►
From YouTube: DevoWorm (2023, #4): Cell packings, Modeling the origins of early life, fossil embryos and brains.
Description
Cellular packings and tensegrity networks, new open-source issues for DevoLearn and DevoGraph, Markov blankets and cell membranes, origins of early life and life from vesicles. Origins of sexual reproduction as cellular integration. How long do we have to wait for life? Attendees: Richard Gordon, Susan Crawford-Young, Sushmanth Reddy Mereddy, Barry Robson, and Bradly Alicea
A
B
B
B
B
A
A
So
they
finally
the
element
models
and
it
can
do
I'm,
just
a
10-seater
structures
and
then
it
can
do
form
Finding
and
then
finite
element
analysis.
So
this
is
pretty
pretty
good.
I
should
try
this
or
show
this
as
an
alternative
to
what
I'm
doing
so.
It
says
this
is
what
it
is
like
yeah.
It
says:
Matlab
function,
so
there's
some
Matlab
and
I
have
Matlab,
so
it
would
be
foreign.
A
B
B
B
A
A
B
A
C
A
And
this
is
what
they'll
they'll
do
for
you.
If
you
have
ordered
bundles,
they
tend
to
to
look
close
if
they're
a
ring,
they'll
they'll
close
if
they're
an
ordered,
bundle
and
a
much
better
than
than
a
disordered
disordered
networks.
Well,
they
started
to
it.
So
nothing
I,
guess
this
might
be
wound
healing
that
dick
was
talking
about
all
right,
she's,
probably
showing
him
this,
but
anyway,
this
kind
of
looks
like
what
happens
in
wound
healing
in
the
tissue.
B
C
B
A
B
C
B
A
So
this
is
what
I
know
from
the
course
on
Coursera
cytoskeleton
course
in
the
Coursera.
Is
that
if
you
have,
if
cells
to
under
low
low
Force,
they
tend
to
do
this
and
as
the
force
increases
the
angle,
the
angle
between
these
the
actin
changes,
and
it
also
tends
to
grow
a
whole
bunch
of
actin
black
actin
bundles
at
the
surface?
A
B
A
C
A
C
A
A
A
It's
the
middle
part
and
it
is
flexible,
but
it
won't
be
flexible.
If
you
pull
on
all
of
these
elastics
in
in
different
directions,
you
need
to
pull
on
them
from
different
sites.
Oops
I
just
lost
okay
on
the
floor,
but
anyway
yeah
some
destabilize,
a
flagpole.
For
instance.
You
put
die
wires
down
right.
B
A
B
B
A
A
C
C
A
A
C
A
A
A
Oh
well
and
and
here's
the
I
was
just
showing
them
this
slide.
So
this
is,
these
are
actin
rings
that
they've
made
and
if
you
have
disordered
networks,
they
sort
of
close
close
in
on
themselves.
But
if
you
have
ordered
bundles,
you
get
a
closure
here.
So
this
is
kind
of
like
wound,
healing,
I
guess
and
then,
if
you
have
disordered
bundles,
nothing
happens.
D
A
A
There
must
be
some
myosin
and
let
me
see,
there's
myosin
associated
with
the
active,
because
there
always
is
only
in
this
experiment.
They
didn't
put
it
in
there,
although
it
looks
like
they
did,
because
this
looks
like
myosin
in
here.
Oh
so,
I'm
not
sure
yeah
I,
don't
know
if
it's
an
actively
myosin
thing-
or
these
are
just
simply
myosin
that
are
ordered
an
okay
I
need
to
read
this
paper
a
bit
more,
but
I'll
read
it
over
again.
It's
called
reading
it
again.
D
A
So
I
added
I
have
that's
what
I
added
to
this
and
I
found.
Github
Roots
can
say
really
models
on
it
for
my
essay
on
Pennsylvania
modeling,
which
I
have
to
do
players,
and
then
I
also
found
this
just
some
tables
where
they
ruined
the
cytoskeleton
and
then
looked
at
its
reaction
force
due
to
concussion
and
shearing,
but
they
didn't
do
tension.
So
my
favorite
experiment
is
incomplete.
B
A
A
A
A
B
So
let
me
share
a
few
updates
with
you
and
some
features
that
I
have
so.
The
first
update
is
on
the
Divo
learn
Devo
graph
repository,
and
so
this
is
a
kind
of
an
important
thing
we
had
such
month
ready
who
filed
an
issue
on
in
the
Devo
graph
repository
in
the
diva
learn
organization,
so
we're
at
the
Devil
in
organization
we're
in
devograph,
and
he
found
a
pretty
serious
issue
with
respect
to
dependencies.
So
we
have
some
dependencies
issues
in
the
devil
graph
stage
one
pipeline.
B
This
goes
back
to
like
last
Summer's
gsoc
and
some
of
the
things
we
did
over
the
fall
where
we
were
modifying
the
stage
one
pipeline.
This
is
the
pipeline
for
the
evograph
platform.
So
stage
one
is
where
you
get
segmentation
of
images
and
it
ties
into
diva
learn
as
well.
So
such
month
made
an
observation
here.
He
created
issue
number
one,
so
it's
open.
If
people
want
to
solve
it
or
people
want
to
address
it,
you
can
leave
a
comment
down
at
the
bottom.
B
B
There
are
a
lot
of
deprecated
libraries
that
are
being
used
for
Diva
learn.
These
are
creating
a
problem
when
we
run
the
collab
notebook.
So
if
you
run
the
collab
notebook
for
Diva
learn,
there's
a
problem
with
dependencies
that
you
hit,
especially
also
if
you're,
integrating
that
with
Devo
graph
and
the
libraries
for
these
should
be
updated
and
Diva
learn.
There's
a
requirements.txt
file
for
smooth
movement
stage,
one
pipeline
of
vivograph,
so
he
has
he's
documented.
B
The
following
errors:
there
are
a
number
of
like
psych
kit
is
deprecated,
numpy
is
deprecated,
pandas
is
deprecated
I'm,
not
really
sure,
like
there
probably
errors
in
the
code
that
need
to
be
resolved,
but
it
could
also
be
that
the
whether
these
packages
are
referenced
is
out
of
date.
So
if
someone
wants
to
take
up
this
issue
you're
more
than
welcome
to
do
it
six
months,
we'll
probably
work
on
it,
he's
Consulting
with
mayuk
and
also
with
ajiahang
who's,
the
sort
of
the
ringleader
of
the
devil
graph
group.
B
B
We
actually
have
three
pull
requests
open
and
eight
issues,
so,
if
you're
interested
in
looking
over
devoner-
and
this
is
going
to
be
for
the
summer
of
code
project
this
year-
key
to
making
Devo
graph
run
and
so
we're
trying
to
get
all
these
bugs
worked
out
by
the
application
period
for
summer
of
code.
But
this
is
also
something
if
people
want
to.
You
know,
propose
a
project.
B
Part
of
that
project
might
be
fixing
some
of
these
bugs
and
making
sure
everything
is
up
to
speed,
because
that's
one
part
of
Open
Source,
that's
very
important,
is
being
able
to
fix,
bugs
and
fix
dependencies
issues
when
needed.
So
this
is
a
good
again,
a
good
thing
to
kind
of
engage
with
before
the
summer
of
code,
but
also
you
know,
even
before
the
summer
of
code.
B
If
you
want
to
make
a
nice
open
source
contribution,
this
would
be
the
place
to
do
it,
and
so
you
could
go
to
the
people
learn
organization
at
github.comarn
either
go
into
the
divorce.
Do
the
learn
repository
or
the
diva
graph
repository
and
check
out
the
open
issues
with
respect
to
these
problems,
foreign.
B
Features
that
I
had-
and
these
are
things
that
I
found
over
the
course
of
the
week,
so
one
of
the
things
we'll
be
talking
about
in
this
meeting
are
the
origin
of
life
and
lipid
membranes,
and
so
there
are
two
features
I
have
for
that.
The
first
one
is
from
Cybertron,
blog
or
Cybertron
asks
blog,
and
the
tagline
of
this
blog
is
what
why
and
how
do
we
know,
and
so
this
is
I,
think
they're
taking
free
energy
principle
approach
to
lipid
membranes.
B
The
title
of
this
blog
is
Markov
blankets
versus
lipid
membranes.
So
they
start
by
talking
about
Nick
Lane,
who
is
an
excellent
Popular,
Science
writer
on
biochemistry
and
some
other
things
he
has
a
book
out
now
called
Transformer.
That's
not
the
machine
learning
type
of
Transformer.
It's
actually
transforming
chemistry
into
biochemistry
and
P
sort
of
the
origins
of
life,
and
so
it's
a
very
interesting
book,
very
wonderful
set
of
references,
a
nice
annotated,
bibliography
at
the
end
of
the
book
and
to
really
frame
some
of
these
issues
in
the
field
nicely.
B
So
watching
this
wonderful
Krebs
cycle,
RI
lecture
by
Nick
Lane.
This
is
the
lecture
here.
I'm
not
familiar
with
the
ri
series,
but
I
just
wanted
to
know
two
things
for
now:
one.
There
is
an
obvious
topological
parallel
between
systems,
markup,
blankets
in
mitochondria,
lipid
membranes,
so
there's
a
systems
level
construct
of
a
Markov
blanket,
and
this
is
something
that
the
free
energy
principle
relies
upon
to
sort
of
build
the
boundaries
around
systems.
So
a
Markov
blanket
is
something
you
put
over.
B
The
states
that
you're
interested
in
solving
for
and
the
blanket
can
be
approximated
to
give
you
a
boundary
of
your
system
systems.
Boundaries
have
always
been
a
contentious
issue
in
systems
modeling
finding
out
where
that
boundary
is
and
giving
a
justification
for
it,
not
just
in
a
definitional
sense,
but
in
a
mathematical
sense
it's
always
been
very
difficult.
B
So
Markov
blankets
give
you
a
tool
to
do
that
and
then
lipid
membranes
are,
of
course,
these
membranes
that
separate
these
in
inside
of
a
cell
or
the
inside
of
a
vesicle
from
the
external
environment.
Its
permeability
is
variable,
we'll
talk
about
that
later
in
the
meeting-
and
you
have
this-
you
know
so
it's
built
out
of
lipids
and
it's
selective
and
What.
B
It
lets
into
the
the
cell
or
the
vesicle
and
what
it
excludes,
and
so
this
is
important
for
compartmentalizing
biochemical
processes
or
other
types
of
things
that
we
might
be
interested
in
so
there's
it
provides
organizational
life,
so
this
actually
gives
you
abstractions
at
two
different
levels
of
of
systems,
the
information
processing
aspect
and
the
biochemical
processing.
So
elliptic
membranes
allow
you
to
think
about
systems
boundaries
at
the
biochemical
level
and
Markov
blankets.
B
So
that
was
the
first
parallel
and
two
early
on
Nick
Lane
makes
quite
a
few
remarks
against
the
informational
computational
View.
So
this
is
again
what
we
just
laid
out
in
the
first
point,
but
you
know
some
people
don't
view
that
as
something
that
the
way
you
should
view
nature.
So
you
know
some
people,
don't
think
you
should
view
nature
from
a
computational
point
of
view
that
it's
not
really
relevant
to
what's
going
on.
B
But
you
know
this
is
something
that
we
use
as
a
way
to
get
a
sort
of
a
quantitative
handle
on
things,
so
he
makes
quite
a
few
more
remarks
against
this
point
of
view.
It
highlights
the
informational
element
when
we
get
to
the
nucleotide
process,
so
fascinating
for
the
metaphysical
aspects
of
which
came
first,
which
is
of
course,
of
course,
what
his
talk
is
about.
So
this
is
in
in
the
book
Transformer.
B
B
That's
one
of
his
points
in
the
book
from
the
simplest
physiochemical
precursors
to
the
most
complex
multi-sold
creatures,
so
the
Krebs
cycle
is
proposed
to
be
sort
of
the
one
of
the
things
that
defines
Luca,
which
is
a
last
Universal
common
ancestor
and
then
from
there
you
see
it
in
every
form
of
life.
So
this
is
something
that
we,
you
know
we
can
focus
on.
B
Once
the
simplest
chemistry
exists,
Evolution
and
the
complex
follows
so
in
other
words,
you
have
this
simple
chemistry
and
it's
inevitable
that
life
will
evolve
from
that
and
that
complexity
will
emerge
from
that
and
I
think
that's
kind
of
an
interesting
point,
because
people
tend
to
always
throw
around
this
term
that
life
is
sort
of
rare,
that
you
don't
see
it
everywhere
and
yet
maybe
you
do.
Maybe
life
does
emerge
a
lot
more
than
we
think.
B
Know
that
we
don't
really
know
say
what's
going
on
another
planet,
so
we
don't
even
know
what's
going
on
in
the
extreme
environments
on
this
planet
very
well.
So
this
is
interesting
that
you
have
a
simple
chemistry
and
that
actually
life
might
be
quite
common
with
respect
to
that
chemistry
that
it
bootstraps
a
lot
of
living
processes
by
default.
B
B
Talk
this
blog
post
gonna
doesn't
talk
about
it
that
much
more,
but
it
does
get
into
some
of
these
issues
that
we're
interested
in
the
meeting,
and
so
so.
This
actually
connects
with,
like
this
sort
of
non-normal
cognition
that
we
talked
about
in
the
group
a
lot.
So
this
set
of
tweets
but
from
Ian
glendening
is
in
response
to
wearing
the
brain,
is
actually
a
very
good
resource
for
our
systems.
B
B
The
sort
of
diseases
which
people
are
more
interested
in
than
the
origin
of
life
to
the
whole
Earth
itself
as
a
self-regulating
organism,
mind-boggling
and
Mark
zones
right
at
the
end,
see
video
the
idea
of
a
fact
feeling
is
an
electrochemical
report
and
the
categorical
qualitative
state
of
the
cell.
So
this
is
interesting
because
you
have
this
idea
that
cells
have
feelings,
but
not
the
feelings
that
we
have
in
our
brains.
They're
different
types
of
like
behavioral
signals
or
signals
of
a
state
of
being
so
effect
and
feeling
might
be
you.
C
B
If
the
cell
is
sick,
it
has
certain
gene
expression
profiles
that
you
could
activated
and
that
results
in
an
electrochemical
signal
that
you
can
then
measure
and
it's
very
different
from
the
cell
in
its
default
state
or
you
know,
as
the
cell
ages.
We
know
that
there
are
different
markers
that
get
up
regulated
and
down
regulated
and
again
these
move
into
some
of
these
electrochemical
assays
that
you
can
do,
and
it
tells
you
something
about
the
state
of
that
cell.
B
For
example,
you
could
have
a
wild
type
sign,
it
doesn't
have
any
of
the
mutations
that
we
might
knock
out
a
gene
or,
we
might
add,
in
a
gene
and
it
you
know
it's
basically
what
you
would
find
in
nature,
and
so
you
know
we
think
about
a
lot
of
biological
experiments
in
terms
of
that
whale
type
versus
treatment
or
versus
mutant
or
whatever.
This
suggests,
of
course,
that
you
can
have
these
states
that
deviate
from
the
normal
from
the
wild
type
State,
and
maybe
the
wild
type
state
is
a
little
bit
overstated.
B
He
asked
a
question
in
this
talk
Midway
through
the
Q
a
he
talked
about
the
less
Universal
common
ancestor
of
both
Archie
archibacteria
and
bacteria.
Mitochondria
in
the
eukaryote
cells,
so
all
these
different,
like
you,
know,
kingdoms
their
last
Universal
common
ancestor.
None
unlikely
coincidence.
Everything
to
sow
is
structure
not
info.
Yes,
yes,
yes,
systems,
architecture,
level,
info
and
individual
bits.
That
was
a
little
bit.
B
So
I
think
this
is
good
and
if
you
want
to
know
more
about
the
Markov
blanket
Carl
friston
has
worked
on
this
for
a
while,
and
you
know
this
is
a
part
of
the
free
energy
principle.
So
this
is,
you
know,
there's
citations
on
this
out
in
the
world,
and
there
are
some
other
notes
in
here
so
yeah.
This
is
a
little
bit
beyond
what
we
were
talking
about,
but
this
is
so
take
a
look
at
this
blog
post.
B
If
you
want
to
know
more
and
I'd
be
interested
to
hear
your
thoughts,
the
other
feature
I
have
for
you
today,
origin
of
Life,
digest
and
then
well
who's
a
another
blogger.
He
has
this
origin
of
Life
digest.
So
it's
a
digest
for
every
week
it
puts
out
a
digestive
pre-prints
and
papers
in
the
field
of
the
origin
of
life.
So
this
is
this.
B
Last
week,
January
30th
we
have
some
papers
in
the
artificial
life
field
from
astrobiology,
biochemistry
biology,
microbiology,
paleontology
planetary,
science,
statistical
physics,
and
so
it
covers
a
lot
of
different
fields.
You
know
there
are
all
these
different
research
topics
that
they
explore
and
they
kind
of
come
from
different
angles.
So
it's
nice
to
have
all
these
papers
in
one
place,
so
I
I'm
not
going
to
get
into
any
of
these
papers,
but
I
wanted
people
to
know
that
this
existed.
B
B
You
know
unicellular
and
then
we're
in
a
lot
of
Innovations
and
as
soon
as
you
had
an
increase
in
oxygen
on
the
planet,
we
got
a
lot
of
different
types
of
life
and
different
types
of
complexity
that
it
was
innovated
by
life.
So
the
first
article
is
this
quanta
article,
a
billion
years
before
sex
ancient
cells
were
equipped
for
it,
and
this
is
based
on
molecular
work.
That's
zeroing
in
on
the
origins
of
sexual
reproduction.
The
protein
tools
for
cell
mergers
seem
to
have
long
predated
sex.
So
what
were
they
doing?
B
And
so
this
goes
through
some
of
this
in
researchers
quest
for
the
origins
of
the
ability
of
gametes
to
feed
sexually.
They
were
looking
at
related
genes
in
primitive,
archaea,
bacteria
and
viruses.
So
that's
what
this
paper
this
article
was
about.
I
talked
about
sort
of
the
genes
that
you
find
common
genes
and
organisms
that
we
don't
think
of
as
being
sexual,
but
they're.
Nevertheless,
there
and
they
manage
this
reproductive
ability.
B
B
They
then
merge
pairs
of
these
gametes
to
create
new
individuals
with
a
full,
unique
genome.
Sexual
reproduction
is
neural,
ubiquitous
among
the
eukaryotes
organisms,
from
kelp
to
koalas
that
have
a
membrane,
bounducleus
and
organelles.
So
the
cell
person
membrane,
the
nucleus,
is
membrane
bound
within
the
nucleus
and
we
have
organelles
inside
such
as
mitochondria
or
chlorophyll
that
perform
specific
functions.
B
This
is
chlorophyll,
of
course,
being
in
Plants,
mitochondria
being
in
animals
and
plants.
We
animals
merge
as
sperm
in
an
egg
Mushroom
sprout
From
The,
Underground,
Collision
of
fungal
threads
pollen,
scent
tubes,
racing
through
floral
tissues
that
join
ovules,
creating
fruit
and
seeds,
and
they
meet
yeah.
Fusing
cells
like
this
runs
contrary
to
normal
cellular
life.
B
So
these
cells,
the
origin
of
eukaryotic
cells,
was
that
they
fused
together
from
multiple
organelles
that
were
floating
around
in
the
early
environment,
and
so
this
is
not
typical
of
a
lot
of
cell
early
cells
or
in
and
just
sort
of
the
origins
of
cells.
Yeah
fusing
cells
like
this
run
runs
contrary
to
the
normal
cellular
site
life
cycle
cells
divide
into
throughout
mitosis
to
produce
asexually,
but
otherwise
they
mostly
guard
against
major
disruptions
of
their
integrity,
which
could
snuff
out
their
lineage.
So
you
see
this
after
the
end
of
the
boring
billion.
A
B
A
typical
thing
that
cells
do
so
what
is
hazardous,
Arrangement
caught
in
an
evolutionism
in
the
subject
of
intense
study,
it's
generally
presume
that
sexual
recombination
helps
to
keep
a
species
and
diverse,
but
researchers
have
also
been
piecing
together,
molecular
clues
about
how
it
evolved.
So
in
the
asexual
organisms,
you
typically
get
sort
of.
You
lack
the
ability
to
generate
genetic
diversity.
You
get
mutations,
but
that's
often
not
enough
to
keep
up.
You
need
to
have
recombination.
B
You
need
to
have
mechanisms,
for
you
know,
keeping
diversity
flowing
in
in
a
population
or
in
a
GM
itself
from
nature
gametes
gamete
sounds
like
Kegs
and
sperm
fuse
to
create
new
genetic
individuals
or
zygotes,
but
outside
of
sex.
These
kinds
of
fusion
events
are
far
from
routine
for
eukaryotes,
so
you
don't
really
see
that
everywhere.
You
just
see
it
in
the
germline
and
sexual
reproduction,
and
so
this
is
a
very
similar
thing
to
what
happened
in
basal
eukaryotes,
where
you
had
this
merger
of
organelles
into
a
cell.
B
You
also
have
this
merger
of
fuse
Fusion
of
eggs
and
sperm
to
create
a
new
type
of
cell,
at
least
from
the
standpoint
of
what
came
before
it
in
that
interaction.
So
there's
this
paper
on
the
bioarchive
that
suggests
some
cells
of
the
power
diffuser
membranes,
far
back
in
their
evolutionary
history.
The
molecular
Machinery
that
makes
this
part
of
the
sexual
reproduction
act
possible
may
have
existed
more
than
2
billion
years
ago
in
a
simple
prokaryotic
cells
of
archaea,
perhaps
as
much
as
a
billion
years
before
eukaryotes
and
sex
evolved.
B
Life's
history,
so
this
is
where
you
get
these
Fusion
proteins,
a
family
of
them
and
they
evolved
fairly
early
on
so
throughout
the
family
tree
eukaryotes.
The
same
protein
enables
the
cell's
membranes
and
nuclear
membranes
of
gametes
diffuse,
and
this
is
hap2
because
it
shared
so
universally
among
eukaryotes.
The
common
ancestor
must
have
had
the
same
mechanism.
B
So
here
using
bioinformatics,
we
identified
archael
genes,
encoding
candidates
of
physics
since
a
family
of
fusogenes
mediating,
somatic
and
comedic
Fusion,
multiple
eukaryotic
lineages,
so
they
were
able
to
assay
some
of
the
aspects
of
this
of
these
candidate
genes
and
they
were
able
to
look
at
like
the
proteins
that
It
produced
and
they
were
able
to
look
at
how
these
things
fuse
mammalian
cells
and
there's
a
conserved
Fusion
Loop.
So
you
see
this
throughout
eukaryotes
and
genome
content.
Analysis
reveals
are
feel
fusaxons
genes
or
within
the
integrated
mobile
elements.
B
So
these
are
our
integrated
mobile
elements,
transposons
that
allow
that
are
have
other
functional
significance.
Finally,
evolutionary
analysis
places
these
archaeophysia
Visa
genes
as
the
founders
of
the
physics
and
super
family.
Based
on
these
findings,
we
propose
a
new
hypothesis
on
the
origins
of
eukaryotic,
sex
or
kale
physics
and
originally
used
by
selfish
elements
for
horizontal
transmission
was
report
pre-purposed
to
enable
gamete
Fusion.
B
So
there
are
these
horizontal
Gene
transfer
elements
and
archaeobacteria
and
bacteria
that
are
their
selfish
elements
of
the
sort
of
transfer
within
the
cell
and
or
they're
able
to
transfer
to
different
cells
and
fuse
together,
and
this
is
something
that
then
was
repurposed
for
sex,
and
so
now,
if
we
go
back
to
our
article,
you
see
here's
an
example
of
the
essays
that
they
did
here.
Those
are
the
have
two
proteins
like
the
one
modeled
here.
B
It
enables
membranes
of
gametes
diffuse
the
structure
of
haptil
links
it
to
a
superfamil
or
fusexin
proteins
with
similar
functions
and
diverse
organisms,
and
this
includes
viruses.
So
in
the
in
the
paper,
they
were
talking
about
the
function
of
this
Anarchy
bacteria,
but
it
also
functions
very
a
very
different
way,
but
with
a
similar
Gene
family
that
produces
a
similar
protein
in
eukaryotes
and
even
in
viruses,
it
has
a
function
of
its
own.
B
B
Let
me
talk
more
about
this.
They
talk
about
functions
before
Fusion,
so,
but
in
some
way
it's
also
a
bit
predictable,
because
basically,
the
whole
machinery
for
meiosis
has
an
archaea
origin,
which
is
interesting.
We
don't
really
think
of
meiosis
as
having
an
archaeal
origin,
but
you
know
they
you
using
molecular
data,
that's
what
they
found,
and
so
they
have
this
figure
how
two
cells
become
one.
B
This
shows
the
fusion
of
gametes
and
it
shows
that
there's
a
half
set
of
chromosomes
in
each
gamete
and
they
combine
to
make
a
full
set
of
chromosomes
for
what
will
become
a
somatic
cell,
and
so
you
have
this.
Basically,
this
is
how
Fusion
proteins
bring
cells
together,
so
you're,
basically
taking
two
cells
and
you're,
transforming
it
into
a
new
type
of
cell,
and
so
this
sort
of
thing
happened
at
the
origin
of
eukaryotes.
It
happens
in
sex.
All
the.
B
Everywhere
in
the
body-
and
it
has
an
archaeal
virgin-
and
it
even
has
some
parallels
with
what
happens
in
viruses,
so
how
Fusion
proteins
bring
cells
together,
gametes
can
be
combined
because
of
a
special
class
of
fusion
proteins
use
exons
on
their
membranes.
So
these
are
the
few
sexins
on
the
cell
membranes.
They
bring
the
cells
together.
They
open
up
this
patch
here
that
allows
things
to
transfer
as
a
bridge
between
two
cells
and
then
as
the
membranes
open
up.
Then
they
share
what's
inside
the
membranes
and
that's
how
you
get
your
cell
Fusion.
B
B
So
this
is
the
graphical
abstract,
where
you
have
a
protist,
a
billion
year
old
protist.
This
is
a
new
genus
by
psyllium,
which
demonstrates
cell
cell
adhesion.
So
we
have
the
naked
stage
here,
there's
differentiation
in
so
long
elongation
in
this
colony
of
cells
in
the
naked
States.
They
seem
to
be
undifferentiated,
with
respect
to
size
and
shape,
but
in
this
cell
elongation
stage
they
get
you
get
these
elongated
cells
and
then
you
get
differential
adhesion
and
cell
migration
within
this
mass
and
you
end
up
with
a
stage.
B
So
this
is
the
life
cycle
morphogenesis
of
this
billionaire
microfosso,
which
they
found
that
actually
may
be
key
to
the
origins
of
embryonic
development.
So
we've
talked
about
the
Del
shanto
findings
from
China,
where
they
found
some
very
old
embryos
that
they
think
are
embryos.
They
don't
actually
know
that,
but
they
they
look
like
modern
embryos,
and
it's
like
the
first
example
in
the
fossil
record
of
this.
This
is
actually
pushing
us
back
a
bit
more.
B
So
the
highway
to
this
paper
or
the
the
multicellular
microfossil
by
Salem
brassiere
possesses
two
distinct
cell
types.
So
you
can
see
the
cell
types
in
here,
differentiating
at
this
stage,
these
elongated
cells
and
these
more
rounded
cells,
and
then
these
push
out
to
the
outside
and
they
become
what
looks
like
this
ring
around.
This
almost
looks
like
the
Inner
Cell
Mass,
with
a
an
outer
shell,
so
this
is
like
the
CIS
stage.
So
this
is
why
you
know
people
say
this
looks
like
an
embryo.
B
Now
it
may
not
be
an
embryo,
but
it's
doing
these
same
sorts
of
things
that
an
embryo
does
3D
preservation
and
phosphate
preserve
different
life
cycle
stages.
Differential
adhesion
may
have
contributed
to
cell
segregation
during
morphogenesis,
and
this
billion
year
old,
freshwater
Proto
shows
evidence
of
holozoan
affinity.
So
it's
a
it's
effying,
the
things
that
you
find
today,
it's
related
to
things
you
find
today.
B
So
this
is
from
the
Northwest
Scottish
Highlands.
This
is
a
micro.
These
are
microfossils
from
a
non-marine
setting
about
a
billion
years
ago,
possibly
nodules
from
the
debug
formation
left
to
right
and
preserve
microorganisms
of
cellular
level
Fidelity.
So
this
is
a
very
nice
preservation
at
the
soiler
level,
so
they're
able
to
reconstruct
developmental
stages
of
this
organism
by
cell
and
brassiere,
the
mature
form
of
bicellium
consists
of
a
solid
spherical,
bulb
tightly
packed
cells
and
stereoblast.
B
It's
what
we
call
stereoblasts
and
when
we
look
under
the
microscope,
I
in
icdometric
cells,
enclosed
in
a
monolayer
of
elongated
sausage
shaped
cells.
However,
two
populations
of
naked
stereoblash
has
mixed
cell
shapes.
So
this
is
where
we're
looking
at
two
populations
of
these
cell
types
or
this,
these
cell
formations
show
mixed
cell
shapes,
which
we
infer
to
indicate
incipient
development
of
elongated
cells
that
were
migrating
to
the
periphery
of
the
cell
Mass.
B
Unicellular
zones
are
known
to
data
for
multicellular
stages
of
within
complex
life
cycles,
so
the
occurrence
of
such
simple
levels
of
transient
multicellular
we
already
seen
here,
is
consistent
with
the
holozoan
affinity,
regardless
of
precise
phylogenetic
placement,
which
means
where
is
it
in
the
Tree
of
Life?
These
fossils
demonstrate
simple
cell
differentiation
and
morphogenetic
processes
similar
to
those
CNN
metazones
today,
so
this
is
similar
to
animals.
B
This
is
their
site
that
they've
gotten
these
fossils
from.
Then
these
are
some
of
the
pictures
of
this
by
selling
brassiere
in
its
mature
form.
So
it
has
this
shape
that
we
see
at
the
sort
of
the
third
stage
of
that
graphical
abstract.
This
is
the
mature
stage
of
bison,
and
then
we
see
variations
on
this.
B
These
are
the
naked
stereoblasts,
so
this
is
sort
of
like
the
first
two
stages
of
that
graphical
abstract.
You
see
how
you
have
like
the
parts
of
the
stereoblasts
that
are
single,
undifferentiated,
looking
cells,
they
all
look
basically
the
same.
There
isn't
that
much
organization
around
the
edges,
and
then
you
move
into
these
other
micrograph,
C
and
D,
where
you
get
these
elongated
cells
and
they're
starting
to
migrate
around
this
mass
and
then
in
E
and
F.
You
start
to
get
these
zones
where
you
get
a
more
defined
Edge,
more
defined
edges.
B
C
B
B
C
B
Sort
of
the
origin
of
Marines
and
arthropods,
so
for
more
than
a
century,
the
origin
and
evolution
of
the
arthropod
head
and
brain
have
alluded,
a
unifying
rationale,
reconciling
Divergent,
morphologies
and
phylogenetic
relationships.
That's
a
long
way
of
saying
they
really
don't
know
how
to
figure
out
which
parts
of
the
like.
How
did
the
brain
evolve
from
a
common
precursor?
How
did
it
evolve
from
a
very
simple
form?
Sometimes
it's
easy
to
reconstruct
these
relationships,
and
sometimes
it's
not
in
this
case.
It's
not
here.
B
Clarification
is
provided
by
the
fossilized
nervous
system
of
the
lower
Cambrian
Global
Podium
cardio
dictum
catanulum,
which
reveals
an
unsegmented
head
and
brain
comprising
three
cephal
domains,
distinct
from
their
metameric
ventral
nervous
system
serving
its
appendicular
trunk.
Each
domain
aligns
with
one
of
the
three
components
of
the
foregut
and
with
a
pair
of
head
appendages.
B
Morphological
correspondences
with
the
stem
group
group
arthropods,
which
are
the
origin
of
arthropods
and
alignments
of
homologous
gene
expression
patterns
with
those
of
extant
paranthropods,
demonstrate
that
cephalic
domains
of
this
organism
predate
the
evolution
of
your
Ur
through
a
pothead
which
is
uarthropod.
Is
the
group
larger
group
of
arthropods
a
correspondent
neuromirs,
defining
brains
of
living,
some
other
like
the
mandibioids
and
chair
with
sureties,
so
I?
Don't
I'm
not
familiar
with
the
former,
but
the
other
is:
is
organisms
of
mandibles,
so
they
have
this.
B
B
B
B
You
have
this
sort
of
it's
like
a
you
know
something
that
allows
the
organism
to
keep
its
structure.
You
have
a
gut
here
that
runs
down
the
middle
of
the
body,
and
then
you
have
the
brain
of
the
at
the
anterior
end
of
the
organism
with
different
aspects
of
it.
So
this
G
or
J
shows
this
organismal
configuration
in
F,
G,
H
and
I.
B
It
all
shows
the
sort
of
the
anterior
end
or
the
of
the
brain,
so
you
have
some
decent
preservation
for
some
of
these
structures
in
the
in
the
earlier
through
pot
brain,
and
you
can
see
it
in
this
diagram
or
correspondence
of
these
different
structures
and
so
moving
on
through
the
paper
we
have
the
segmented
nervous
system
and
a
segmental
brain.
So
all
this
is
just
showing
evidence.
B
B
You
have
gene
expression
or
gene
expression
spatially
restricted.
Here.
You
see
these
lines.
They
show
these
different
genes
that
are
expressed
in
different
parts
of
the
organism.
So
you
know,
invertebrates
are
a
very
good
example.
Insects
are
very
good
examples
of
restricted
gene
expression,
so
the
gene
expression
and
different
segments
of
the
organism.
So
you
see,
for
example,
the
Hox
genes
are
expressed
at
different
parts
of
the
organism's
length,
and
then
you
get
these
other
genes
that
are
expressed
more
in
the
brain.
So
pack,
six
is
expressed
throughout
six.
B
Three
is
expressed
sort
of
in
the
anterior
end,
and
you
see
this
across
different
taxa,
and
you
see
this
how
this
can
resolve
in
the
formation
of
an
anterior
and
that's,
not
segmented,
versus
a
body
that
is
segmented
or
at
least
a
non-brain
body
or
non-brain
part
of
the
nervous
system.
That
is
part
of
a
segmented
phenotype,
and
so
this
just
shows
some
of
these
details
of
the
nervous
system
and
showing
that
you
have
this
anterior
end,
where
there's
a
non-segmented
brain
that
there
are
different
ways
that
this
can
be
done.
B
B
D
D
C
C
D
And
please
these
are
the
number
of
references.
So
what
we
found
is,
if
you
take
a
given
amino
acid,
this
one,
for
example,
eight
papers
said
it
was
first
here:
okay,
existing
none,
glycine
15.,
so
we're
looking
at
a
consensus
and
basically
what
we
concluded
is
that
there
is
no
consensus.
If
you
look
at
the
number
of
references.
C
D
They
would
stick
together
and
form
a
two-dimensional
fluid
right,
but
they're
melting
temperature,
saturates,
variously
very
close
to
the
boiling
point
of
water
versus
n,
is
the
length
of
the
chain.
Okay.
So
this
indicates
there's
some
phenomenon
which
might
limit
how
long
a
an
amplifier
can
be
in
a
membrane.
D
D
Versus
the
log
of
The
Logical
length,
a
stereoisomer
is
a
molecule,
the
same
number
of
carbon
atoms
or
the
same
number
of
atoms,
but
in
a
different
configuration.
And
if
you
look
at
this
table,
this
is
n.
The
number
of
structural
isomers,
the
number
of
stereoisomers
and
so
serial
isomers
would
would
include
those
that
are
experience,
symmetric
and
look
how
this
cable
goes
up.
D
D
D
C
D
An
answer:
metric
access
is
an
interesting
concept.
If
you
have
a
mixture
of
left
and
right-handed
amino
acids
mere
symmetric,
it
turns
out
that
the
portion
in
different
meteorites
is
not
50,
50.,
okay
and
they
have
an
X
set,
and
these
are
the
estimated
access.
There
are
various
ways
of
calculating
the
excess,
but
basically
closer.
You
are
to
0.5
the
closer
okay.
D
D
D
D
Usually
they
go
inside
of
meteorite.
They
take
some
fresh
stuff
from
the
inside,
but
some
meteorites
are
very
porous,
so
living
things
could
fall
in
on
Earth,
so
that
may
account
for
some
of
the
observations
of
Mike
organisms
and
meteorites.
You,
you
put
a
put
a
motile
organism
in
a
meteorite.
You
don't
control
for
humidity
and
after
100
years,
pull
off.
You
can
crawling
all
the
way
inside.
D
D
Okay,
so
this
is
where
the
hypothesis
came
from
that
curved
membranes
had
to
be
formed
by
some
classes,
even
thicker.
Okay,
all
right.
The
initial
ones
might
fits
in
okay,
all
right
Okay.
So
then
I
checked
the
carb
and
carbon
carbon
distance
literature
and
for
chains
the
chains
are
zigzags.
So
these
these
these
numbers
are
a
little
bit
weird.
But
a
couple
of
estimates,
two
different
estimates,
they're
reasonably
close.
D
D
Layers
or
they
have
u-shaped
bipolar
molecules,
I,
don't
I,
haven't
found
any
literature
that
settled
this
question
for
archaea,
which
have
bipolar
current
existing
archaea
all
have
bipolar
membranes,
which
means
that
they
consist
of
a
long
molecule
which
has
a
charge
of
both
ends.
But
it's
not
known
whether
two
charges
of
the
two
ends
are
on
opposite
sides
of
the
membrane
or
on
an
individual
side
membrane,
in
which
case
the
molecule
would
bend
and
it
would
be
u-shaped
okay.
So
that's
one
of
the
unsettled
questions.
D
D
D
D
C
D
D
D
D
So
the
idea
is
that
the
top
left
is
that
these
peptides,
since
they
keep
growing,
they
can
be
thicker
than
the
current
membrane,
okay,
yeah
and
as
a
result,
there
is
a
selection
for
membrane
molecules
that
are
longer
because
there's
a
force
pulling
up
and
therefore
longer
ones
can
be
attached
to
the
to
the
pep
membrane
peptides.
And
so
you
get
a
positive
feedback
mechanism
here,
where
they
the
longer
the
peptide,
goes,
the
thicker
the
membrane
becomes,
and
the
thicker
the
membrane
becomes,
the
it
can
accommodate
thicker
peptides,
okay.
D
So
that's
the
positive
feedback
is
postulated
here
you
can
here,
you
can
see
this
one's
sticking
out.
I
would
bring.
In
now,
we
calculated
in
a
separate
paper
that
the
number
of
day
night
cycles
available
for
these
polymerizations
of
peptides
are
about
10
to
the
11th,
and
that's
assuming
that
from
the
Earth,
when
the
moon
formed
the
Earth
spun
up,
because
it
was
hit
sideways
by
a
rogue
planet
called
Thea
and
the
original
spin
of
the
earth
may
have
been
as
short
as
two
hours
to
go
a
full
cycle
day
night.
D
So
the
Earth
may
have
started
out
with
the
daylight
cycle
of
two
hours
and
it's
as
the
Moon
drifted
further
and
further.
It
came
down
to
24
hour
24
hours
and
there's
lots
of
evidence
of
his
paleontological
evidence
that
the
day
was
shorter,
I
think
I.
Think
for
paleontology
evidence
goes
down
to
18
hours,
okay,
but
the
calculation
from
the
formation
of
the
earth
is
somewhere
between
two
and
six
hours
for
this
for
a
day
night
cycling.
D
So
the
assumption
is
each
day
night
cycle
you
might
have
been
able
to
alternate
the
temperature,
the
UV
exposure
Etc
and
this
might
have
led
to
the
formation
of
growth
peptides.
So
the
energy
source
so
to
speak,
is
the
spin
of
the
Earth
okay.
So
let's
go
the
idea.
The
peptides
keep
growing
is
plausible.
D
D
B
D
B
C
C
D
C
D
C
C
D
C
C
D
D
Okay,
there's
a
huge
literature
speculating
on
why
this
might
have
occurred,
but
here
I
think
we'd
have
to
get
to
molecular
Dynamics
to
figure
figure
this
out
Okay.
So
so
Bradley
you
got
now
the
thing
that
we've
added
in
the
paper
we're
working
on
is
that
if
we
go
see
if
we
can
find
a
curve,
if
we
go
back
to
the
first
term,.
D
This
one
yeah
this
one
yeah,
okay,
suppose,
instead
of
temperature,
the
vertical
structure,
the
vertical
the
the
y-axis
is
permeability:
okay,
okay!
So
over
time
the
permeability
decreases
it
starts
with
one
at
the
bottom
goes
to
zero
at
the
top
all
right.
Okay,
but
as
you
do
this
you're
getting
more
and
more
peptides
and
you
might
end
up
with
some
peptides
that
cause
the
membrane
to
leak
through
the
peptides,
but
through
structural
alteration
of
the
membrane
due
to
the
peptides
right.
D
Okay,
so
there's
a
possibility
of
a
mechanism
set
again
to
get
what
are
called
ion
channels
which
are
special
proteins
that
allows
things
selectively
to
come
through
the
past.
The
membrane,
okay,
but
we're
not.
Our
simulation
does
not
include
that.
We
just
go
to
the
point
where
it's
close,
where
the
membrane
itself
closes
up
entirely
right.
B
D
B
B
D
D
D
B
A
D
A
D
Okay,
okay
and
this
could
be
made
more
complicated
if
we
actually
knew
the
distribution
of
molecules,
you
could
also
say:
okay,
how
do
you
make
a
membrane
with
a
bunch
of
a
bunch
of
amplifiers
that
are
of
different
lengths
and
does
that
cause
selection
for
those
that
are
the
same
length
as
the
current
thickness
of
the
membrane?
Okay,
they
yeah
the
spinal
could
be
made
more
much
more
complicated.
D
To
life
oh
yeah,
those
are
the
channels
and
obviously,
if
the
membrane's
closing
down
they're
becoming
impermeable
at
some
point,
you've
got
to
have
domains
you
have
to.
You
have
to
have
iron
shafts,
okay,
yeah
the
iron
channels
were
also
essential
for
creating
a
charge
difference
between
the
inside
and
outside
yeah,
okay,
so
make
sure
which-
and
then
you
have
the
whole
problem
of
how
do
you
have?
How
do
you
get
camera
Cami
osmosis
going?
Okay,
okay,
so
yeah!
This
does
not
stop
everything.
Okay,.