Survival of One Strain

Introduction: Minimal differences determine who will survive

In this section we will show a shocking scenario of evolution: the future of each of three different rival strains (whether the strain will die or survive) is marked by minimal differences between the initial population densities of the strains. Furthermore, we will also show that we can apply this very interesting result to create a randomizer that can be used in our Rock-Paper-Scissors game, due to the fact that only one of the rival strains will survive. More specifically, we assign to each of the three rival strains either of Rock, Paper or Scissors, make them compete for survival and take the surviving strain to represent the bacteria’s choice for the RPS game.

Adjusting the Model to create a True Randomizer

The idea for creating this randomizer was born from a paper written in 1996 by Durret and Levin. In it, the authors described a system of three types of bacteria that competed for survival in dynamic that resembled a Rock-Paper-Scissors (RPS) game. However, the model proposed in this paper is not fully appropriate for our RPS randomizer, since one of the three types of bacteria cannot ultimately survive (although it can dominate the system, i.e. have the highest population density, for definite periods of time). We will discuss more on the limitations we found in this model to be adopted as a randomizer and the modifications we made to create a true randomizer.

How the Three Types of Bacteria Compete for Survival

The three types of bacteria that compete for survival use three tactics to outcompete their rivals: the production of a toxin (a bacteriocin called colicin) that is toxic to other strains, resistance to the toxin produced by other strains, and a higher birth rate than their rival strains. Namely, the three types of bacteria are: colicin-producing E. coli (R), colicin-resistant E. coli (P) and colicin-sensitive E. coli (S). The colicin-producer outcompetes the colicin-sensitive by producing the colicin. The colicin-sensitive bacteria outcompetes the colicin-resistant because its birth rate is higher than that of the colicin-resistant. The colicin-resistant outcompetes the colicin producer because its birth rate is higher than that of the colicin producer. The colicin resistant bacteria are also able to produce colicin, but at a lower energetic cost, which allows them to have a higher birth rate.

The system was described by the following general differential equations

Where

The Old Model

In the model described by Durret and Levin’s paper the equations were as follows:

These equations show that the colicin-resistant bacteria are completely immune to colicin (there is not death factor associated to colicin in the equation for du₂/dt). However, as will be explained afterwards, this results in a loss of balance that does not allow building a true randomizing system.

Now, setting the parameters as follows, the graph below was created by Durret and Levin.

Our New Model

As mentioned before, the model proposed by Durret and Levin has critical limitations as a randomizer for the RPS game. To be able to create a true randomizer, we modified the differential equations of the model taking care to give it a biological meaning. With our new differential equations, any of the three types of bacteria can ultimately survive by outcompeting the other two strains, which will die. More specifically, we limited the resistance of the colicin-resistant bacteria in the sense that it would produce a type of bacteriocin that is only toxic to itself and to the sensitive strain, and additionally the resistant strain would also be vulnerable to the colicin produced by the colicin-producer. Since which strain will be the one that survives is determined by very small differences in the initial concentrations of the three different populations of bacteria, in practice this systems becomes a randomizer because of the imprecisions in the measurements that result, for example, when using micropipettes. This randomizer describes a new competition dynamic that could not be reproduced in the previous model proposed by Durret and Levin due to the instability along the axis.

If we set the parameters as follows

and we graph this equations using a Matlab program, we get a graph which clearly shows there are stable points on each of the three axes (Figure 1, Up).

These stable points (u1,0,0), (0,u2,0) and (0,0,u3) indicate that for the equations we have set all of the three strains may ultimately survive for infinite peiriods of time. The differences between our model and the model of Durret and Levin can be seen graphically in Figure 1. These graphs were plotted using Matlab.

Note that the parameters we have set for our equations satisfy the initail conditions of the model proposed by Durret and Levin (indicated in black font)

The Biological Meaning of our Model

From a biological perspective, our model describes the existence of two strains of bacteria that produce two different types of bacteriocins. One of these strains is not completely resistant to its own bacteriocin nor to the bacteriocin produced by its rival strain. This can be justified as the consequence of insufficient/ineffective resistance protein production by the “resistant” strain. This limitation in the production of resistance protein could be thought of as a consequence of the “resistant” strain being a mutant of a colicin-sensitive strain.

Making it Obvious

From the graph of our new model (Figure 1, left) it can be deduced that there are paths that converge at stable points (u1,0,0), (0,u2,0) and (0,0,u3), and that this paths all have an approximately common origin. In this section we would like to show that the origin of these paths is practically the same, and that in that sense we have designed a true randomizer (since, as mentioned before, the imprecisions that result in the experimental measurements will make it impossible to make the initial population density of the three strains the same).

In the following set of graphs we will make it obvious that each of the three different strains of E. coli to survive in a random fashion by minimal differences on the initial concentrations of each strain.

We modeled our results using Matlab. As can be seen in the graphs below, each of the strains can survive if their initial density in only tree hundredths (a.u.) greater than the other two strains' initial concentrations.

	We this graphs it becomes clear that the imprecisions in experimental measurements (i.e. pipetting) are enough to cause the outcome of Rock, Paper or Scissors signaling molecule to be random. Consequently, we can conclude that this randomizer is not only feasible but also practical and effective (let alone interesting).

We this graphs it becomes clear that the imprecisions in experimental measurements (i.e. pipetting) are enough to cause the outcome of Rock, Paper or Scissors signaling molecule to be random. Consequently, we can conclude that this randomizer is not only feasible but also practical and effective (let alone interesting).