Team:RHIT/Modeling.html
From 2013.igem.org
- Mathematical Model
- Derivation
- Qualitative Analysis
- Survival Predictions
- Mathematical Model
- Survival Predictions
![](http://i.imgur.com/GCSBkyF.png)
We created a mathematical model to verify that obligate mutualism was theoretically possible. Since the S. cerevisiae and E. coli will depend on each other to survive, if one population dies the other will follow. From the model, we developed predictions about the initial concentrations of S. cerevisiae and E. coli needed for co-dependence, and devised more informed laboratory experiments. See the technical section for the model derivation or either section for the model's predictions.
![](http://i.imgur.com/QgXpAWW.png)
• E represents the concentration of E. coli • Y represents the concentration Yeast • L represents the concentration of Lactate • t represents time • Parameters $k_1$ through $k_6$ describe the population dynamics
To simplify the system further we assume that the concentration of lactate equilibrtates rapidly compared to the bacteria and yeast concentrations so that $$\frac{dL}{dt} = 0 \\ k_5 Y - k_6 E L = 0 \\ L = \frac{k_5 Y}{k_6 E}$$ Substituting this into the original model gives the following system of two ODE $$\frac{dE}{dt} = -k_1 E + k_3 \frac{k_5}{k_6} Y \\ \frac{Y}{dt} = -k_2 Y + k_4 E Y \\$$ Before we can analyze the model, we must make it dimensionless. Let $k_1 = \alpha$, $k_3 \frac{k_5}{k_6} = \gamma$, $k_2 = \sigma$, and $k_4 = \phi$ so that the model becomes $$\frac{dE}{dt} = -\alpha E + \gamma Y \\ \frac{dY}{dt} = -\sigma Y + \phi E Y $$ Defining the dimensionless parameters to be $$ e = \frac{E}{\frac{\sigma}{\phi}} \\ y = \frac{Y}{\frac{\alpha \sigma}{\phi \gamma}} \\ s = \frac{t}{\frac{1}{\alpha}} $$ simplifies the model to \begin{align} \frac{de}{ds} = y - e \\ \frac{dy}{ds} = \theta y (e - 1) \end{align} where $\theta = \frac{\sigma}{\alpha} = \frac{k_2}{k_1}$ which is the ratio of the decay rate of S. cerevisiae to E. coli. We can now qualitivately analyze the model and make predictions for behavior.
![](http://i.imgur.com/gOV4b4r.png)
![](http://i.imgur.com/ODHvsNY.png)
Our model has three unknown parameters: the initial concentration of yeast, the initial concentration of E. coli, and the dimensionless parameter theta. In wet lab, we can control the initial concentrations used, but we cannot control theta. In the figures below, we graphed the phase plane of our model and the solution to the system of differential equations. Drag and drop the blue dot to adjust the initial concentrations used and the slider to adjust theta to determine if the populations will survive or not!
You will discover that there are certain thresholds that our initial concentrations of Yeast and E. Coli must be greater than so that the population survives.
![](http://i.imgur.com/GCSBkyF.png)
We created a mathematical model to verify that obligate mutualism was theoretically possible. Since the S. cerevisiae and E. coli will depend on each other to survive, if one population dies the other will follow. From the model, we developed predictions about the initial concentrations of S. cerevisiae and E. coli needed for co-dependence, and devised more informed laboratory experiments. See the technical section for the model derivation or either section for the model's predictions.
![](http://i.imgur.com/ODHvsNY.png)
In wet lab, we are planning on co-culturing our strains of Yeast and E. Coli. We envision the species coming together to form a multicellular unit. We want the mathematical model to help inform us of the initial concentrations of Yeast and E. Coli that we should use in laboratory so that the populations will surivive.
The blue dot on the first graph below represents the initial concentrations of the Yeast and E. Coli used in our experiment. Drag the blue dot around to change our initial concentrations of Yeast and E. Coli to determine if the populations in our experiment will survive! You can see how the Yeast and E. Coli concentrations vary in time in the lower graph.
You can also adjust the value of the theta, which is another parameter in the model. It represents the relative death rates of Yeast to E. Coli. We are not sure what the actual value of it is, but you can adjust it to see how it influences the population's dynamics.
You will discover that there are certain thresholds that our initial concentrations of Yeast and E. Coli must be greater than so that the population survives.