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Living systems are ubiquitous in the natural world. While they exist at many different scales—from the tiniest bacterial colony to vast human societies—they share some commonalities between them, such as the drive for growth, the need for nutrient consumption and waste, and the capability to spontaneously mutate and evolve. These commonalities create the potential to apply principles across living systems that occupy vastly different scales and complexity. In this presentation, I will consider populations composed of two very different living organisms—budding yeast and humans—and consider examples of how principles derived from the study of each system can shed light on the other. In the case of budding yeast, we will discuss the problematic biological phenomenon of stochastic gene expression and show how it can be reconciled to evolutionary principles by considering it within a framework taken from economic game theory. In the case of human populations, we will consider community resilience in light of two recent advances in microbial ecology: 1) cooperation density leading to higher resilience and 2) critical slowing down preceding sudden systemic collapse. These examples will highlight the potential for learning from cross-disciplinary models of living systems.
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Social dynamics in living systems: from microbe to metropolis
David Healey
27 August 2014
Ph.D. Candidate Department of Biology Visiting student
Living systems exist at many different scales
• Patterns emerge across all living systems!
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
Part 1: Borrowing models from social science to better understand single cells
Yeast Humans
How cells “make decisions”
DNA RNA Proteins
Environment
Phenotype (cell characteristics)
Identical DNA +
Identical environment =
Identical phenotypes
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?
How cells “make decisions”
DNA RNA Proteins
Environment
Phenotype (cell characteristics)
In a given environment, different phenotypes have different [itness!
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.
• 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.
All seeds germinate Some seeds stay dormant Draught!
Hedge bets
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
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
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.
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
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
Is yeast playing a game of chicken?
1. Question: are GAL-‐ON and GAL-‐OFF mutually invasible?
Glucose Galactose
Healey and Gore, in submission
GAL-‐ON and GAL-‐OFF strategies are mutually invasible
Engineered yeast whose GAL genes can be chemically controlled.
GAL-‐ON GAL-‐OFF Wild type (normal yeast)
Mixed them at different fractions of the population, and competed
Healey and Gore, in submission
There is an evolutionarily stable mixed equilibrium of GAL-‐ON and GAL-‐OFFat a “non-‐optimal” ratio
Healey and Gore, in submission
Stable mix is a “non-‐optimal” ratio
Healey and Gore, in submission
Part 2: Borrowing models from microbial population dynamics to better understand human populations
Yeast Humans
How do cooperative interactions within populations affect resilience?
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
Different levels of cooperation in yeast does not affect the size of population, but drastically affects its resilience
“Non-‐cooperator”
“Cooperator” High cooperation
Low cooperation
SALT SHOCK!!
Sanchez and Gore, PLoS Biology (2013)
Recovery
EXTINCTION
Is there a similar effect in localized human populations?
Village L’Est
Surrounding neighborhood
HURRICANE <½ of schools & businesses reopened
1 year later: >90% of schools & businesses reopened
Chamlee-Wright, The Cultural and Political Economy of Recovery (2010)
New Orleans
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”?
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
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
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)
Another microbially-‐informed question about resilience:
Can you observe loss of resilience preceding a sudden social collapse?
Living systems are prone to contain tipping points that lead to sudden collapse
Scheffer et al. Nature (2009)
Environmental deterioration
Pop
ulat
ion
size
Environmental deterioration
Yeast populations experience a fold bifurcation
Dai et al. Science (2012)
A system experiences a loss of resilience before the tipping point
Scheffer et al. Nature (2009)
This phenomenon is known as “critical slowing down”
Could you see this effect in the anger level of a social network before a sudden social upheaval?
1 June 2013 Rate Hike
Photo credit: EFE
Mass Protests
Time
How does the system respond to shocks in this region?
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
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
