Difference between revisions of "Team:MIT/Poster"
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| + | <figcaption><i>FIGURE 3: Full network circuit design, responsive to COVID-19 cytokine storm biomarkers’ (IP-10 + MCP-3) nuclear transcription factors (ELK-1 + NF-κB) , using digital PERSIST “OFF” logic (CasE, Csy4, Cas6), and producing an individualized anti-inflammatory, single chain antibody fragment output (scFv).</i> | ||
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Revision as of 00:44, 5 November 2020
MIT: Mammalian Circuitry for Treatment of COVID-19 Cytokine Storms
Poster Title
You should list all authors and their affiliations here. You can also add the project's abstract.
Introduction
Introduce your project and your team's goals.
Inspiration
What inspired your team? What motivated you to work on this particular project?
Medal Requirements
Idea
How are you going to solve the problem? Where did the idea come from?
Engineering
Section 1
Use this section to explain whatever you would like! Suggestions: Safety, Human Practices, Measurement, etc.
Modelling
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 on the right: 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.
Section 3
Use this section to explain whatever you would like! Suggestions: Safety, Human Practices, Measurement, etc.
Results
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.
References and Acknowledgements
If not already cited in other sections of your poster, what literature sources did you reference on this poster? Who helped or advised you?