This document summarizes research on applying concepts from social science and game theory to better understand living systems across scales, from microbes to human populations. It discusses how phenotypic noise in yeast cells can arise from an evolutionary game of chicken, and how the density of cooperative interactions within microbial and human populations can impact their resilience to environmental shocks. It proposes that analyzing cell phone call data could provide insights into human social connectivity and its relationship to community resilience.

1. Social
dynamics
in
living
systems:
from
microbe
to
metropolis
David
Healey
27
August
2014
Ph.D.
Candidate
Department
of
Biology
Visiting
student

2. Living
systems
exist
at
many
different
scales
• Patterns
emerge
across
all
living
systems!

3. We
are
more
similar
to
fungus
than
you
might
think
Common
attributes
of
populations:
• Consume
nutrients
• Produce
waste
• Transport
nutrients
and
waste
• Expand
and
migrate
• Cooperate
and
compete
• Mutate
and
evolve
Potential
for
learning
from
Photo
credit:
NASA
(upper)
and

4. Part
1:
Borrowing
models
from
social
science
to
better
understand
single
cells
Humans
Yeast

5. How
cells
“make
decisions”
DNA
RNA
Proteins
Environment
Phenotype
(cell
characteristics)
Identical
DNA
+
Identical
environment
=
Identical
phenotypes

6. There
is
a
lot
more
randomness
than
anyone
expected
The
discovery
of
“stochastic
gene
expression”
or
“phenotypic
heterogeneity”
brought
up
two
questions:
1.
How
do
cells
introduce
randomness
into
their
decision-­‐making
process?
2.
Why
do
cells
introduce
randomness
into
their
decision-­‐making
process?

7. How
cells
“make
decisions”
DNA
RNA
Proteins
Environment
Phenotype
(cell
characteristics)
In
a
given
environment,
different
phenotypes
have
different
[itness!

8. What
is
the
evolutionary
advantage
of
phenotypic
noise?
• Previous
answer
in
the
literature
centered
around
“bet-­‐hedging”:
variation
spreads
risk
in
uncertain
and
[luctuating
environments.
Draught!
All
seeds
germinate
Some
seeds
stay
dormant
• Another
possible
answer:
Randomness
could
be
a
social
adaptation.
Q:
What
is
the
most
optimal
thing
for
me
to
do?
A:
It
depends
on
what
everyone
else
is
doing.
Hedge
bets

9. Game
theory
deals
with
what
is
optimal
given
the
actions
of
other
individuals
• Players
receive
payoffs
dependent
on
what
everyone
chooses
• Solution
concept:
Nash
equilibrium.
A
stable
state
where
no
one
has
an
incentive
to
switch
strategies.
• There
is
a
class
of
two-­‐person
games
that
have
mixed
strategy
Nash
equilibria
– Mixed
strategy:
a
probabilistic
mix
between
pure
strategies
Chicken
game
a.k.a
Snowdrift
Games,
Hawk-­‐dove
games,
anticoordination
games
Swerve
Straight
Swerve
3
,
3
1
,
5
Straight
5
,
1
0
,
0
De[ining
characteristic:
the
optimal
thing
to
do
is
the
opposite
of
whatever
your
opponent
chooses
Driver
1
Driver
2

10. Evolutionary
game
theory
replaces
rationality
with
evolution
• Payoffs
are
evolutionary
[itness
(ie
numbers
of
offspring)
• Strategy
is
de[ined
by
your
genes,
and
you
consider
whether
a
population
can
be
invaded.
Swerve
Straight
Swerve
3
,
3
1
,
5
Straight
5
,
1
0
,
0
Driver
1
Driver
2
• Imagine
a
population
of
clone
drivers.
They
all
have
to
use
the
same
strategy)
• “Swerve”
yields
a
payoff
of
3
for
every
member
of
the
population
• “Swerve”
is
not
an
evolutionarily
stable
strategy.
It
can
be
invaded
by
“Straight”
• “Straight”
is
also
not
stable,
since
it
can
be
invaded
by
“Swerve”
• The
only
evolutionarily
stable
thing
to
do
is
to
sometimes
swerve
and
sometimes
go
straight
– Swerve
1/3
of
the
time.
The
population
can’t
be
invaded.
– This
stable
mixed
strategy
gives
everyone
an
average
payoff
of
1.7

11. What
is
important
about
evolutionary
chicken
games:
Evolutionary
“chicken”
games:
1. Both
strategies
win
when
rare
2. Evolution
will
favor
a
population
that
randomizes
(mixed
strategy)
3. This
results
in
a
population
with
both
phenotypes
present
4. The
evolutionarily
stable
state
is
not
necessarily
the
optimum.
Goal:
show
that
phenotypic
noise
can
be
a
social
adaptation.
A
mixed
strategy
in
response
to
an
evolutionary
game
of
chicken.

12. Budding
yeast’s
GAL
gene
network
is
an
example
of
cellular
randomness
• Yeast
prefer
to
consume
the
sugar
glucose,
but
galactose
is
also
acceptable.
• GAL
enzymes
are
costly,
so
they
should
only
be
produced
when
needed
• (Aside:
you
can
measure
the
activation
state
of
the
GAL
network)
DNA
codes
a
[luorescent
protein
driven
by
GAL
regulator
sequence
yeast
Integrate
into
Yeast
genome
yeast
Laser
Fluorescent
if
GAL
is
“ON”
Healey and Gore, in submission

13. Budding
yeast’s
GAL
gene
network
is
an
example
of
cellular
randomness
• Yeast
prefer
to
consume
the
sugar
glucose,
but
galactose
is
also
acceptable.
• GAL
enzymes
are
costly,
so
they
should
only
be
produced
when
needed
Healey and Gore, in submission

14. Is
yeast
playing
a
game
of
chicken?
1. Question:
are
GAL-­‐ON
and
GAL-­‐OFF
mutually
invasible?
Glucose
Galactose
Healey and Gore, in submission

15. GAL-­‐ON
and
GAL-­‐OFF
strategies
are
mutually
invasible
Engineered
yeast
whose
GAL
genes
can
be
chemically
controlled.
Wild
type
GAL-­‐OFF
GAL-­‐ON
(normal
yeast)
Mixed
them
at
different
fractions
of
the
population,
and
competed
Healey and Gore, in submission

16. There
is
an
evolutionarily
stable
mixed
equilibrium
of
GAL-­‐ON
and
GAL-­‐OFFat
a
“non-­‐optimal”
ratio
Healey and Gore, in submission

17. Stable
mix
is
a
“non-­‐optimal”
ratio
Healey and Gore, in submission

18. Part
2:
Borrowing
models
from
microbial
population
dynamics
to
better
understand
human
populations
Humans
Yeast

19. How
do
cooperative
interactions
within
populations
affect
resilience?

20. Yeast:
a
primitive
model
of
cooperation
and
social
capital
sucrose
glucose
• The
majority
of
glucose
that
yeast
consume
is
produced
by
a
different
yeast!
• Similar
cooperative
dynamics
with
bacterial
antibiotic
resistance!
Yurtsev
et
al,
Mol
Sys
Bio
(2013)
Yeast
cell

21. Different
levels
of
cooperation
in
yeast
does
not
affect
the
size
of
population,
but
drastically
affects
its
resilience
“Cooperator”
“Non-­‐cooperator”
High
cooperation
Low
cooperation
SALT
SHOCK!!
Recovery
EXTINCTION
Sanchez and Gore, PLoS Biology (2013)

22. Is
there
a
similar
effect
in
localized
human
populations?
Village
L’Est
HURRICANE
Surrounding
neighborhood
1
year
later:
>90%
of
schools
&
businesses
reopened
<½
of
schools
&
businesses
reopened
Chamlee-Wright, The Cultural and Political Economy of Recovery (2010)
New
Orleans

23. Social
connectivity
and
resilience
to
disaster
The
most
common
group
people
received
help
from
was
local
friends
and
family
(<
1mi)
~750
heat-­‐related
deaths
• Most
were
socially
isolated
Is community
level social
capital eroding?
Has technology caused “the death
of distance”?

24. How
do
we
measure
connectivity
in
human
populations?
Surveys and interviews
• “The
presence
of
an
external
observer,
typically
the
researcher,
may
heighten
people’s
self
consciousness
and
concerns
with
appearing
in
socially
desirable
ways
(Onnela
et
al.
2014)”
From
social
capital
community
benchmark
survey:
• “How
many
of
your
neighbors’
[irst
names
do
you
know?”
From

25. What
if
we
could
measure
neighborhood-­‐level
connectivity
directly?
Streaming
Twitter
API
hits
from
Arlington
Co.
over
24
hr
1) Not enough geotagged tweets to reconstruct a social network from @mentions
2) Twitter is definitely not a subset of the population

26. The
holy
grail
of
human
connectivity:
call
detail
records
(CDRs)
1. Establish
“neighborhoods”
based
on
radii
around
cell
towers
(down
to
<500
meters
in
high
density
areas)
2. Reconstruct
social
networks
based
on
reciprocated
calls
3. Ask
a
bunch
of
questions:
1. Which
neighborhoods
have
the
highest
inter-­‐neighborhood
connectivity?
(ie
network
density
or
average
number
of
connections
per
node)
2. Which
neighborhoods
are
isolated?
3. What
are
the
“natural”
communities
within
a
city?
4. So
many
more!
(The
vision)

27. Another
microbially-­‐informed
question
about
resilience:
Can
you
observe
loss
of
resilience
preceding
a
sudden
social
collapse?

28. Living
systems
are
prone
to
contain
tipping
points
that
lead
to
sudden
collapse
Scheffer et al. Nature (2009)
Environmental deterioration
Population size
Environmental deterioration

29. Yeast
populations
experience
a
fold
bifurcation
Dai et al. Science (2012)

30. A
system
experiences
a
loss
of
resilience
before
the
tipping
point
This phenomenon is known as “critical slowing down”
Scheffer et al. Nature (2009)

31. Could
you
see
this
effect
in
the
anger
level
of
a
social
network
before
a
sudden
social
upheaval?
Mass
Protests
1
June
2013
Rate
Hike
Photo credit: EFE
How
does
the
system
respond
to
shocks
in
this
region?
Time

32. Summary:
very
similar
population
dynamics
can
exist
even
between
microbes
and
people
• Economic
game
theory
can
help
explain
why
yeast
have
evolved
a
high
degree
of
randomness
in
their
environmental
responses
• The
density
of
cooperative
interactions
within
populations
might
underlie
local
variation
in
resilience
• Living
systems
might
exhibit
observable
loss
of
resilience
preceding
a
tipping
point

33. Acknowledgements
• Jeff
Gore,
Alvaro
Sanchez,
Lei
Dai,
and
other
members
of
the
Gore
group
• Sallie
Keller,
Stephanie
Shipp,
Gizem
Korkmaz,
and
members
of
SDAL
• Funding:
National
Science
Foundation,
National
Institutes
of
Health,
Virginia
Bioinformatics
Institute