Evolving Quorum Sensing in Digital Organisms Benjamin E. Beckmann and Phillip K. McKinley Department of Computer Science and Engineering 3115 Engineering Building Michigan State University East Lansing, Michigan 48824 {beckma24,mckinley}@cse.msu.edu ABSTRACT 1. INTRODUCTION For centuries it was thought that bacteria live asocial lives. The human body is made up of 1013 human cells. Yet, However, recent discoveries show many species of bacteria this number is an order of magnitude smaller than the num- communicate in order to perform tasks previously thought ber a bacterial cells living within the gastrointestinal tract to be limited to multicellular organisms. Central to this ca- of a single adult [4]. Although it was previously assumed pability is quorum sensing, whereby organisms detect cell that bacteria and other microorganisms rarely interact [24], density and use this information to trigger group behav- in 1979 Nealson and Hastings found evidence that bacterial iors. Quorum sensing is used by bacteria in the formation communities of Vibrio fischeri and Vibrio harveyi were able of biofilms, secretion of digestive enzymes and, in the case to perform a coordinated behavioral change, namely emit- of pathogenic bacteria, release of toxins or other virulence ting light, when the cell density rose above a certain thresh- factors. Indeed, methods to disrupt quorum sensing are cur- old [20]. This type of density-based behavioral change is rently being investigated as possible treatments for numer- called quorum sensing [27]. In quorum sensing (QS), con- ous diseases, including cystic fibrosis, epidemic cholera, and tinual secretion and detection of chemicals provide a way methicillin-resistant Staphylococcus aureus. In this paper we for bacteria to assess local cell density. Reaching a suffi- demonstrate the evolution of a quorum sensing behavior in cient density can trigger expression of genes that produce populations of digital organisms. Specifically, we show that behaviors more likely to succeed under such conditions. digital organisms are capable of evolving a strategy to collec- The initial discovery of Nealson and Hastings spawned a tively suppress self-replication, when the population density new branch of research to discover the nature of such inter- reaches a specific, evolved threshold. We present the evolved actions, whether they occur in other microorganisms, and genome of an organism exhibiting this behavior and analyze the consequences of these behaviors. QS has since been ob- the collective operation of this \algorithm." Finally, through served in many species of bacteria, which use it for a variety a set of experiments we demonstrate that the behavior scales of purposes, including secretion of digestive enzymes in the to populations up to 400 times larger than those in which gastrointestinal tract [6], bioluminescence and phototrophy the behavior evolved. in marine bacteria [3, 20], and, in the case of pathogenic bacteria such as Salmonella and Staphylococcus, release of toxins or other virulence factors [7, 9, 18]. QS is also known Categories and Subject Descriptors to be closely related to more complex behaviors, such as aggregation into biofilms [12] and even fruiting bodies [10]. I.2.8 [Computing Methodologies]: Artificial Intelligence| For example, when confronted with starvation due to nu- Problem Solving, Control Methods, and Search trient depletion, Myxococcus xanthus bacteria cooperate to form a stalk, enabling some cells to be carried as spores to General Terms new locations where conditions might be better. Improved understanding of QS has numerous scientific Experimentation benefits [7]. Foremost, diseases caused by quorum-sensing bacteria might be treated with medications that inhibit this Keywords behavior (i.e., quorum quenching) [18], an approach that may have milder side effects than some antibiotics. For ex- Artificial life, digital evolution, quorum sensing, multi-agent ample, Davies et al. [8] showed that QS is essential to the system, cooperative behavior, self-organization. development of biofilms in Pseudomonas aeruginosa, the pri- mary pathogen observed in the lungs of people with cystic fibrosis. In addition, quorum quenching has been proposed as a possible treatment for methicillin-resistant Staphylococ- Permission to make digital or hard copies of all or part of this work for cus aureus [23], some strains of which are resistant to most personal or classroom use is granted without fee provided that copies are traditional antibiotics [16]. Moreover, a deeper understand- not made or distributed for profit or commercial advantage and that copies ing of these interactions and their evolution may provide bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific insight into the evolution of multicellularity itself. permission and/or a fee. GECCO’09, July 8–12, 2009, Montréal Québec, Canada. Copyright 2009 ACM 978-1-60558-325-9/09/07 ...$5.00. In addition to furthering biological studies, knowledge of 2.2. Avida Overview how relatively simple organisms cooperate to perform com- Avida is a well established artificial life platform used in plex tasks may also be beneficial to the development of dis- evolutionary biology [1, 14, 15, 22] and more recently in dis- tributed computational systems that need to tolerate dy- tributed systems research [17]. In Avida, individuals, or namic conditions and survive component failures as well digital organisms, compete for space within a fixed-size two- as cyber-attacks. For example, collective behaviors among dimensional collection of cells. Each cell can contain at most agents in an artificial immune system are essential to de- one organism, which comprises a circular list of instructions tecting and responding to potential threats, while nodes in (its genome) and a virtual CPU that executes those instruc- sensor networks need to implement complex distributed op- tions, as shown at the top of Figure 1. Instructions perform erations such as multicasting, gathering sensed data, and simple arithmetic operations (addition, bit-shift, increment, maintaining a network topology. Many traditional algo- etc.), control execution flow, aid in self-replication, and pro- rithms for solving these problems are brittle when deployed vide a means for the organism to interact with other organ- in dynamic environments, and several promising algorithms isms and the environment. The execution of an instruction proposed recently are inspired by biology [2]. Leveraging costs both virtual CPU cycles and energy. Different instruc- the evolutionary process to produce well adapted systems is tions can be assigned different CPU cycle and energy costs. a logical next step. An organism executes instructions on its virtual CPU, which In this paper we demonstrate the evolution of QS be- contains three general purpose registers (AX, BX, CX), two havior in populations of self-replicating digital organisms. general purpose stacks, and special purpose heads that point Specifically, we show that digital organisms in the Avida to locations within an organism's genome. Similar to a tra- system [22] are capable of evolving a strategy to collec- ditional program counter and stack pointer, heads are used tively suppress self-replication when the population density to control the flow of execution. reaches an evolved threshold. We describe the operation of an evolved genome exhibiting this behavior and analyze the collective operation of a population of such organisms. We also show that the behavior scales to populations up to 400 times larger than those in which the behavior evolved. This study represents a first step in using artificial life, specifically digital organisms, to investigate the evolution and operation of QS. 2. BACKGROUND 2.1. Quorum Sensing in Bacteria Bacteria that participate in QS continuously release signal- ing molecules, called autoinducers (AIs) [21], which they can also detect with AI receptors. Under low-density conditions, AI molecules diffuse throughout the environment and go un- detected by the bacteria. However, the level of AI increases directly with cell density and, if it exceeds a threshold, the detection mechanism in the bacteria causes the up regula- tion of the genes that produce AI molecules. This creates a positive feedback loop which greatly increases the level of AI in the environment. Once a receptor has been fully activated by a high concentration of AI, the activated receptor causes the up or down regulation of other genes in the bacteria. If the level of AI is relatively uniform throughout the environ- ment, all of the bacteria that respond to the high level of AI Figure 1: Population (bottom), sub-population will begin transcription of the same genes at approximately (middle) and composition of a digital organism: the same time, thereby changing the population's behavior genome (top right), virtual CPU (top left) with once a quorum has been reached. heads pointing to locations within the genome. In addition to numerous wet lab studies of QS and biofilm formation [6{8,18,20,27], several researchers have constructed Avida organisms are self replicating, that is, their genomes mathematical models that describe gene expression in QS must contain instructions to create offspring. An offspring bacteria [5,25,26]. These works differ from ours in that they is placed in a randomly selected cell, terminating any previ- use P systems to model known gene expression mechanisms. ous inhabitant.