Now that we had designed our circuit, it was time to see how our components worked together. To do this, we simulated the behavior of our system in MATLAB‘s SimBiology compartmentally and mechanistically through differential equations. By compartmental, we mean that each reaction or event takes place in a defined location in respect to our engineered cell. In our model figure here, the intracellular space is orange, while the extracellular space is blue. By mechanistic, we mean that each step of a process is represented through interactions of species in different combinations of reactions, which are depicted as lines, as you can see from the legend on the left. Let’s take a closer look at the orange intracellular compartment.

Within our engineered cell, you can see the full circuit that, here in SimBiology representation. To put together the components, we thus constructed a system of differential equations that could model the interrelationships between species. A typical differential equation for a species looks like the one in blue text above: here, we can relate the rate of change of a species by adding its basal transcription, beta, subtracting its degradation, delta, and if the species participates in a binding event, a Hill cooperativity term. In the end, by providing literature values and relevant initial concentrations across timesteps, we can start to probe trends within the reactions we care about. If you look at the bottom of our AND gate, you can see this sort of antibody shaped representation of a translated ScFv output.

We can see the ScFv being exported out of our engineered cell into the extracellular space. This means we can assess its relationship to systemic serum circulating in the body. We were able to construct a cytokine interaction map in our model based upon plasma cytokine levels reported by Yiu et al. in patients undergoing inflammatory cytokine storms. We then could take our output ScFv and have it target each specific player in the cytokine storm, and from there, analyze its effects on the cytokine storm as a whole--using this information to tailor the future design of our system.

Our modelling results can be seen in the graph above. We found two distinct features in our system's cytokine modulation: differences in the peak IP-10 observed and in the steady state value of IP-10.

First, we looked for the lowest peak IP-10 values, since this would indicate which therapeutic target would diminish the cytokine storm the most during the peak of the cytokine storm. As seen in the figure above, the lowest peaks corresponded to IFN-γ and IL-1, respectively; thus we found IFN-γ and IL-1 to be highly effective therapeutic targets for our scFv.

Another state we looked into was the steady-state levels of IP-10. These are significant since our modulatory system should bring the resting cytokine levels to a minimum. As seen in the figure, IL-1 and IFN-γ’s steady state had the lowest average values. Thus, we once again identified IFN-γ and IL-1 as strong candidates for our scFv target since over the long term, they would most effectively reduce the cytokine storm.

We found IFN-γ to most directly inhibit the cytokine storm. However, IFN-γ is also a master regulator of the immune response, and targeting this could lead to a significant drop in patient immune strength. (Kaps et. al.). Although IL-1 is also a key player in the immune response, other studies have found clinical efficacy in IL-1 inhibition (Cavalli et al., 2020). Thus, since IL-1 had the lowest steady-state IP-10 level, the second lowest peak cytokine level, and previous literature indicating its efficacy as a cytokine storm target, we found it to be the most effective target for our scFv in modulating the cytokine storm.

We understand that it is important to test the feasibility of our system in vitro, as well as be able to explain our computational results biologically. Further we wish to play around with multiple parts and parameters to produce input output relations nearer to actual biology. We see our project guiding the study of COVID-19 cytokine storm pathology by focusing on specific cytokine(s) found to be have significant effect if modulated from our model. We also hope that parallels can be drawn to other inflammatory diseases from our study to help in identifying relevant cytokines and potentially using the results therapeutically.