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From YouTube: DevoWorm #35: Differentiation waves, Janus-faced logic, mitochondrial networks, energetic dynamics
Description
Talk on Differentiation Waves and Janus-faced logic. Discussion of mitochondrial networks, the energetic dynamics of cells, and Eukaryotic symbiosis. Attendees: Bradly Alicea, Richard Gordon, Susan Crawford-Young, and Morgan Hough
A
A
C
B
B
B
B
A
C
Yeah,
we
did
two
papers
in
that
issue
or
well.
He
did
one.
We
did
a
tutorial
on
Janna's
face
logic,
and
we
also
did
they.
I
did
I
did
the
a
paper
by
this
title,
except
he
didn't
have
the
part
after
the
cop
okay,
okay
yeah,
since
since
this
is
a
memorial
symposium
of
that
elusive
died
a
few
years
ago,
I
decided
to
check
out
his
works
and
it
looks
like
he
did
some
Janice
based
stuff:
okay,
okay,
yeah,
okay,
next
slide,
please.
C
C
Okay,
here
is
a
differentiation
tree
of
the
early
Axolotl
and
this
is
a
Mexicana
and
it
shows
how
various
tissues
that
we've
named
over
time
have
been,
are
organized
and
our
hypotheses
about
expansion
and
contraction
ways,
all
of
which
have
been
at
these
stages,
observed
okay,
so
this
is
up
to
up
to
neural
plate
formation.
Basically,
okay
and
the
the
way
the
tree
is
organized
is
if
let's
see
this
left,
it's
gotten
expect.
C
B
C
And
left
are
the
contraction
waves,
so
they're
they're,
red
okay,
so
we've
got
all
these
tissues
organized
and
this
fashion
these
are
based
on
observed
waves
on
the
embryo.
The
numbers
are
kind
of
weird
they're,
zero
means
contraction,
wave
and
one
means
expansion
wave
in
these
long
digits,
and
the
first
number
is,
is
how
many
steps
it's
gone
through.
Okay,
so
you
don't
put
trailing
zeros
on
numbers
like
these
yeah
okay,
because
it
has
a
specific
meaning
with
a
specific
number
of
digits.
C
Okay,
so
four
has
four
digits.
Five
has
five
digits,
so
it's
gone
to
five
stages
of
differentiation.
If
you
take
the
middle
one,
so
much
five
stages
of
preparation,
differentiation
in
the
cells
that
it
became
so
much
gone
through,
let's
say:
contraction.
Waves,
expansion,
contraction,
expansion,
contraction,
okay,
all
right,
so
so
that's
what
the
differentiation
code
is.
C
C
Okay.
So
that's
an
example.
That's
the
economic
attraction,
so
you
can
see
it's
on
a
two
millimeter.
The
axial
Amber
is
quite
large.
It's
two
millimeters
and
these
things
are.
You
can
actually
spot
these
because
the
the
cells
contract
in
the
contraction
wave
and
you
could,
with
some
practice
you
can
see
you
can
actually
see
because
of
the
cells,
are
pigmented
and
when
there's
a
contraction
they're
closer
together.
C
C
C
C
Now,
if
you
take
that
tree
of
life
and
we
erase
all
of
the
vertical
branches
that
just
show
the
horizontal
branches,
you
get
a
mess
and
if
that's
all,
you
can
see
It'll
be
very
hard
to
analyze.
What's
going
on
so
that's
a
phylogenetic
tree
with
horizontal
Gene
transfers,
but
with
the
tree
removed,
okay,
yeah
yeah,
so
it's
kind
of
weird
and
if
you
could
only
see
that
you
you'd
be
dumbfounded
what's
going
on
okay
next,
please.
C
C
Okay,
the
and
this
is
this
is
the
current.
This
is
basically
the
current
thinking
for
most
people
of
Developmental
biology
and
they
don't.
They
don't
understand
that
it's
it's
illogical
to
both
think
that
all
cells
have
the
same
genes,
but
what
but
different
chains
are
turned
on
in
different
tissues,
but
what
turns
them
on
is
not
just
don't
discuss
that
so
imagine
some
some
people
say
the
environment
doesn't
or
the
environments
of
the
cells
or
the
contact
cells
or
the
induction
of
one
cell
by
another
one.
C
C
Okay,
now
here's
this
is
something
from
stuff.
We
did
badly
and
I
did
together.
I
barely
recognize
this.
If
you
take
C
elegans,
the
every
cell
has
daughter
cells,
except
for
some
sensitial
tissues,
Sensational,
meaning
that
the
cells
fuse
together.
Okay,
so,
except
for
those
each
each
cell
has
a
daughter
cell.
Next,
please.
C
C
C
C
Okay,
so
each
each
cell
gives
rise
to
two
daughter
cells,
but
then
we
bundle
them.
We
can
bundle
them
according
to
cell
type,
so
we
have
the
ones
on
the
left,
bundled
together
and
the
ones
on
the
right
and
those
two
have
diverged.
Some
cells
may
be
left
behind,
they're
represented
by
the
ones
in
the
middle
and
they
might
be
stem
cells.
C
Okay,
next
slide,
please,
okay.
So
this
is
an
ideal
limit.
Idealized
differentiation
tree
real
ones,
may
be
a
little
more
complicated,
but
with
the
depression
codes
attached
and
what
I've
done
is
put
the
Roman
coin
on
each
branch,
which
is
the
Janus
based
object.
In
other
words,
what's
going,
what
seems
to
be
going
on
was
that
at
each
each
time
there's
a
division
of
cells,
it's
done
in
a
group
of
cells
and
that
group
has
been
given
a
different
color.
C
That's
why
the
arrows
are
all
different,
colors
and
what's
going
on,
is
the
the
cells
in
that
whole
group
become
one
type
and
but
they're
organized
by
a
an
expansion
or
contraction
wave,
so
they're
organized
from
above.
So
in
a
way,
the
with
the
Janice
phase
structure
means
that
we're
looking
down
to
the
cells,
the
cells
create
the
waves
which
in
turn,
cause
the
cells
to
change
for
that
type.
C
So
we
have
sort
of
a
local
Global
interaction
between
them
and
to
give
you
an
analogy,
suppose
you're
driving
in
traffic
and
listening
to
the
radio-
and
you
change
your
course
because
of
something
that's
going
on.
Then
you
have
a
global
effect
on
the
individual
and
the
individual
behaviors
change
according
to
Global
Effect,
but
the
Global
Effect
may
be.
C
C
The
cells
are
connected
by
Junctions
and
there's
a
microtubule
mass
in
between,
and
what
we
think
is
going
on
is
that
the
cells
have
are
by
stable
and
we
call
this
structure
the
cell
State
splitter
that
that
they're
bystable
and
what
happens
is
that
either
the
microfilament
ring
contracts
or
the
microtubule
mat
expands
and
as
a
result,
the
cell
becomes
tall
and
narrow
or
it
becomes
flat.
Squat
and
I
can
see
I'm
missing
from
this.
What
goes
on
with
the
nucleus?
C
C
C
C
Okay,
there'll
be
a
couple
of
talks
at
the
end
in
English,
oh
okay.
What
I
want
to
show
here
is
that
Lev
may
have
been
the
first
to
to
probe
this
local
Global
interaction
and
its
effect
on
differentiation.
This
is
an
experiment
he
did
where
he
took
an
embryo
cut
out
a
sector
of
it
and
then
filled
the
sector
with
this
with
a
corresponding
piece
from
another
embryo.
B
C
C
Okay,
you
can
read
this
yeah
yeah,
okay,
so
they're
a
bunch
of
old,
a
bunch
of
questions
that
have
been
made
about
the
whole
thing.
What
triggers
the
difference?
We
don't
know
it
might
be.
Oh
I
can
say
I
put
something
in
there.
It
might
be,
for
example,
but
in
the
place
where
the
wave
starts
two
tissues
have
adhered
changing.
C
C
C
For
some
reason,
but
we
don't
know
okay
and
then
what
is
actually
the
pathway
I
mean,
in
this
case
the
molecular
pathway
from
the
South
State
splitter
to
the
nucleus
itself,
which
I
didn't
illustrate.
This
is
how
how
the
cell
States
filter
changes,
changes
the
presumably
the
epigenetics
of
the
cell
and
leads
makes
itself
change
into
a
different
type
and
then
a
little
more
sophisticated.
Each
cell
has
got
a
history
and
we
don't
know
what
on
Earth,
that
history
is
stored
in
from
its
sequence
of
differentiations.
C
C
Okay
transdifferentiation
is
when
I
sell
type
experimentally,
changes
to
an
entirely
different
cell
type
or
maybe
wound
healing
trans
differentiate.
When
we
don't
know,
if
transition
supports
this
idea
or
contradicts
it,
that's
why
I
said
transferation
could
be
done
by
backing
down
the
differentiation
tree
and
coming
up
a
different
branch
for
the
cell,
or
it
could
be
something
entirely
different.
C
C
C
Okay,
okay,
now
continuing
differentiation,
it
was
a
concept.
I
came
up
with
because
if
you
look
at
things
like
bacteria,
let's
say
cyanobacteria,
they're
capable
of
producing
two
or
three
cell
types,
and
that's
it:
okay,
whereas
eukaryotes
can
produce
literally
thousands
I
mean
the
nematodesian
cells
and
for
mice
the
number
of
cell
types
has
been
estimated
is
between
250
and
about
five
thousand
okay,
again,
not
a
very
strong
figure
there.
C
Okay,
then
we
have
a
fun
one.
This
is
due
to
Donald
Williamson
and
that
metamorphosis,
which
is
well
the
for
instance,
if
a
caterpillar
changes
into
a
butterfly
they're,
quite
different
animals,
and
the
hypothesis
is
that,
at
least
in
some
of
those
cases,
the
transition
may
be
due
to
the
fusion
of
two
different
organisms
rather
than
one
Community
to
the
other.
But
in
this
Fusion
they
their
differentiation
trees
are
fused,
okay,
so.
C
C
Okay,
now
I
was
asked
to
make
some
personal
comments
about
Liv,
so
I,
unfortunately,
I
don't
know
him
well,
I
only
met
him
twice,
but
in
terms
of
stories
from
him,
I
won't
say
who,
but
he
was
very
badly
treated
by
a
California
embryologist
and
then
I
had
some
funny
situations
with
them.
When
I
went
to
Moscow
for
a
meeting
he
organized
a
he
got
me
through
customs.
C
C
C
C
C
I
mean
and
Zev
Donald
ingber,
your
address
I
didn't
agree
with
all
these
people,
but
right
right,
but
the
number
of
people
who
were
working
on
mechanics
in
the
early
70s
was
pretty
small.
Okay
and
my
observations
attending
the
the
soft
matter.
I,
don't
know
if
you're
doing
that,
Bradley
the
soft
matter
conferences,
yeah,
is
now
it's
dominated
by
physicists,
who
don't
understand,
embryology
so
they're
doing
trivial
problems
right.
C
B
C
A
B
Yeah,
this
is
the
one
where
we
did
the
C
elegans
and
and
the
z-square
differentiation
trees.
So
these
are
differentiation
trees
of
of
this
sort
of
deterministic
evolution
or
mosaic
Evolution
that
we
talk
about
with
C
elegans.
You
see
it
in
Sea
squirts,
where
they
don't
like
it.
It
isn't
the
same
as
like
the
type
of
differentiation
tree
that
you
showed
there.
It's
more
like.
We
had
developed
a
procedure
to
look
at
the
cells
as
they
divide
and
then
look
at
the
smaller
and
larger
cells
and.
C
B
C
B
B
A
C
C
A
A
C
B
Yeah,
so
let
me
go
back
to
the
slides
if
I
go
through,
if
you
like
the
usual
Google
yeah,
so
yeah.
This
is.
This
is
a
figure
from
the
paper
that
we
did.
The
one
from
the
biology,
Journal
you'll
see
it
in
the
references
there.
I
can
also
send
it
out
if
people
want
to
read
it
read
through
it.
This
is
where
this
is
C
elegans.
So
this
is
where
you
have
cell
division
and
you
have
these
cells
that
are
deterministic,
so
you
can
trace
them.
B
So
a
b
forms
a
host
of
tissues,
things
like
c
and
e
form
different
sub
tissues,
and
so
there's
that
aspect
of
it
there's
lineage,
but
there's
also
the
size
of
the
cells,
and
so
you
can
build
something
similar
to
like
a
tissue
based
differentiation
tree
from
the
C
elegans.
So
we
did
that,
and
we
did
also
did
this
with
obsidians
or
C
Squirts
and
C.
Squareds
are
interesting
because
they
have
like
multiple
stages
of
life:
history,
yeah.
B
Right
and
what
happens
is
the
order
of
the
nodes
are
swapped
so
like
what
you'll
have
is
you'll
have
the
the
lineage
tree
is,
as
proposed
originally
was
where
it
was
organized
anterior
to
posterior
end
of
the
worm,
so
the
cells
that
ended
up
at
the
head
were
down
at
the
left
side
and
the
cells
that
were
at
the
tail
were
at
the
right
side
yeah.
So
you
can
see
it
starts
here
where
you
have
the
posterior
end
here
and
the
anterior
end
here
and
then
there's
Division
and
migration
and
then
the
tissues
form.
B
A
B
Yeah
so
that
I
remember
doing
that
and
like
the
problem
we
had,
there
was
that
we
didn't
have
a
very
good
estimate
of
cell
volume.
We
worked
from
some
cell
volumes
that
were,
you
know,
collected
through
some
experiments,
but
those
weren't
like
very
good
I
mean
you
know
you
could
get
better
measurements
of
it.
So,
but
then
you
know
that's
that's
how
we
did
that
and
so
yeah
in
a
in
a
c
squirt,
though
it's
interesting,
the
life
history
is
where
you
have
different
stages
of
the
phenotype.
B
So
the
early
phenotype
of
z-score
is
not
this
two-fold
symmetry
but
four-fold
symmetry.
Where
you
have
for
sort
of
founder
sub
lineages,
like
you,
have
a
b
p
one,
you
don't
that's
not
the
normal
Glacier,
but
you
have
two
additional
founder
lineages
and
basically
it's
like
that
four-fold
symmetry
instead
of
two-fold
symmetry
and
then
it
becomes
like
later
on.
It
develops
a
brain
and
then
it
becomes
susile
even
later
on.
B
So
there's
a
lot
of
interesting
stuff,
but
the
early
embryogenesis
is
this
sort
of
you
know
you
can
start
with
this
simple
Symmetry
and
then
it's
you
know
the
the
cells
are.
Dividing
and
you
have
size
information,
so
you
can
sort
it
by
size
and
then
eventually
you
start
to
get
this
tissue
differentiation,
and
so
that's
that's
where
we
were
doing
in
that
paper.
We're
looking
at
that
very
early
stage.
C
B
I
think
the
volume
is
I
think
we
determined
that
it
was
constant,
or
at
least
that's
how
we
modeled
it
I.
Don't
think
they're
huge
differences
in
the
AG
at
that
stage,
so
that
you
know
the
the
egg
eventually
expands
out
a
bit
but
I,
don't
remember
what
the
scaling
Factor
was,
but
in
the
early
embryo
it's
pretty
pretty
consistent
and
the
cells
just
are
like
half
the
size
of
the.
So.
B
C
B
B
C
B
C
B
C
B
C
C
B
A
C
B
A
B
Yeah,
that
might
be
helpful,
so
Susan
actually
mentioned
something.
Last
week
about
mitochondria
and
I
mentioned
that
in
like
in
textbooks,
you
have
this,
it
looks
like
mitochondria
looked
like
a
bean,
so
they're
Bean
shaped,
and
you
see
these
things
that
are
sitting
in
the
cytoplasm
and
then
you
know
they
produce
energy
for
the
cell.
B
But
if
you
actually
look
at
a
three-dimensional
scan
of
the
cell,
you
can
see
that
mitochondria
actually
form
these
reticulative
networks,
so
they're
connected
together
near
these,
like
they
kind
of
look
like
beans
still
but
they're
connected
through
these
strings
of
of
things
and
they're
connected,
and
so
I
I
heard
this
at
a
conference
and
as
interested
to
know
more
and
actually
there's
been
some
interesting
work
on
this.
So
the
first
paper
I'm
going
to
talk
about
here
is
this
paper.
B
Let
me
see
if
I
can
get
it
here.
What
is
the
function
of
the
mitochondrial
Network,
a
theoretical
assessment
of
hypotheses
and
proposal
for
future
research?
So
they
have
there.
They
kind
of
review
this
area
of
what
the
mitochondria,
how
it
sort
of
structured
in
the
cell
and
what
it's
doing
so.
They
state
that
mitochondria
can
change
their
shape
from
discrete
isolated
organelles
to
a
large,
continuous
reticulum.
B
So
a
reticulum
is
like
there's
an
endoplasmic
reticulum
in
the
cell,
but
it
also
has
this
reticulum
of
mitochondria
and
it
can
actually
change
its
shape,
so
it
can
actually
have
these
isolated
organelles.
It
could
at
some
points
in
time
it
can
be
like
that
or
it
can
have
this
large,
continuous
reticulum
structure.
So
this
I
assume
this
means
that
it's
kind
of
you
know
it
can
oscillate
between
these
states
depending
on
the
function
depending
on
what's
going
on
in
the
cell,
but
that
you
know
this
is.
This
is
how
they
Envision
it.
B
At
least
according
to
this
View,
the
cellular
advantages
underlying
these
fused
networks
are
still
uncompletely
understood.
In
this
paper.
We
describe
and
compare
hypotheses
regarding
the
functional
mitochondrial
networks,
these
mathematical
and
physical
tools,
the
both
to
investigate
existing
hypotheses
and
to
generate
new
ones
and
to
suggest
modeling
strategies,
and
then
the
novel
insights
that
they
get
from
the
work.
That's
ongoing
is
that
selective
mitophages
not
required
for
quality
control,
because
selective
Fusion
is
sufficient.
B
I,
don't
know
exactly
what
that
means
and
then
two
increased
connectivity
may
have
non-linear
effects
on
the
diffusion
rate
of
proteins.
So
this
is
connecting
the
different
organelles
together
and
then
three
fuse
networks
connect
to
dampen
biochemical
fluctuations.
So
you
might
want
a
network
to
dampen
biochemical
fluctuations
during
certain
processes
and,
of
course,
the
mitochondria
produce
energy.
So
there
are
a
lot
of
fluctuations
biochemical
wise
related
to
that.
So
you
know
each
cell
has
a
number
of
mitochondria
and
then
they
get
linked
together
in
these
networks
and
they
have
certain
properties.
B
C
B
So
I
guess
they
they
go.
They
talk
about,
like
you
know,
in
cases
where
you
might
need
to
have
this
sort
of
structure
where
they
might
observe
it.
Mitochondrial,
Dynamics
and
structures
have
been
the
object
of
intense
study
in
the
past
two
decades,
they're
fundamental
to
the
function
of
the
cell,
highly
responsive
to
Cellular
state.
So
in
things
like
Parkinson's
diabetes,
cancer
Alzheimer's,
you
can
have
these
networks
form
and
change
their
shape
and
their
connectivity
in
mitochondrial
diseases.
B
You
see
this
as
well,
so
there
are
a
lot
of
ways:
reasons
why
these
networks
form
and
how
they
operate.
Various
effects
of
fused
mitochondrial
states
have
been
observed,
including
an
increase
in
energy
production
protection
against
apoptotic
stresses.
These
are
stresses
that
sort
of
force,
the
cell
to
go
through
program,
death
or
sometimes
through
necrosis
I
guess,
is
why
well
an
increase
in
cell
proliferation.
So
if
the
rate
of
proliferation
increases,
you
need
more
energy
and
the
regulation
of
various
signaling
Pathways,
so
they
don't
know
how
this
like.
Why
this?
B
How
this
is
established,
but
they
can
observe.
So
this
is
where
you
get
the
different
types
of
fusion.
You
get
some
degradation
of
dysfunctional
Parts,
sharing
a
matrix
and
membrane
components,
so
this
is
where
mitochondria
get
fused
together.
So
you
see
this
being
like
structure
here
as
a
single
mitochondrion,
and
then
they
get
fused
together
like
this.
So
these
are
organelles
in
the
cell
membrane
and
they
get
fused
together.
B
C
B
A
B
So
and
then
on
the
right,
you
have
this
these
different
types
of
like
they
have
different
regimes
for
Fusion.
So
this
is
where
you
have
microfusion,
where
you're
very
few
of
them
fusing
together-
and
this
is
just
a
change
in
the
parameter
value.
So
microfusion
is
where
you
have
a
few
things
fusing
together.
Mesofusion
is
where
you
start
to
get
more
fusion
into
these
networks.
Dynamic
hyperfusion
is
where
you
get
like
intense
fusion
and
most
of
the
mitochondrion
are
fused
together
and
then
static,
or
they
they
kind
of
move
around.
B
So
I
guess
you
have
these
like
sub
networks
that
attach
and
detach
in
different
places
and
then
static.
Hyperfusion
is
where
all
the
mitochondria
in
this
network,
but
they
don't
move
around
they're
sort
of
static
there.
So
this
is
just
a
matter
of
you
know
the
different
types
of
fusion
that
they're
proposing
and
again
none
of
this
is
like
really
well
understood.
B
It's
just
kind
of
like
what
this
field
is
is
grappling
with,
and
then
they
have
some
modeling,
so
they
have
like
some
Criterion
for
usefulness
of
so
I
guess
this
is
these
equations
here
are
where
how
useful
mitochondrion
is
to
the
cell?
If
there's
like
a
shared
usefulness
of
the
mind,
I,
don't
know
what
that
means,
but
basically,
if
it's
maybe
more
energetically
necessary
to
fuse,
then
they
fuse
together,
and
so
you
can
have
like
a
pairwise
model
here,
where
you.
B
B
It
might
enable
faster
energy
transmission,
it
might
increase
ATP
synthesis,
so
there
are
a
lot
of
energetic
and
Susan
was
asking
about
some
of
the
energetic
where
we
shoot
something
with
energetic
aspects
of
mitochondria
last
week
and
I
I
I'm
gonna
quickly
show
that
paper
in
a
minute,
but
this
this
goes
through
the
hypotheses
that
people
have
for.
Why
you
have
this
mitochondrial
fusion
and
it's
just
like
a
list
of
things
that,
maybe
are
why
you
have
this.
There's
some
criticisms
as
well.
B
So
these
are
things
that,
like
you
know,
people
have
proposed
these
hypotheses
and
then
of
course
some
of
them
are
more
plausible
than
others
and
of
course
you
have
all
those
cavi.
You
have
a
lot
of
caveats
to
these,
so
this
is
not
like.
We
don't
know
exactly
why
this
happens.
It
could
be
multi-causal.
It
could
do
you
know,
depending
on
when
it's
happening
or
in
what
cells
it's
happening
in.
B
B
B
B
B
This
goes
back
to
where
we
were
talking
about
where
different
mitochondria
are
connected
into
this
network,
based
on
their
quality
or
their
like
level
of
ability
to
produce
energy,
and
then
you
can
use
ordinary
differential
equation
models
to
do
this,
and
then
you,
you
know
that
might
be
a
way
to
model
it.
If
you,
you
know,
there
are
other
ways
to
model
it,
but
this
is.
B
This
allows
us
to
look
at
like
stochastic
influences
and
other
things,
and
then
the
experimental
test
would
be
that
to
look
at
the
assumptions
of
this
example
where
small,
mitochondrial
fragments
are
degraded.
Only
small
mitochondrial
fragments
are
upgraded.
The
existence
of
a
threshold
size
above
which
a
mitochondrial
filament
is
not
degraded
by
microfiji
can
be
measured,
so
you
can
actually
look
at
this
quality
of
a
mitochondria
experimentally
and
then
look
at
how
they're
forming
networks
and
actually
see.
If
that's,
why
that's
happening?
B
B
So
that's
that's
and
then
so
I,
don't
think
they
talk
about
evolution
in
here.
They
just
kind
of
go
through
some
of
these
hypotheses
they
just
they
do
so
actually,
some
interesting
modeling
here.
So
this
is
a
a
diffusion
model
of
the
formation
of
these
networks
using
some
sort
of
I
guess:
a
two-dimensional
fluctuating
lattice,
so
they're,
actually
looking
it's
a
lattice
simulation
where
they're
looking
at
sort
of
like
how
these
things
connect
together.
B
So
you
know
you
could
do
all
sorts
of
modeling
to
look
at
this,
so
this
yeah.
This
is
more
of
the
sort
of
how
we
might
do
the
experimental
or
how
these
things
are
fused
together,
where
you
have
you
know,
Fusion
points
and
you
can
have
a
perturbation
to
break
them
apart
or
to
to
prevent
them
from
fusing
together.
B
So
they
show
a
lot
of
the
biochemistry
involved
in
that,
and
maybe
some
of
the
ways
you
can
experimentally
keep
that
from
happening,
so
I'm
gonna
in
the
interest
of
time,
I'm
gonna
go
to
this
other
paper,
and
this
is
mitochondrial.
Network
complexity
emerges
from
fission
fusion
Dynamics.
So
this
is
one
example
that
was
a
large
broad
review
of
like
what's
going
on
in
that
area.
B
So
it's
very
much
a
network
Science
question,
they're
kind
of
bringing
Tools
in
from
other
types
of
networks
to
ask
this
you're
asking:
how
does
this
network
complexity
or
how
does
this
connectivity
emerge,
and
so
they
propose
its
fission
fusion
Dynamics,
and
you
saw
a
little
bit
of
that
in
those
simulations
and
in
those
diagrams
where
they
propose
different
mechanisms
for
that.
In
this
case,
the
abstract
reads:
mitochondrial
Networks
exhibit
a
variety
of
complex
behaviors,
including
coordinated
cell-wide
oscillations
of
energy
states,
as
well
as
a
phase
transition
or
depolarization
in
response
to
oxidative
stress.
B
So
there
are
all
these
Dynamics
going
on
within
mitochondria
and
mitochondrial
networks.
So
there's
very
much
that
issue
of
like
processing
going
on
and
the
question
is:
is
you
know
what
is
the
thing?
That's
sort
of
shaping
these
Networks
since
functional
and
structural
properties
are
often
inter
interwinded,
I
guess
intertwined?
Maybe
here
we
characterize
the
structure
of
mitochondrial
networks
in
Mouse
embryonic
fibroblasts,
so
this
is
in
Mouse
using
network
tools
and
percolation
Theory.
B
So
this
is
a
percolation
theory
is
a
tool
from
physics
where
they
sort
of
grow
networks
and
they
look
at
you
know.
Percolation
is
this
process
where
liquid
goes
through
a
layer
of
of
of,
like
maybe
white,
rocks
or
mud
and
it
kind
of
goes
through,
and
you
look
at
how
it
moves
through
that
Medium.
So
you
know
they
use
that
as
a
metaphor
for
sort
of
looking
at
how
networks
emerge,
different
nodes
emerge
with
connections
between
them.
So
it's
a
different.
B
You
know
it's
a
way
to
do
this
sort
of
simulating
the
growth
of
Networks,
so
they
actually
look
at
this.
They
have
the
some
quantitative
analysis
of
mitochondrial
clusters.
These
reveal
that
structural
parameters
of
healthy
mitochondria,
laying
between
the
extremes
of
Highly,
fragmented
and
completely
Fusion
networks.
So
you
saw
in
the
example
from
the
other
paper
they
had
where
you
know
you
had
an
example
where
there
were
no
mitochondria
infused.
B
They
were
all
these
little
beans
that
were
isolated
and
then
the
second
example
was
where
there
was
another
example
where
they
were
all
fused
together,
sometimes
dynamically,
sometimes
statically
and
so
they're,
arguing
that
what
you
actually
observe
is
sort
of
an
in-between
state
where
a
lot
of
times,
some
mitochondria
or
independent
in
some
form
these
Networks.
So
we've
confirmed
our
results
by
contrasting
our
empirical
findings
with
the
predictions
of
recently
described.
B
Although
these
results
offer
not
only
an
objective
methodology
to
prioritize
the
complexity
of
this
organelle,
but
also
support
the
idea
that
mitochondrial
networks
behave
as
critical
systems
that
undergo
structural
phase
transitions,
so
phase
transition,
of
course,
is
where
you
get
a
critical
value
where
you
go
from
like
something
that's
disconnected
to
something
that's
connected,
and
so
this
is
a
these
are
mitochondrial
networks.
These
are
images
that
they're
using
from
confocal
microscopy
they're.
Looking
at
these
cells
they
have.
This
is
a
yfp
marker
and
they're
looking
at
how
they
sort
of
form.
B
So
this
is
yeah.
This
is
just
an
example
of
that.
Let
me
show
some
examples
of
things
where
you
know
you're.
Looking
at
like
power,
law
behavior
and
things
like
that,
so
you
can
identify
phase
transition
from
these
power
loss
signatures.
The
exponent
of
the
of
the
power
of
law
is
usually
the
thing
that
tells
you
whether
there's
like
a
phase
transition
going
on
and
again
it's
like
where
you
have
this.
B
You
know
this
independent,
the
state
where
they're,
independent
and
then
there's
a
rapid
transition
towards
this
network
structure
and
if
there's
anything
more
here,
just
kind
of
showing
this
how
you
get
these
different
Power
law
signatures
for
different
types
of
Network
topology.
So
you
get
these
clusters
and
then
these
are
the
power
loss
signatures
for
these
clusters.
B
You
get
one,
you
know
one
one
segments
and
one
three
segments
that
they
call.
So
these
are
different
parts
of
this
structure
and
you
see
different
Power
law
behaviors
for
that.
So
there
are
changes
in
Mass
distributions
upon
fission,
fusion
balance
perturbation.
So
when
they
look
at
the
different
parts
of
these
networks
that
are
fused
together,
they
can
see
changes
in
the
mass
distribution,
which
is
just
a
characterization
of
how
they're
put
together
I
guess.
B
So
this
tells
you
some
things
about
like
that:
emergent
property
or
how
the
whole
is
greater
than
some
of
the
parts,
just
ways
to
measure
that
and
then
they
think
there's
some
other
things.
Here's
some
comparisons
of
some
experimental
results
with
the
model.
So
this
shows
you
know
just
examples
of
this
where
you
have.
B
D
is
semi-connected
in
different
parts
and
then
e
is
more
fully
connected
and
you
can
see
that
the
phase
transition
is
later
for
each
of
these,
so
if
they
move
out
as
they
get
more
connected,
so
that's
that's
what
they're
doing
now
and
then.
Finally,
this
is
the
paper
that
Susan
sent
me.
She
sent
me
a
paper
on.
B
This
was
the
paper
here,
the
energetics
of
genome
complexity.
So
this
is
a
kind
of
she's
talking
about
last
week
about
energy
balances
and
things
like
that-
and
this
is
a
paper
on
looking
at
sort
of
I-
think
molecular
energetics.
Let's
see
this
is
Nick
Lane
and
William
Martin.
This
is
out
of
I.
Think
nature.
It's
a
hypothesis
paper,
so
the
abstract
is
all
complex.
Life
is
composed
of
eukaryotic
or
nucleated
cells,
so
these
are
eukaryotic
cells,
they
have
organelles
inside
and
they
have
of
course,
mitochondria
and
they
have
a.
B
B
The
eukaryotic
cell
arose
from
prokaryotes
just
once
in
four
billion
years,
so
this
is
an
event
where
a
lot
of
different
organelles
came
together
to
form
a
eukaryotic
cell.
The
the
prokaryotic
cell
doesn't
have
a
lot
of
the
same
complexity
as
eukaryotic
cells.
This
was
a
sort
of
a
some
by
a
symbiotic
event
that
occurred.
Prokaryotes
went
on
their
own
way
after
that
Fusion
event
that
formed
eukaryotic
cells.
So
it
isn't
like
a
transition
from
prokaryotes
to
eukaryotes,
it's
the
eukaryotes.
B
They
have
this
evolutionary
event
where
you
had
the
symbiosis
and
then
prokaryotic
cells
never
really
showed
any
greater
from
plexity
that
characterized
what
happened
in
eukaryotic
cells,
and
then
they
asked
the
question:
why
not?
And
then
they
go
to
the
what
they
do.
Is
they
look
at
the
Genome
of
the
prokaryotes?
B
They
look
at
the
genome
size
and
they
say
that
that's
constrained
by
bioenergetics,
the
endosymbiosis
that
gave
rise
to
mitochondria
restructured,
the
distribution
of
DNA
in
relation
to
bioenergetic
membranes,
permitting
a
remarkable
200
000
fold,
which
means
it's
200
000
times
greater
than
expansion
in
the
number
of
genes
expressed.
So
what
happened
in
eukaryotes
is
with
this
with
all
these
organelles
that
came
into
association
with
the
rearrangement
of
the
genome
and
with
this
nuclear
genome,
it's
very
diverse
and
then
a
mitochondrial
genome.
B
On
top
of
that,
you
have
this
large
number
of
genes
that
were
expressed
beyond
the
prokaryotic
genome
and
the
genomes
were
bigger
and
more
flexible.
This
fast
Peep
and
genomic
capacity
was
strictly
dependent
on
mitochondrial
power
and
a
prerequisite
to
eukaryotic
complexity.
So
you
needed
to
have
that
power
source,
basically
in
the
cell,
to
drive
a
lot
of
this
complexity.
B
So
if
you
think
about
a
you
might
boundary
is
being
Powerhouse
of
the
cell,
you
know
having
a
Powerhouse
within
the
cell
is
actually
quite
useful,
as
opposed
to
like
individual
cells
being
in
it
for
themselves
and
getting
their
own
energy
from
the
environment,
and
so
that's
kind
of
like
almost
like
how
embryos
form
from
you
know
just
multicellular
associations.
You
know
so
that
there's
that
sort
of
evolutionary
transition
that
I
don't
know
how
many
you
know,
I,
don't
know
what
kind
of
work
is
other
than
this
is
a
hypothesis
paper.
B
C
C
And
they
and
that's
go
back
at
least
the
beginning.
Our
kale
mats
really
important
by
paleontologists
yeah
a
few
billion
years
old
now
in
terms
of
you
might
want
to.
You
might
want
to
ask
George
nikolowski
to
give
us
a
talk,
oh
yeah,
because
he
and
I
did
two
papers
on
why
it
took
so
long
for
the
confusion
to
occur,
and
we
concluded
that
it
was
due
to
having
enough
variety
of
genes
that
were
transferred
by
a
horizontal
Gene
transfer.
C
B
A
Yeah
I
I
sent
it
to
you
and
it
was
mainly
the
quote
about
the
mitochondria
charge
density
that
I
was
interested
in
it's
on
the
after
table,
one
mitochondrial
genes
key
to
nuclear
genome
expansion
and
it's
mitochondria
a
must
respond
quickly
to
changes
in
membrane
potential
and
the
penalty
for
any
failure
to
do
so
is
serious.
The
electron
and
proton
transfers
of
chemo
rotastic
energy
coupling
generates
a
transmain
potential
of
150
to
200
millivolts
over
the
membrane.
That's
huge!