MODELLING

The evolution of our bacteria-phage dynamic model helped us gain a better understanding of the interaction between a bacteria population and a phage population and its impact on the viability of our design. After defining a variety of parameters and making several assumptions, we showed that it is possible for our system of bacteria and phages to be self-sustainable. Comparing our model with our experimental results, we developed a second model where we accounted for additional factors such as a possible mutation in the bacteria’s DNA that results in resistance against phage infection. Furthermore, we modelled the copper-binding efficiency of CUP I (our copper-binding protein) to estimate the optimal ratio of enzyme and copper concentrations that would result in the most efficient binding in the implementation of our system.

DISCRETE TIME MODEL

Purpose:Given an initial Multiplicity of Infection (MOI) and infection onset point (during a bacteria lifecycle), determine how the populations of bacteria and phages change over discrete time intervals.

Assumptions:

• There is no delay in infection
• All bacteria are susceptible to infection, and all infections are successful.
• All bacteria death is caused by infection (i.e. there is no natural death)

Definitions of Parameters and Variables:

Multiplicity of Infection (MOI): The MOI represents the initial ratio between the number of phages and number of bacteria at the time of inoculation. It is a decisive factor in calculating the probability that a bacteria will be infected by at least one phage particle. The equation that relates the MOI to this probability is:

where P is the probability, and m represents the multiplicity of infection. (reference Wikipedia page) Although it is possible that a bacterium is infected with more than one phage particles, a research study conducted by Ellis and Delbruck suggests that bacteria infected with multiple phage particles had similar burst sizes than bacteria that were infected with only one phage. (insert reference)

Burst Size: the number of phages produced per infected bacteria.

Lysis Time: the time it takes for a phage to infect and lyse a bacteria host.

Both the burst size and the lysis time depend on the point during a bacteria lifecycle when inoculation begins. One research study conducted by Zachary Storms and Tobin Brown shows how the burst size—the squares, and the lysis time—the circles, vary with when the infection starts. Storms and Brown suggest that the burst size is the highest and the lysis time is the shortest if the infection starts when the bacteria cell enters the division stage, because at that point the cell has the richest intracellular resources.

Bacteria doubling time: the time it takes a bacteria population to double in size.

Equations:

Results and Interpretation:

The following graphs are constructed with an initial bacteria population of 1,000 and an initial phage population calculated according to the MOI used. However, the actual numbers of bacteria and phages do not influence the trends observed in the graph, as it is the ratio between these numbers (the MOI), not the actual numbers, that matters.

Discussion:

Although this model clearly shows the influence of the initial MOI on the bacteria/phage dynamics, it suggests that even with a very small MOI, the bacteria and phage populations always become extinct. This is not realistic because in many real-world instances, bacteria and phages co-exist. We realized that the problem with this model is that it treats time as a discrete variable. In discrete time, phages reproduce much faster than bacteria so that the bacteria population is wiped out regardless of the initial values. Hence, we proceeded with a different approach, treating time as continuous.

CONTINUOUS TIME MODEL

Purpose:Given initial populations of bacteria and phages (and hence an MOI), understand how the populations interact and change over continuous time.

Assumptions:

• There is no delay in infection. (All infections are instantaneous.)
• All infections are successful (infected bacteria subsequently die of infection)
• New bacteria are introduced from the external environment at a constant rate. This is a reasonable assumption because bacteria concentration in the water that enters our multi-cistern system should be consistent.

Definitions of Parameters and Variables:

Intrinsic Growth Rate of Bacteria (r): aka. Intrinsic rate of natural increase or the Malthusian parameter. This rate describes the maximum theoretical rate of increase of a population per individual. Using this parameter to account for bacteria population growth instead of the traditional doubling time allows us to investigate the interactions between phages and bacteria in continuous time.

Intrinsic Growth Rate of Bacteria (r): aka. Intrinsic rate of natural increase or the Malthusian parameter. This rate describes the maximum theoretical rate of increase of a population per individual. Using this parameter to account for bacteria population growth instead of the traditional doubling time allows us to investigate the interactions between phages and bacteria in continuous time.

Influx of Bacteria: An increase in bacteria population caused by the entry of external bacteria into the system.

Natural Death Rate of Bacteria: Bacteria death caused by factors other than infection.

Natural Death Rate of Phages: Phages that become unable to infect more bacteria.

Contact factor (c): This parameter describes how efficiently a given phage population can infect a given bacterial population. Although the computation of c is very complex and involves many factors as described above (such as the time during a bacteria lifecycle when inoculation begins), the use of empirical evidence of the value of c from past researches simplifies this process without compromising the accuracy of our model.

Equations

Results and Interpretations

Figure 5: Bacteria-phage dynamic in continuous time. The initial bacteria population reproduces and gradually increases. Once the bacteria population reaches the threshold (the peak observed) for mass action, the high abundance of susceptible bacteria makes the probability of phage infection increase greatly, at which time (about 1.25 hours after inoculation as shown in figure 5b) the phage population increase drastically and overwhelms the bacterial population. However, the bacteria population is never zero at any given moment due to the constant influx and the intrinsic rate of natural increase. Any presence of bacteria, though, results in the bacteria being immediately infected by the phage population, which explains the constant gradual increase observed in the phage population following its drastic increase around 1.25 hours and the appearance of the bacteria population to be zero.

Discussion

The gradual increase of phage populations proves that it is possible for our system to be sustainable. However, the extinction of the bacterial population (or, at least, the appearance of extinction, as the bacterial population is not actually zero but appears to be zero because at any moment, the presence of bacteria induces immediate infection and lysis) deviates from reality. The model suggests that the introduction of a very small amount of phage into a population of bacteria will cause the bacteria population to reach an equilibrium centered around zero. We do not believe this to be the case in real ecosystems. Hence, we carried out experiments where we collected real data on the interaction between bacteria and phage populations.

EXPERIMENTAL RESULTS

In one of our experiments, we added in the same amount to phages to different concentrations of bacteria cultures. In another of our experiments, we added in different concentrations of phages to the same concentration of bacteria. We monitored the change in the optical density of the cultures over 24 hours to investigate the influence of the initial concentrations of bacteria and phages on how the populations interact. Growth of bacteria cultures in the presence of phages. Figure 6a shows that different concentrations of phages have some influences on the growth curve of bacteria. For each group of bacteria, the highest concentration of phages results in the least amount of growth. (the lowest concentration at the end of data collection.) However, in no instances did the bacterial population perish completely. Similarly, as figure 6b shows, without the presence of phages, the bacteria population grows before reaching a carrying capacity, at which point the growth curve levels off. However, even in the presence of phages, similar growth curves can be observed. There is no sign that the presence of a phage populations causes the bacteria population to become extinct, which our previous models suggest. In fact, the culture with the same concentration of bacteria as the negative control (0.01OD) but with the presence of phages had a slightly higher concentration of bacteria at the end of data collection. Clearly, bacteria and phages can and do coexist. After obtaining these results, we proceeded to improve our model in hopes of explaining the coexistence of bacteria and phages.

SIRV MODEL

What is SIRV?

An SIRV model (Susceptible, Infected, Recovered, and Vaccinated) for the spread of a virus, in general, divides the viral host population into four classes: those susceptible to be infected, those that have already been infected, those that have recovered from an infection, and those that have grown resistance to the virus and thus not susceptible to be infected. The difference between our SIRV model and the earlier simple continuous time model is that with the SIRV model, we take into consideration the possibility that certain individuals from a strain of bacteria may, over time, become resistant to a type of phage through genetic mutation. This is in keeping with the law of natural selection and evolution, and we hoped that this model may help to explain why bacteria and phages co-exist in many instances.