Emergence Using Reinforcing Feedback: Quorum Sensing Bacteria Quick Overview

Emergence Using Reinforcing Feedback: Quorum Sensing Bacteria

Quick overview:

Emergent Behavior in Quorums Sensing model introduces the concept of emergence and reinforcing feedback by using as an example: how bacteria talk and work together to overwhelm something (or someone) they have infected. You’ll be able to make it difﬁcult for these bacteria to work collectively by blocking how they communicate individually, hopefully before what they have infected gets too sick.

What is emergence?

“Emergence may seem mysterious, but it is actually something that we experience every day. For example, a single water molecule of two hydrogen atoms and an oxygen atom does not feel wet (assuming you could feel a single molecule). But a few billion water molecules in a cup feel wet. That is because wetness is a collective property of the slippery interactions between water molecules in a particular temperature range… Similarly, what we call a symphony is a pattern of sound that emerges out of the playing of individual instruments, and what we call a kidney is a pattern of cells working together to provide a higher level function that none of the cells could do on its own.”

Eric Beinhocker- Origin of Wealth1

Take a puzzle down from a dusty shelf and shake the contents onto a table. Now take a piece of the puzzle at random and inspect it. Note the color, shape, and presence of patterns on the piece. Now take another, and do the same. Immediately you’ll be able to note certain differences that exist between two pieces and after awhile you might notice certain trends in patterns in these differences. Now consider doing this process until you’ve looked at every single piece in the puzzle. Having considered each piece, would you have a clear understanding as to what the puzzle would look like if it were completed ( without looking at the box)? Perhaps, but certainly not as clearly as you would if you were to put all the pieces on the table, assemble them together, and see what, as a whole, they revealed.

Like the picture of the completed puzzle, there are certain things that only become apparent or real to us after they have been organized in a particular way. There appear to be many things that make a qualitative change or shift once a certain quantitative

1 Beinhocker, E. D. (2007). The origin of wealth: Evolution, complexity, and the radical remaking of economics. Century.

! boundary is crossed. Traffic jams2, flocking birds3, and even our own consciousness4 are examples of emergent properties that we experience in our day to day life. However, our understanding of exactly how these phenomena occur, and how to understand the significance of emergent properties is a challenging part within the study of complex systems5.

What is feedback?

“All dynamics arise from the interaction of just two types of feedback loops, positive (or self-reinforcing) and negative (self-correcting) loops. Positive loops tend to reinforce or amplify whatever is happening in the system: The more nuclear weapons NATO deployed during the Cold War, the more the Soviet Union built, leading NATO to build still more....Negative loops counteract and oppose change... The larger the market share of dominant firms, the more likely is government antitrust action to limit their monopoly power. These loops all all describe processes that tend to be self-limiting, processes that seek balance and equilibrium.”

John D. Sterman6

Imagine two mirrors facing each other in a well lit room, and place yourself between them. It will seem that you will be represented continually and each reflection of yourself is getting smaller, stretching out into what seems like forever. This is a kind of feedback called optical feedback. A simplification of what is happening is that there is a signal that is being sent out or in this case, reflected off the surface of one mirror, toward the surface of another. The signal is received, represented as an artifact ( the reflection you see ) and rebroadcast ( reflected ) out again from the perspective of mirror that received the signal. The signal is fed back to the original mirror, and the process begins again. This process happens again and again, deteriorating the signal, certainly, but also it creates a great

2 Resnick, M. (1997). Turtles, termites, and trafﬁc jams: Explorations in massively parallel microworlds. Mit Press.

3 Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., & Viale, M. (2010). Scale-free correlations in starling flocks. Proceedings of the National Academy of Sciences, 107(26), 11865-11870.

4 Baas, N. A., & Emmeche, C. (1997, February). On emergence and explanation. Santa Fe, NM: Santa Fe Institute.

5 Anderson, P. W. (1972). More is different. Science, 177(4047), 393-396.

6 Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world (Vol. 19). New York: Irwin/McGraw-Hill.

! many versions of the signal, an exciting result from a very simple feedback experiment. Feedback can be seen as the process by which an output is ‘fed’ ‘back’ into the initial input that created it. The pathway of the feedback is referred to as a loop. Feedback loops can be negative ( self-correcting ) or positive ( self-reinforcing ). Complex systems have a tendency to be driven by dynamics composed of feedback loops, making the understanding of feedback helpful in understanding the behavior of complex systems. In the instance of quorum-sensing bacteria a feedback loop drives the production of the substance ( autoinducers ) that bacteria use to communicate. When bacteria detect autoinducers, they are stimulated to create more autoinducers, as more autoinducers are made, the higher the chance there will be to make even more, creating a higher chance that even more will be made!

What is quorum sensing?

The word quorum, having its etymological roots in Latin ( the genitive plural of qui meaning; of whom ) means in the larger sense; a particular group’s set minimum amount of individuals needed to make a decision. In regards to this model, our quorum is a group of bacteria. This raises the question; how do bacteria know there are enough individuals in their group (a quorum) to make a decision? And, what is the decision that they are making?

In the late 60’s microbiologists observing bioluminescent bacteria7 determined that individual bacteria were somehow working collectively in a manner that a great many lit up all at the same time without the presence of something controlling them to do so. The concept of a decentralized process guiding the group activity was expanded upon in the 70’s by another group of microbiologists8 showing that the bacteria were in fact inducing bioluminescence by means of an individually synthesized molecule called an autoinducer. Bacteria were sending out these autoinducers into their environment. Other bacteria of the same species were able to detect the presence of these molecules. In a sense, bacteria were communicating to each other by producing these little molecules. By being able to detect the frequency and number of autoinducers bacteria were able to answer the question; how many other bacteria are around me right now?

The purpose of answering the question of how many, has to do with the reaction a bacteria has when a certain number ( threshold ) of these autoinducer molecules are

7 Kempner, E.S. and Hanson, F.E. (1968) Aspects of light production by Photobacterium ﬁscheri. J. Bacteriol. 95, 975-979.

8 Nealson, K.H., Platt, T. and Hastings, J.W. (1970) Cellular control of the synthesis and activity of the bacterial luminescence system. J. Bacteriol. 104, 313-322.

! encountered. When a particular threshold of ‘how many are there?’ is breached, bacteria begin transcribing genes that synthesize chemicals that, depending on the bacteria transcribing them, have a wide range of outcomes,

“Quorum sensing is the regulation of gene expression in response to ﬂuctuations in cell- population density. Quorum sensing bacteria produce and release chemical signal molecules called autoinducers that increase in concentrations a function of cell density. The detection of a minimal threshold stimulatory concentration of an autoinducer leads to an alteration in gene expression....These processes include symbiosis, virulence, competence, conjugation, antibiotic production, motility, sporulation, and bioﬁlm formation.” 9

Consider this strange situation as an analogy to quorum sensing:

Imagine yourself in a perfectly dark room. You stumble around the room looking for a light switch, but find there are none. Alas, you take inventory of your situation. You hold in your hand an empty flashlight that seems to require an usual amount of batteries to turn on. You also have a bunch batteries filling all of your pockets. You try and fit each of the batteries you have in your flashlight, and find that for some reason they just don’t fit inside. Wandering around aimlessly you step on something strange and reach down to find that you’ve discovered a battery on the floor. You try this new battery and low and behold; it fits! You decide it would be a good idea to start getting rid of your other batteries, as they are of no obvious use to you, and you’re finding ones that seem to work a lot better anyway. As you walk around, each new battery you find encourages you to drop more and more of your ‘unfit’ batteries that you can’t use. Finally it seems, that every where you turn is a new battery that works with your light and you are dropping your bad batteries left and right. Finally; you find the last battery you need to turn your light on, and just before you do so you notice all around this dark room other flash lights are lighting up, you turn yours on, and before you know it, the whole room is awash in light of a great many individual flashlights.

Here you (and the many other people that end up lighting up the room) are the bacteria, the batteries are the autoinducers, and the light from the flashlight is the product of your special gene transcription. What is interesting about quorum-sensing and the dark room full of people walking around aimlessly with cumbersome flashlights, is that the collective action of bacteria, and the flashlight-holders isn’t regulated by any central figure; there is no master bacteria and there is no king of the flashlight holders. Nothing is telling each group to act all at once, it is only by means of using a shared environment (the room or a petri dish), having a similar threshold (amount of batteries to turn on the flashlight or amount of autoinducers to transcribe genes) and having a shared method of communication (batteries or autoinducers) that allows each group of individuals to act collectively. It is this collective response that we describe as being an emergent property of the system.

9 Miller, M. B., Bassler, B. L. (2001) Quorum Sensing in Bacteria. Annu. Rev. Microbiol. 55, 165-199

! Now, what if we were to encounter a bacteria that when they worked together they produced a bunch of toxins in order to overtake a much, much larger organism. How could that be stopped? For a very long time antibiotics have been used to kill as many bacteria as possible in order to stop this from happening. A new approach has been investigated10 that hinders the collective activity of certain bacteria that use quorum sensing as a way to organize. This new process blocks bacteria from sensing the quorum, that is, knowing that there are enough bacteria like themselves around for it to be a good idea to start producing toxins (fewer might mean there might not be enough of them to successfully win the ﬁght against an immune system). In this model an agent-set called antiquorum are used to represent chemicals that adopt aspects of this novel method of disrupting the sensing of the quorum.

How the model works:

The world is populated with a set number of bacteria that randomly wander around both reproducing and creating autoinducers. Each bacteria has a random chance of dying, and creating new bacteria. The total bacteria population is controlled by a ‘slider’ that sets a carrying capacity in the model. Autoinducers bounce around randomly in the model as well, and have a random chance of dying. When an autoinducer and a bacteria meet, the autoinducer nests inside the bacteria. Once a bacteria has a certain number of autoinducers inside of it, the bacteria changes color (yellow to magenta) and begins to change the color of the patchers surrounding it, simulating the diffusion of a generic toxin. This toxin dissipates at a set rate speciﬁed by the model. The concentration of this toxin is shown with color and brightness illustrated in the right-hand margin. A plot shows the change in population of bacteria and autoinducers over time, as well as the amount of patches that have some toxin on them.

In the plot there are two ﬂat lines, running horizontal. The lower one indicates where the carrying capacity of the total population has been set, the higher one shows how much toxin there can be in the model before the organism being infected is overwhelmed and dies. When the organism dies, the model stops. A histogram (health bar) corresponds with the health line on the plot.

How can I keep the organism alive?

There are several ways to keep the bacteria from working collectively. Controlling their use of feedback, keeping their population small, and releasing antiquorum into the model all help to mitigate the release to toxins. Understanding the reasons why these methods work is essential in understanding this instance of self-organization. If there are simple

10 Adonizio, Allison L., et al. Anti-quorum sensing activity of medicinal plants in southern Florida. Journal of Ethnopharmacology 105.3 (2006): 427-435.

! rules that are being followed in order for agents in a system to self-organize, it is there that there is a great point of leverage for changing the system.

The agents are programmed to release toxin once a certain amount of autoinducers are encountered, which they are producing at a fairly steady rate. With feedback on this rate is increased when they encounter other autoinducers. Without the utility of this feedback loop, self-organization cannot take place.

The agents exist in a world of ﬁnite boundaries. Space, lifespan, and resources are limited. The latter of the three isn’t explicitly obvious and is held in the value of the slider carrying-capacity. This slider functions by holding a value that is used in a procedure that limits the number of bacteria-agents that may populate the world window at any given time. The procedure takes the total number of bacteria-agents at each time-step and subtracts the value held by the carrying capacity slider. This new value is then divided by the total number of bacteria-agents at that same time-step. If the result is greater than a randomly generated number that is between 0 and almost 1, then bacteria are subject to being possibly removed from the model. The purpose of a carrying capacity is to imply that the bacteria-agents are sharing some environmentally based resource that is vital to the maintenance of the population and reproduction. The environment holds enough of this resource to ‘carry‘ a certain number of bacteria. Once this threshold or ‘capacity‘ is reached, bacteria-agents begin to die off, as they do not have a sufﬁcient amount of this resource to maintain life. If the carrying-capacity slider is set to a very low value, the number of bacteria-agents never achieve a population dense enough for them to self-organize.

For the agents to self-organize communication is key. The sending a signal may be difﬁcult to disrupt, as it is created by each individual agent and implicit in how they function on a very basic level. However, once it has been sent, perhaps the receiving of the signal may be disrupted. By clicking on the world window a set amount of antiquorum agents are released into the environment using the placement of the mouse as a point of origin. These antiquorum agents make it more difﬁcult for the bacteria to sense the presence of autoinducers. The more antiquorum, the less apt bacteria-agents to sense the quorum.

Buttons/Sliders/Switches/Plots:

Buttons:

Setup: Clears all agents from the world window and clears all plots.

Go: Toggles between iterating the model, and stopping the model.

! Sliders:

Initial-Population: Sets the initial starting population of bacteria.

Carrying-Capacity: Sets the maximum number of bacteria that are allowed to be present in the model at any given time step.

Switches:

Reinforcing-Feedback: Toggles between the bacteria responding to the presence of autoinducers as a source of positive feedback.

Plots:

Populations-Plot: Shows the amount of each agent-set as well as the value of the carrying capacity (horizontal black line on the bottom of the plot) and a line that shows the total amount of toxin that can be in the world window before the model stops.

Health-Histogram: Monitors the health of the organism by changing the height of the histogram based on the amount of toxin in the world window. When the bar disappears, the organism is considered dead and the model stops.

Model Key:

This agent-set represents generic bacteria. When surrounded by a yellow boarder they are not releasing toxin into their environment. A magenta boarder indicates the release of toxin by that individual bacteria-agent.

This agent-set represents autoinducers produced by bacteria-agents.

These patches colored with a gradient of pink indicate the presences of a generic toxin produced by bacteria-agents. Brighter patches have more toxin on them.

This agent-set represents antiquorum created by clicking on the world window of the model.

Exercises:

1) Play with the set-population slider. See what effect different initial populations have on the emergent behavior of the bacteria. Does it speed up the process? Slow it down? If so, why?

2) Play with the carrying-capacity slider. Does the maximum amount of bacteria change the ability for collective activity to occur? Why do you think this is?

3) Investigate the logic that allows for antiquorum to disrupt quorum sensing. Try changing this logic so that it encourages self-organization. Why might this be useful in real world applications? to change-behavior

ask bacterias

[ let sense-auto count autoinducers in-radius 1 ; creates a local variable that holds the value of how many autoinducers are in a small radius let sense-anti count antiquorums in-radius 1 ; creates a local variable that holds the value of how many antiquorum are in a small radius

; a conditional statement that says if the value of the local variable that ; counts the amount of autoinducers is more than some random value between 0 and almost 25 ; in addition to the amount of antiquorums around the bacteria ; then the following actions will occur : ; then the amount of toxin that is on the patch that the bacteria is on ; will be added to by 2 ; the bacteria will change color ( to magenta ) ; if none of the conditions are met

ifelse sense-auto > (random-ﬂoat 25 + sense-anti) [ set toxin toxin + 2 ; then the amount of toxin that is on the patch that the bacteria is on will be added to by 2 set color magenta ] ; the bacteria will change color ( to magenta )

! [ set color yellow ] ; if none of the conditions are met the bacteria turns yellow ]

4) What are some things in your day to day life that could be described as self-organizing or a result of self-organization? What simple rules were followed for this self-organization to occur?

6) Turn reinforcing-feedback switch to the ON position and run the model. Now, turn the reinforcing-feedback switch to the OFF position and run the model again. What differences do you see? What role does reinforcing-feedback play in quorum-sensing?

7) What processes around in the world can you describe as having reinforcing feedback? Investigate self-correcting feedback.

8) Go into the code of the selforgqsbacteria.nlogo model. Find the procedure labeled feedback:

to feedback

if reinforcing-feedback = true [ ask bacterias [ let see count autoinducers in-radius .5 if see > 3 [ hatch-autoinducers 1 [ set color blue set size 1 jump 3 ] ] ] ] end

8a.) What changes could you make to this code that would hasten global behavior in the model?

8b.) What changes would lessen it?

References:

Adonizio, Allison L., et al. Anti-quorum sensing activity of medicinal plants in southern Florida. Journal of Ethnopharmacology 105.3 (2006): 427-435.

Anderson, P. W. (1972). More is different. Science, 177(4047), 393-396.

! Beinhocker, E. D. (2007). The origin of wealth: Evolution, complexity, and the radical remaking of economics. Century.

Resnick, M. (1997). Turtles, termites, and trafﬁc jams: Explorations in massively parallel microworlds. MIT Press.

Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., & Viale, M. (2010). Scale-free correlations in starling flocks. Proceedings of the National Academy of Sciences, 107(26), 11865-11870.

Baas, N. A., & Emmeche, C. (1997, February). On emergence and explanation. Santa Fe, NM: Santa Fe Institute.

Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world (Vol. 19). New York: Irwin/McGraw-Hill.

Kempner, E.S. and Hanson, F.E. (1968) Aspects of light production by Photobacterium ﬁscheri. J. Bacteriol. 95, 975-979.

Nealson, K.H., Platt, T. and Hastings, J.W. (1970) Cellular control of the synthesis and activity of the bacterial luminescence system. J. Bacteriol. 104, 313-322.

Miller, M. B., Bassler, B. L. (2001) Quorum Sensing in Bacteria. Annu. Rev. Microbiol. 55, 165-199