More Bang For Your Buck: Quorum-Sensing Capabilities Improve
the Efficacy of Suicidal Altruism
Anya Elaine Johnson1,5 , Eli Strauss2,5 , Rodney Pickett1,5 , Christoph Adami3,5 , Ian Dworkin2,5 , & Heather J. Goldsby4,5
1

arXiv:1406.0416v1 [] 2 Jun 2014

Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
2
Zoology, Michigan State University, East Lansing, MI, USA
3
Microbiology and Molecular Genetics / Physics and Astronomy, Michigan State University, East Lansing, MI, USA
4
Biology, University of Washington, Seattle, WA, USA, 5 BEACON Center for the Study of Evolution in Action
anyaejo@msu.edu
Abstract
Within the context of evolution, an altruistic act that benefits the receiving individual at the expense of the acting individual is a puzzling phenomenon. An extreme form of altruism can be found in colicinogenic E. coli. These suicidal
altruists explode, releasing colicins that kill unrelated individuals, which are not colicin resistant. By committing suicide, the altruist makes it more likely that its kin will have
less competition. The benefits of this strategy rely on the
number of competitors and kin nearby. If the organism explodes at an inopportune time, the suicidal act may not harm
any competitors. Communication could enable organisms to
act altruistically when environmental conditions suggest that
that strategy would be most beneficial. Quorum sensing is a
form of communication in which bacteria produce a protein
and gauge the amount of that protein around them. Quorum
sensing is one means by which bacteria sense the biotic factors around them and determine when to produce products,
such as antibiotics, that influence competition. Suicidal altruists could use quorum sensing to determine when exploding
is most beneficial, but it is challenging to study the selective forces at work in microbes. To address these challenges,
we use digital evolution (a form of experimental evolution
that uses self-replicating computer programs as organisms)
to investigate the effects of enabling altruistic organisms to
communicate via quorum sensing. We found that quorumsensing altruists killed a greater number of competitors per
explosion, winning competitions against non-communicative
altruists. These findings indicate that quorum sensing could
increase the beneficial effect of altruism and the suite of conditions under which it will evolve.

et al., 2000; Khare et al., 2009). However, inclusive fitness theory and multi-level selection theory both describe
conditions under which altruism–even suicidal altruism–can
evolve (Traulsen, 2006; Hamilton, 1963). According to inclusive fitness theory, altruism is favored by selection when
an organism’s altruistic action directly benefits its kin, who
are likely to share the same altruistic gene(s) (Khare et al.,
2009). However, an open question is how do altruistic organisms know when to behave altruistically? For example,
how do altruistic organisms know when their kin surround
them and would benefit from their actions?

Introduction

Under certain conditions, colicinogenic E. coli commit
suicide to kill nearby competitors and thus free resources for
their kin (Chao and Levin, 1981). Specifically, these E. coli
stochastically produce a toxin until they explode. Once an
E. coli explodes, the toxin is released, killing all surrounding organisms that are not resistant. Because the exploding organism’s kin are usually resistant to the toxin, they
survive the explosion and benefit from the decrease in surrounding competition for resources (Chao and Levin, 1981).
However, the prevailing environmental conditions, such as
the number of competitors and kin surrounding the altruistic organism, determine the benefits of the suicidal action.
Specifically, when an organism explodes, it is possible that
either competitors are not present (and thus the explosion
does not affect the competition for resources) or kin are not
nearby (and thus are unable to take advantage of the accessible resources). In both of these cases, the altruistic organism
committed suicide without providing any benefit to its kin.

An organism behaves altruistically when it performs an action that lowers its own fitness in order to increase the fitness
of another organism (West et al., 2007). Altruistic organisms
ranging from bacteria to humans are found frequently in nature (Chao and Levin, 1981; Bowles, 2006). We might expect that any altruistic genes should be selected against because the altruistic organism will have a lower fitness than
a non-altruistic organism benefitting from an altruistic act
(a “cheater”). A particularly challenging form of altruism
to explain is suicidal altruism, where the altruistic organism dies to increase the fitness of other organisms (Velicer

An organism that can communicate with constituents to
gain information about its environment could potentially
improve decisions regarding when to perform altruistic actions. Quorum sensing is a type of communication found in
many species of bacteria (Diggle et al., 2007; Davies, 1998),
whereby the bacteria can assess how many constituent organisms surround them. Quorum sensing involves an organism releasing a small amount of a signaling protein and
gauging the amount of that same protein that has been released by other bacteria around it (Bassler, 2002). A number of organisms combine quorum sensing with antibiotic

production to strategically kill competitors (Chandler et al.,
2012). In these situations, producing an antibiotic is an expensive action and requires the cooperation of other constituents to produce enough to harm competitors. If too few
organisms are producing the antibiotic, the action is expensive and does not accrue any benefits. However, if the organisms achieve quorum and ensure that enough constituents
are also producing the antibiotic, then it is likely they can
harm or kill the competitor. Thus, quorum sensing is used
to enable organisms to determine whether the environmental
conditions are favorable to perform expensive and possibly
altruistic acts.
In this study, we explore whether altruistic organisms
that do not have access to information about their environment would benefit from quorum-sensing capabilities.
While enabling colicinogenic E. coli to use quorum sensing would determine the benefit of such a strategy, the challenges involved with performing that experiment are numerous. They include the difficulty associated with evolving
complex traits, such as quorum sensing and suicidal altruism, and the generation time of E. coli. To address these
experimental challenges, we use Avida, a digital evolution
system (Ofria and Wilke, 2004). Within Avida, computer
programs (“digital organisms”) self-replicate with a chance
of mutation and compete for space within their environment.
Avida has been used to study evolutionary topics such as
division of labor (Goldsby et al., 2012) and the evolution
of biological complexity (Lenski et al., 2003). Additionally, Avida has been used to study suicidal altruism (Goings
et al., 2004) and quorum sensing (Beckmann and McKinley, 2009) in isolation, making it the ideal platform for evolution experiments that combine these two complex traits.
In this study, we use digital organisms that explode based
on stochastic factors and quorum-sensing information when
available. When an organism explodes, it kills competitors,
and thus behaves similarly to colicinogenic E. coli. We study
whether such organisms will evolve to use quorum-sensing
capabilities and whether these capabilities provide a competitive advantage against altruists who cannot communicate.

Related Work
Suicidal altruism and communication have been studied extensively in both organic and digital systems (Berngruber
et al., 2013; Bordereau et al., 1997; Chao and Levin, 1981;
Crespi, 2001; Davies, 1998; Diggle et al., 2007; Foster et al.,
2006; Goings et al., 2004; Goldsby et al., 2012; Hamilton,
1964, 1963; Kerr et al., 2004). Using both types of systems, researchers have found strong evidence that the benefits from higher inclusive fitness enables the success of
suicidal-altruism strategies (Chao and Levin, 1981; Goings
et al., 2004; Berngruber et al., 2013; Bordereau et al., 1997;
Crespi, 2001; Foster et al., 2006). Furthermore, studies in
both organic and digital systems have found that cooperation
mediated by quorum sensing can be a successful strategy in a

number of environments (Diggle et al., 2007; Davies, 1998).
Here we discuss relevant studies surrounding the evolution
of suicidal altruism and quorum sensing within organic and
digital systems.
Within nature, suicidal altruistic acts are generally performed to increase the success of the altruistic organism’s
kin (Chao and Levin, 1981; Berngruber et al., 2013). For example, colicinogenic E. coli stochastically kill competitors
in an explosion of toxins, thus the surviving colony members have less competition for resources (Chao and Levin,
1981). Additionally, in some species of E. coli, when a bacterium of E. coli carrying the suicide gene Lit is infected by
a lytic phage, it may kill itself to prevent the spread of the
pathogen to the rest of the colony (Berngruber et al., 2013).
There are a number of organisms that use quorum sensing to regulate altruistic behavior (Miller and Bassler, 2001;
Dworkin and Kaiser, 1985; Chandler et al., 2012). For example, Myxococus xanthus is a bacterium that uses quorum
sensing to detect cell density. When confronted with starvation, at high cell density this bacterium forms fruiting bodies (Miller and Bassler, 2001). For the fruiting body to be
successful, the majority of organisms must lyse to form the
stalk and only a small fraction of the organisms reproduce as
spores (Dworkin and Kaiser, 1985). Additionally, a number
of species of bacteria use quorum sensing to kill competitors with antibiotics (Chandler et al., 2012). Competitors
only die once the amount of antibiotics in the environment
reaches a certain concentration (Chandler et al., 2012). If the
antibiotic that is produced is too diluted, competitors may
become resistant to it. As such, the bacteria are most likely
to succeed in harming the competitors if a group of them
simultaneously produce the antibiotic. Quorum sensing is
used to establish that such a group is present and available
to produce the antibiotic.
Altruism and quorum sensing have previously been studied in digital systems as well. Goings et al. studied suicidal altruism modeled on the colicinogenic E. coli’s behavior (Goings et al., 2004). They discovered that suicidal
altruism could evolve in the Avida digital evolution platform, even though the organisms could not communicate
with each other. Suicidal altruism was most likely to evolve
in a structured environment, where an organism had a high
probability of being surrounded by its kin, rather than in a
well-mixed environment, where an organism’s kin were distributed randomly. In a subsequent study investigating environmental factors that influence the evolution of altruism,
we found that large population sizes and high mutation rates
increased the evolution of suicidal altruism (Johnson et al.,
2014a). Additionally, Beckmann et al. studied the evolution of quorum sensing in Avida (Beckmann and McKinley, 2009). They determined that digital organisms could
evolve to use quorum sensing to suppress replication when
population density reached a specific threshold. Furthermore, they established that organisms using quorum sens-

Figure 1: An Avida population. Each square represents a
cell potentially occupied by an organism. Each circle represents an organism, where color denotes the genotype. Each
organism consists of a genome, a virtual CPU for executing
instructions, and memory space for storing values.
ing could utilize it within populations up to 400 times larger
than those in which the organisms had evolved. While these
studies explored the conditions under which suicidal altruism and quorum sensing arise independently, here we investigate whether the conjunction of these traits is beneficial.

Methods
For these experiments, we used the Avida digital evolution
platform (Ofria and Wilke, 2004). Within Avida, digital organisms live on a toroidal grid where they compete for space
(Figure 1). A digital organism consists of a computer program (its genome) and virtual hardware upon which the program is executed. Included in this virtual hardware are three
registers that the organism can use to store and manipulate
numbers. Each genome consists of a series of instructions
that enable the organism to input numbers from and output
numbers to the environment (IO), manipulate numbers, and
self-replicate. Each organism occupies a specific cell in the
grid. When an organism replicates, it creates two daughter
organisms that inherit a potentially-mutated version of their
parent’s genome. One daughter organism replaces the parent
and the other daughter organism is placed into a surrounding
cell, potentially killing the current occupant.

Altruism and Quorum Sensing Instructions
For this study, in addition to the standard set of Avida instructions (Ofria and Wilke, 2004), we developed several
instructions that enable organisms to evolve to use quorum
sensing and altruistically explode (potentially killing competitors).
If evolved, the quorum-sense instruction enables the digital organism to sense how many related organisms surround
it. For these experiments, we consider two organisms to be
related if their genomes differ by three or fewer instructions. An organism’s neighborhood consists of any organisms in the 5x5 grid surrounding the organism. When an
organism executes the quorum-sense instruction, the following computation occurs: (1) The proportion of neighboring

Figure 2: The results of an organism exploding in a beneficial and non-beneficial environment. Each 5x5 grid represents a subsection of the Avida world, where each square
represents an organism. Within the grid, the center square
represents the focal, exploding organism. The numbers
specify the number of genomic differences from the focal
organism. Squares containing an X represent organisms that
are killed by the explosion because they are unrelated to the
focal organism (i.e., they have four or more genomic differences). (a) This grid depicts the effects of a beneficial explosion. Many of the organisms surrounding the exploding
organism are unrelated and are killed by the toxin released
by the exploding organism. (b) This grid depicts the effects
of a non-beneficial explosion. All the organisms in the exploding organism’s neighborhood are related, thus the focal
organism’s explosion did not kill any competitors.
cells filled by related organisms is computed. (2) If this proportion is less than a quorum-threshold value specified by
the organism, then one of the organism’s registers is set to
TRUE. While the organisms can use this capability in isolation, we envision that they will use it to assess whether environmental conditions are suitable for altruistic explosions.
The smart-explode instruction, if evolved, enables an organism to use quorum information in determining whether
or not to explode. In many examples within nature, quorum sensing is used as a trigger for actions (e.g., a sufficient
number of bacteria are present to produce an antibiotic).
However, for this study, we envision that quorum sensing
will be used to inhibit explosions under unsuitable conditions (Figure 2), though the population must still evolve to
use the instruction in this way. Specifically, the presence
of a quorum indicates that the focal organism is surrounded
by related organisms and an explosion would not free up
resources. As such, when the smart-explode instruction is
executed, the following computation occurs (Figure 3): If
the register specified by the organism (by default this is the
same register that quorum-sense sets) is set to TRUE, the
organism explodes with 5% probability. Otherwise, the instruction has no effect. The probability of exploding was
5% to be congruent with previous digital evolution studies
(Goings et al., 2004).
Several factors influence whether organisms successfully
make use of these instructions to sense their environment

ings et al., 2004; Goldsby et al., 2012).

Figure 3: An organism executing the quorum-sense and
smart-explode instructions. A portion of the genome and
internal state of the exploding organism (exploding center square) are shown. When the organism executes the
quorum-sense instruction, a register in the organism’s memory is set to TRUE because the percentage of related individuals is less than 92% (the organism’s quorum threshold).
The organism then executes the smart-explode instruction,
which checks the same register in memory and, because it is
TRUE, explodes with a 5% chance.
and react accordingly. First, organisms are not able to directly sense the presence of competitors, but must infer this
information from the proportion of related organisms around
them. When an organism detects that only a few kin are
in its neighborhood, it cannot be certain that the rest of its
neighborhood contains competitors. Some or all of the nonkin cells could be empty spaces instead, rendering an explosion useless. This instruction mimics the quorum-sensing
capabilities of natural organisms, who only sense the presence of conspecifics. Additionally, the register used by the
smart-explode instruction can also be used by other instructions. An organism could have a suicidal-altruism strategy
that does not use quorum sensing. The quorum-sense and
smart-explode instructions provide the most accurate information when executed sequentially. This ensures that the
organism is using the information gathered by the quorumsense instruction and that that information reflects the current status of the surrounding environment. For example,
if an organism executes the quorum-sense instruction early
within its lifetime, but waits to execute the smart-explode
instruction until substantially later, then the quorum-sense
information may no longer be accurate. This could result in
an explosion that would not otherwise have occurred.

Experimental Parameters
For each treatment, we configured an environment in which
organisms evolved on a 60x60 toroidal grid for 30,000 updates. An update is the time unit used by Avida. One update
allows an organism to execute 30 instructions on average
and 10 updates is approximately one generation. All experiments had a mutation rate of 0.02 genomic. These are
standard parameters employed by Avida experiments (Go-

We performed two main types of experiments: singlelineage evolution and competition assays. The singlelineage evolution experiments started with one ancestor with
a genome of length 100. The ancestor contained only instructions necessary for replication and no-operation (nop)
instructions. In the quorum-sensing-altruists treatment of
this experiment, the standard set of instructions and the
quorum-sense and smart-explode instructions were available
via mutation. In the non-quorum-sensing-altruists treatment
(a control), the standard set of instructions and the explode
instruction were available via mutation. When an organism
executes the explode instruction, it explodes with a 5% probability.
To assess whether the quorum-sense and smart-explode
instructions were undergoing positive selection, we compared how frequently they were executed to the frequency
with which the nop-Y instruction was executed. Nop-Y is a
no-operation instruction that does not affect the operation of
the organism and therefore was used as a control.
We performed several competition assays with distinct
starting proportions of the different lineages. For these
experiments, the organisms were not able to mutate the
quorum-sense, smart-explode or explode instructions in or
out of their genome, which enabled us to assess the competitive capability of these lineages. The first set of competition assays started with a colony of non-quorum-sensing
altruists and a colony of quorum-sensing altruists in equal
proportions. The second set of experiments began with 95%
non-quorum-sensing altruists and 5% quorum-sensing altruists. This permutation of initial conditions enabled us to
test whether quorum-sensing altruists could invade a colony
of non-quorum-sensing altruists. Finally, we determined
if quorum-sensing altruists could repel non-quorum-sensing
altruists by configuring the initial population to contain 5%
non-quorum-sensing altruists and 95% quorum-sensing altruists.
Configuration files and data for all experiments are available at https://github.com/anyaejohnson/Communication14.

Results and Discussion
In this study, we first explore whether organisms will evolve
to use quorum sensing to inform their decisions about when
to behave altruistically. Next, we examine the strategies organisms evolved to determine how many related organisms
constitute a quorum. Finally, we determine if organisms that
can use quorum sensing to inform their decisions regarding
if and when to altruistically commit suicide have a competitive advantage over organisms that do not have access to
quorum-sensing information.

100

2

1

0
smart-explode

quorum-sense nop-Y (a control)
Instruction

Figure 4: The average instruction executions for smartexplode, quorum-sense and a neutral instruction per organism in the population. The smart-explode instruction
was used 1.95 more per organism than the neutral instruction (nop-Y) in the last 100 updates. The quorum-sense instruction was used 0.99 more per organism than the neutral instruction (nop-Y) in the last 100 updates (95% CI for
difference between smart-explode and neutral instruction is
1.92 to 1.97 executions per organism, Wilcox rank sum test
p < 0.0001; 95% CI difference quorum-sense instruction
executions and neutral instruction is 0.96 to 1.00 executions
per organism, Wilcox rank sum test p < 0.0001, error bars
represent 95% confidence intervals). Both quorum sensing
and suicidal altruism are under positive selection in this environment.

Can organisms evolve to use quorum information
to decide when to commit suicide?
For our first experiment, we investigated whether quorumsensing and suicidal-altruism behaviors would evolve, rise
in frequency, and ultimately stabilize in a population. To assess whether these capabilities were beneficial, we evolved
a colony of organisms from an ancestor organism that could
only reproduce, but quorum-sensing and suicidal-altruism
instructions were available via mutation.
Figure 4 shows the execution rates per organism of the
quorum-sensing, suicidal-altruism, and neutral instructions.
In agreement with previous work (Goings et al., 2004), suicidal altruism was used an average of 1.95 time more frequently than a neutral instruction per organism by the final
time point (median number suicidal-altruism instruction executions per organism = 2.00, neutral instruction executions
per organism = 0.04, 95% confidence interval of difference
is 1.92 to 1.97 instruction executions, Wilcox rank sum test
p < 0.0001). Organisms also evolved to use the quorumsensing instruction an average of 0.99 more frequently than
the neutral instruction per organism by the final time point
(median number of quorum-sensing executions per organism = 1.03, neutral instruction executions = 0.04, 95% confidence interval of difference is 0.96 to 1.00 more instruction
executions per organism, Wilcox rank sum test p < 0.0001).
These results demonstrate that both suicidal-altruism and
quorum-sensing capabilities were selected for.

Mean Quorum Threshold
(Percent of related organisms in neighborhood)

Mean Number of Instruction Executions
Per Organism

3

75

50

25

0
0

10000

20000

Evolutionary time (in updates)

30000

Figure 5: Mean quorum threshold evolved by quorumsensing altruists. The quorum-sensing altruists evolved to
use a quorum threshold of approximately 92% related organisms in their neighborhood. Within the context of this study,
it is likely that an organism using quorum sensing would explode if its neighborhood contained 8% or more unrelated
organisms or empty spaces.

How many related organisms constitute a quorum?
Quorum-sensing altruists are required to set a threshold to
determine how many organisms constitute a quorum. This
quorum threshold also implicitly sets the number of potential competitors or empty spaces that must be present for
an explosion to be considered beneficial. For our quorumsensing-altruist treatment, we explored how many organisms
constituted a quorum by recording the value of the quorum
threshold each time an organism exploded (once the exploding mechanism evolved into the population).
Figure 5 shows that the organisms evolved a median quorum threshold that corresponded to approximately 92% of
the organisms around the focal organism being related (95%
confidence interval is 91.29% to 92.63%). This percentage
indicates that organisms using the quorum-sensing instruction with suicidal altruism would only probabilistically explode if one or more unrelated organisms or empty spaces
were around the organism. This mechanism prevents the
organism from exploding when the organism is completely
surrounded by related organisms.

Is quorum information beneficial?
Ideally, an organism should use quorum sensing to assess
whether its environmental conditions are such that an explosion would benefit its kin. However, the act of quorum
sensing also has a cost: the organism must spend time gathering information about its environment. Within our study,
this cost is experienced by the organism as the CPU cycles it
devotes to gathering and using quorum-sensing information.
To assess whether the costs of using quorum-sensing information outweigh the benefits of additional environmental information, we compared the behavior of quorum-sensing altruists to non-quorum-sensing altruists.

0.75

0.50

0.25

8

treatment

Mean Number of Organisms Killed
per Explosion per 100 Updates

1.00

Mean Number of Organisms Killed per Explosion per 10

Mean Number of Explosions
Per Organism

1.25

6

4

Quorum-Sensing Altruists
Non-Quorum-Sensing Altruists

6

4

2

2

quorum-sensing altruists non-quorum-sensing altruists

Treatment

0
0

10000

20000

Evolutionary time (in updates)

30000

Figure 6: Mean number
0 of explosions per organism
Figure 7: A comparison of the effects of quorum-sensing
for the quorum-sensing altruists and the non-quorum0
10000
20000
30000
and non-quorum-sensing-altruist
explosions. Shown is
sensing altruists. At the final time point Evolutionary
in the treattime (in updates)
the
average
number
of
organisms
killed
per explosion every
ments, the quorum-sensing altruists explode 0.35 less per
100
updates.
The
quorum-sensing-altruist
treatment is more
organism on average than the non-quorum-sensing altruists
efficient, killing 0.702 more organisms on average than the
(median quorum-sensing-altruist explosions = 0.35, median
non-quorum-sensing altruists (mean number of organisms
non-quorum-sensing-altruist explosions = 0.75, 95% CI for
killed per explosion by quorum-sensing altruists = 2.004,
difference is 0.28 to 0.45 explosions per organism, Wilcox
mean number of organisms killed per explosion by nonrank sum test p < 0.0001).
quorum-sensing altruists = 1.231, 95% CI of difference is
0.567 to 0.847, Wilcox rank sum test p < 0.0001). We also
To investigate how these costs and benefits influence
calculated the average number of organisms killed per exaltruistic behavior, we determined the number of exploplosion normalized by population size. When normalized by
sions per organism that occurred within the quorum-sensingpopulation size, the average number of organisms killed per
altruist and non-quorum-sensing-altruist treatments. Figexplosion by quorum-sensing altruists is 0.000197 more orure 6 shows the number of explosions per organism in each
ganisms than the non-quorum-sensing altruists (mean numtreatment. The quorum-sensing altruists explode an averber of organisms killed per explosion normalized by popage of 0.35 times less frequently per organism than the nonulation size for quorum-sensing altruists = 0.00058, nonquorum-sensing altruists (median quorum-sensing altruist
quorum-sensing altruists = 0.00037, 95% CI of difference
explosions = 0.35, median non-quorum-sensing-altruist exis 0.000161 to 0.000243, Wilcox rank sum test p < 0.0001).
plosions = 0.75, 95% confidence interval of difference is
0.28 to 0.45 fewer explosions per organism, Wilcox rank
of 0.000197 more organisms per explosion than the nonsum test p < 0.0001). These results may indicate that quoquorum-sensing altruists (mean number of organisms killed
rum sensing allows the quorum-sensing altruists to avoid exper explosion normalized by population size for quorumploding in a detrimental environment or they may indicate
sensing altruists = 0.00058, for non-quorum-sensing altruthat quorum sensing is not adaptive.
ists = 0.00037, 95% confidence interval of difference is
To assess whether quorum-sensing altruists are deriv0.000161 to 0.000243, Wilcox rank sum test p < 0.0001).
ing more benefit from each explosion than non-quorumTherefore, even when larger population size is accounted
sensing altruists, we compared the mean number of organfor, quorum-sensing altruists kill more competing organisms
isms killed per explosion in each treatment. Figure 7 shows
than the non-quorum-sensing altruists on average. These rethat the mean number of organisms killed per explosion by
sults suggest that the benefits of quorum sensing may outthe quorum-sensing altruists and non-quorum-sensing altruweigh the costs.
ists is 2.004 and 1.231, respectively. Thus, on average, the
quorum-sensing altruists kill 0.702 more organisms per exDoes quorum sensing provide a competitive
plosion than by the non-quorum-sensing altruists (95% conadvantage for suicidal altruists?
fidence interval of difference is 0.567 to 0.847 organisms
killed per explosion, Wilcox rank sum test p < 0.0001). The
We have demonstrated that quorum-sensing altruists kill
population size is also larger in the quorum-sensing-altruists
more organisms per explosion than non-quorum-sensing altreatment, which could be contributing to the greater numtruists. However, these results do not yet demonstrate that
ber of organisms killed (median population size in the final
quorum sensing provides a competitive advantage. While
time point of quorum-sensing-altruists treatment = 3423.5,
quorum-sensing altruists may kill more organisms per exnon-quorum-sensing-altruists treatment = 3371.5). When
plosion, the costs of quorum sensing may yet outweigh
the mean number of organisms killed is normalized by popthis benefit. Therefore, we performed competition assays
ulation size, the quorum-sensing altruists kill an average
in which quorum-sensing altruists compete directly against

non-quorum-sensing altruists. Our competition assays used
two lineages of organisms. One lineage was required to
use the quorum-sensing-altruist strategy and the other lineage was required to use the non-quorum-sensing-altruist
strategy. To focus on the competitive capabilities of these
two strategies, we hand wrote both ancestor organisms and
prevented the quorum-sensing and suicidal-altruism instructions from being accessible via mutation.
For our first competition assay, we filled half the population with copies of the quorum-sensing-altruist ancestor and
the other half with copies of the non-quorum-sensing-altruist
ancestor. Figure 8a shows the mean percent of organisms of
each lineage over time. Out of 30 replicates, the quorumsensing lineage fixed 30 times. Therefore, despite the additional costs of quorum sensing, the information gathered
through quorum sensing provides a competitive advantage.
There are many situations in nature where a colony of organisms already exists. Therefore, we examined whether
or not the quorum-sensing altruists could invade an existing
population of non-quorum-sensing altruists. Specifically,
we performed competition assays in which the lineage of
quorum-sensing altruists started at only 5% of the population and the non-quorum-sensing altruists started at 95% of
the population. As seen in Figure 8b, the quorum-sensing
altruists were not able to invade a pre-established population of the non-quorum-sensing altruists and went extinct in
30/30 replicates.
Finally, we examined the reverse situation and explored
whether quorum-sensing altruists could repel invading nonquorum-sensing altruists. Figure 8c depicts our results. In
this case, the quorum-sensing altruists are able to repel the
non-quorum-sensing altruists in all 30 replicates. These experiments demonstrate that quorum-sensing altruists have
a competitive advantage over non-quorum-sensing altruists
when they start at an equal or greater proportion of the population.

the quorum-sensing mechanisms affected their use (Johnson
et al., 2014b). In general, quorum-sensing accuracy was
correlated with an advantage in competitions against nonquorum-sensing altruists.
Suicidal altruism is a critical aspect of survival strategies in many natural systems, despite its high cost (Chao
and Levin, 1981; Shorter and Rueppell, 2011). Communication has often been implicated as the element that might
aid the evolution of altruism (West et al., 2007; Turner and
Chao, 1999; Strassmann et al., 2011). This study offers support that: (1) Quorum-sensing capabilities can be used to
make altruistic acts more effective. (2) Communicating altruists can outcompete altruists that are not able to communicate. In this study we purposefully kept the cost of quorumsensing low to facilitate evolution. Future work could explore how increasing this cost affects the competitive advantage of quorum-sensing altruists.

Conclusions

Berngruber, T. W., Lion, S., and Gandon, S. (2013). Evolution of
suicide as a defence strategy against pathogens in a spatially
structured environment. Ecology Letters, 16:446–453.

We explored whether organisms would evolve to use quorum
sensing to inform a suicidal altruism strategy and whether
such a strategy would have a competitive advantage. We
found that when we enable digital organisms to set their own
quorum threshold, they evolved a threshold of 92.1% on average, meaning that organisms evolved to require at least
one competitor or empty space to be in their neighborhood
in order to explode. Additionally, we found that quorum
sensing increased the benefit of suicidal altruism (i.e., the
number of competitors killed per explosion significantly increased). Finally, we found that quorum-sensing altruists
outcompeted non-quorum-sensing altruists when starting at
equal proportions or in the majority. Future work could
explore intermediate values to address whether quorumsensing altruists could invade a pre-existing population. In
further experiments, we also explored how the accuracy of

Acknowledgments
We thank Charles Ofria, David Bryson, and the Devolab for
their insightful contributions to this work. This research
was supported by funding from the BEACON Center for
the Study of Evolution in Action and the NSF. This material
is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI0939454 and No. OCI-1122620. Any opinions, findings,
and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect
the views of the National Science Foundation.

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