Exploring the impact of parameter changes

Overview

This app allows you to explore the effect of specific model parameters on some outcomes of interest for the simple bacteria model. Read about the model in the “Model” tab. Then do the tasks described in the “What to do” tab.

The Model

Model Overview

The model used here is the one introduced in the ‘Simple Bacteria Model’ app. If you haven’t done so, check out and explore that app first. Here we are only considering the continuous time, ordinary differential equation implementation of the model.

What’s different here compared to the ‘Simple Bacteria Model’ app is that instead of running the simulation once for a given choice of parameter values and looking at the resulting time-series, this app lets you explore the impact of each model parameter on some outcomes. Specifically, we consider the maximum number of bacteria and immune response, and their values at the end of the simulation, when the system has settled down to a steady state. To ensure a steady state, you need to run the simulation for a long enough time. A steady state corresponds biologically to a chronic infection condition.

In the app, those 4 outcomes of interest are labeled Bpeak, Ipeak, Bsteady and Isteady. You can choose one of the model parameters to be varied between some minimum and maximum value. The other parameter values remain fixed. For each parameter value, the model is run and the 4 outcomes of interest computed. The resulting plot is one showing how those 4 outcomes of interest vary with the parameter you investigated.

Model Diagram and equations

As mentioned, this is the same model as used in the ‘Simple Bacteria Model’ app. Details can be found there. For ease of reference, the flow diagram and the equations of the continuous model are shown again here:

Flow diagram for simple bacteria infection model..

Flow diagram for simple bacteria infection model..

\[\dot B = gB(1-\frac{B}{B_{max}})-d_B B - k BI\] \[\dot I = r B I - d_I I\]

What to do

The tasks below are described in a way that assumes everything is in units of days (rate parameters, therefore, have units of inverse days).

Task 1:

Task 2:

Task 3:

With enough practice, it is often possible to intuit specific results based on the underlying equations - but that requires a good bit of modeling practice. As models get more complicated, even experienced modelers can often not intuit what model behavior one should expect as specific parameters vary. Often the only way to find out is by actually running the simulations.

Task 4:

The model we have here is so simple that we can in fact figure out two of the outcomes shown in the plot without having to run simulations but instead by doing some math. Specifically, we can compute the steady states for the bacteria and immune response variables. If you have done the basic virus tasks, you have encountered the steady state idea already. We repeat it here.

Once the system has settled down, there are no more changes in the numbers for each compartment. Mathematically, that means that the left side of the differential equations becomes 0, and they turn into the following algebraic equations: \(0 = gB(1-\frac{B}{B_{max}})-d_B B - k BI\) and \(0 = r B I - d_I I\). One can solve those equations for each of the compartments to get a mathematical expression of what B and I are at steady state. Try to do this. You should find that \(B_{steady} = d_I / r\) and a similar equation for \(I_{steady}\).

Task 5:

We cannot compute similar mathematical expressions for Bpeak and Ipeak. In general, as soon as our model reaches a certain level of complexity (maybe around 5 equations and more), getting analytic/mathematical equations for most outcomes of interest is not possible and the numerical approach of running the simulations and looking at the results is the only option we have.

Task 6:

Task 7:

Task 8:

Note that here we focus on a single parameter at a time. With some coding effort, we could change the underlying simulation to loop over say 2 parameters and produce outcomes for sets of parameter values, e.g. Bpeak as a function of g and r. The results could be plotted as a 2-dimensional heatmap. While this could be extended to more than 2 parameters, it will become hard to visualize and long to run. If there are many parameters that could change, a different approach is useful, which you’ll learn about in the Uncertainty and Sensitivity App.

Further Information

References

Antia, R, J C Koella, and V Perrot. 1996. “Models of the Within-Host Dynamics of Persistent Mycobacterial Infections.” Proceedings. Biological Sciences 263 (March): 257–63. https://doi.org/10.1098/rspb.1996.0040.
Kochin, Beth F, Andrew J Yates, Jacobus C de Roode, and Rustom Antia. 2010. “On the Control of Acute Rodent Malaria Infections by Innate Immunity.” PloS One 5 (May): e10444. https://doi.org/10.1371/journal.pone.0010444.