera
very
few
for
side.
You
don't
get
any
triangles
in
this
case.
H
L
H
Bacterium's
prokaryotes
have
surfaces,
they're,
a
brand
negative
of
grandpas,
music,
stayin
positively
or
negatively
to
a
certain
state
and
then
I'm
dictator,
and
how
much
sodium
they
can
take
minimum.
The
maximum
will
react
in
the
optimum
temperature
living
take
the
temperature
range.
Many
of
these
are
certified
as
a
little
bit
high
temperatures
and
they
have
lured
strange
creation
according
to
get
the
pH
seven
each
chamber:
okay,
okay,
as
reported
the
literature
and
as
I,
see
it
in
the
picture.
So
if
it's
italicize
here,
for
example,
this
here's
one
which
was
reported
as
irregular
cosine.
H
H
H
F
H
So
so,
and
this
theory
might
hold
up
it's
it's
very
simple.
Basically,
it's
saying
that
the
the
energy
is
primarily
in
the
surface
layer,
proteins
and
the
energy
is
different
if
their
units,
but
if
they're
stuck
at
the
perimeter
for
us
they're
stuck
in
a
corner,
but
you
get
different
energies
for
those
two
cases.